Medicare Program; Home Health Prospective Payment System Refinement and Rate Update for Calendar Year 2008, 25356-25481 [07-2167]
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Friday,
May 4, 2007
Part II
Department of
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Services
Centers for Medicare and Medicaid
Services
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42 CFR Part 484
Medicare Program; Home Health
Prospective Payment System Refinement
and Rate Update for Calendar Year 2008;
Proposed Rule
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Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
DEPARTMENT OF HEALTH AND
HUMAN SERVICES
Centers for Medicare & Medicaid
Services
42 CFR Part 484
[CMS–1541–P]
RIN 0938–AO32
Medicare Program; Home Health
Prospective Payment System
Refinement and Rate Update for
Calendar Year 2008
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AGENCY: Centers for Medicare &
Medicaid Services (CMS), HHS.
ACTION: Proposed rule.
SUMMARY: This proposed rule would set
forth an update to the 60-day national
episode rates and the national per-visit
amounts under the Medicare
prospective payment system for home
health services, effective on January 1,
2008. As part of this proposed rule, we
are also proposing to rebase and revise
the home health market basket to ensure
it continues to adequately reflect the
price changes of efficiently providing
home health services. This proposed
rule also would set forth the refinements
to the payment system. In addition, this
proposed rule would establish new
quality of care data collection
requirements.
DATES: To be assured consideration,
comments must be received at one of
the addresses provided below, no later
than 5 p.m. on July 3, 2007.
ADDRESSES: In commenting, please refer
to file code CMS–1541–P. Because of
staff and resource limitations, we cannot
accept comments by facsimile (FAX)
transmission.
You may submit comments in one of
four ways (no duplicates, please):
1. Electronically. You may submit
electronic comments on specific issues
in this regulation to https://
www.cms.hhs.gov/eRulemaking. Click
on the link ‘‘Submit electronic
comments on CMS regulations with an
open comment period.’’ (Attachments
should be in Microsoft Word,
WordPerfect, or Excel; however, we
prefer Microsoft Word.)
2. By regular mail. You may mail
written comments (one original and two
copies) to the following address ONLY:
Centers for Medicare & Medicaid
Services, Department of Health and
Human Services, Attention: CMS–1541–
P, P.O. Box 8012, Baltimore, MD 21244–
8012.
Please allow sufficient time for mailed
comments to be received before the
close of the comment period.
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3. By express or overnight mail. You
may send written comments (one
original and two copies) to the following
address ONLY: Centers for Medicare &
Medicaid Services, Department of
Health and Human Services, Attention:
CMS–1541–P, Mail Stop C4–26–05,
7500 Security Boulevard, Baltimore, MD
21244–1850.
4. By hand or courier. If you prefer,
you may deliver (by hand or courier)
your written comments (one original
and two copies) before the close of the
comment period to one of the following
addresses. If you intend to deliver your
comments to the Baltimore address,
please call telephone number (410) 786–
7195 in advance to schedule your
arrival with one of our staff members.
Room 445–G, Hubert H. Humphrey
Building, 200 Independence Avenue,
SW., Washington, DC 20201; or 7500
Security Boulevard, Baltimore, MD
21244–1850.
(Because access to the interior of the
HHH Building is not readily available to
persons without Federal Government
identification, commenters are
encouraged to leave their comments in
the CMS drop slots located in the main
lobby of the building. A stamp-in clock
is available for persons wishing to retain
a proof of filing by stamping in and
retaining an extra copy of the comments
being filed.)
Comments mailed to the addresses
indicated as appropriate for hand or
courier delivery may be delayed and
received after the comment period.
Submission of comments on
paperwork requirements. You may
submit comments on this document’s
paperwork requirements by mailing
your comments to the addresses
provided at the end of the ‘‘Collection of
Information Requirements’’ section in
this document.
For information on viewing public
comments, see the beginning of the
SUPPLEMENTARY INFORMATION section.
FOR FURTHER INFORMATION CONTACT:
Randy Throndset, (410) 786–0131.
General Issues: Sharon Ventura, (410)
786–1985.
Clinical (OASIS) Issues: Kathy Walch,
(410) 786–7970.
Quality Issues: Doug Brown, (410)
786–0028.
Market Basket Update Issues: Mollie
Knight, (410) 786–7948; and Heidi
Oumarou, (410) 786–7942.
SUPPLEMENTARY INFORMATION:
Submitting Comments: We welcome
comments from the public on all issues
set forth in this rule to assist us in fully
considering issues and developing
policies. You can assist us by
referencing the file code CMS–1541–P
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and the specific ‘‘issue identifier’’ that
precedes the section on which you
choose to comment.
Inspection of Public Comments: All
comments received before the close of
the comment period are available for
viewing by the public, including any
personally identifiable or confidential
business information that is included in
a comment. We post all comments
received before the close of the
comment period on the following Web
site as soon as possible after they have
been received: https://www.cms.hhs.gov/
eRulemaking. Click on the link
‘‘Electronic Comments on CMS
Regulations’’ on that Web site to view
public comments.
Comments received timely will also
be available for public inspection as
they are received, generally beginning
approximately 3 weeks after publication
of a document, at the headquarters of
the Centers for Medicare & Medicaid
Services, 7500 Security Boulevard,
Baltimore, Maryland 21244, Monday
through Friday of each week from 8:30
a.m. to 4 p.m. To schedule an
appointment to view public comments,
phone 1–800–743–3951.
Table of Contents
I. Background
A. Requirements of the Balanced Budget
Act of 1997 for Updating the Prospective
Payment System for Home Health
Services
B. Deficit Reduction Act of 2005
C. Updates to the HH PPS
D. System for Payment of Home Health
Services
E. Summary of Home Health Payment
Research
II. Provisions of the Proposed Regulation
A. Refinements to the Home Health
Prospective Payment System
1. Current Payment Model
2. Refinements to the Case-Mix Model
a. Analysis of Later Episodes
b. Addition of Variables
c. Addition of Therapy Thresholds
d. Determining the Case-Mix Weights
3. Description & Analysis of Case-Mix
Coding Change Under the HH PPS
a. Change in Case-Mix Group Frequencies
b. Health Characteristics Reported on the
OASIS
c. Impact of the Context of OASIS
Reporting
4. Partial Episode Payment Adjustment
(PEP Adjustment) Review
5. Low-Utilization Payment Adjustment
(LUPA) Review
6. Significant Change in Condition (SCIC)
Adjustment Review
7. Non-Routine Medical Supply (NRS)
Amounts Review
8. Outlier Payment Review
B. Rebasing and Revising the Home Health
Market Basket
1. Background
2. Rebasing and Revising the Home Health
Market Basket
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3. Price Proxies Used To Measure Cost
Category Growth
4. Rebasing Results
5. Labor-Related Share
C. National Standardized 60-Day Episode
Payment Rate
D. Proposed CY 2008 Rate Update by the
Home Health Market Basket Index (With
Examples of Standard 60-Day and LUPA
Episode Payment Calculations)
E. Hospital Wage Index
1. Background
2. Update
F. Home Health Care Quality Improvement
III. Collection of Information Requirements
IV. Response to Comments
V. Regulatory Impact Analysis
A. Overall Impact
B. Anticipated Effects
C. Accounting Statement
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I. Background
[If you choose to comment on issues
in this section, please include the
caption ‘‘BACKGROUND’’ at the
beginning of your comments.]
A. Requirements of the Balanced Budget
Act of 1997 for Updating the Prospective
Payment System for Home Health
Services
The Balanced Budget Act of 1997
(BBA) (Pub. L. 105–33) enacted on
August 5, 1997, significantly changed
the way Medicare pays for Medicare
home health services. Until the
implementation of a home health
prospective payment system (HH PPS)
on October 1, 2000, home health
agencies (HHAs) received payment
under a cost-based reimbursement
system. Section 4603 of the BBA
governed the development of the HH
PPS.
Section 4603(a) of the BBA provides
the authority for the development of a
PPS for all Medicare-covered home
health services provided under a plan of
care that were paid on a reasonable cost
basis by adding section 1895, entitled
‘‘Prospective Payment For Home Health
Services,’’ to the Social Security Act (the
Act).
Section 1895(b)(1) of the Act requires
the Secretary to establish a PPS for all
costs of home health services paid
under Medicare.
Section 1895(b)(3)(A) of the Act
requires that (1) The computation of a
standard prospective payment amount
include all costs for home health
services covered and paid for on a
reasonable cost basis and be initially
based on the most recent audited cost
report data available to the Secretary,
and (2) the prospective payment
amounts be standardized to eliminate
the effects of case-mix and wage levels
among HHAs.
Section 1895(b)(3)(B) of the Act
addresses the annual update to the
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standard prospective payment amounts
by the home health applicable increase
percentage as specified in the statute.
Section 1895(b)(4) of the Act governs
the payment computation. Sections
1895(b)(4)(A)(i) and (b)(4)(A)(ii) of the
Act require the standard prospective
payment amount to be adjusted for casemix and geographic differences in wage
levels. Section 1895(b)(4)(B) of the Act
requires the establishment of an
appropriate case-mix adjustment factor
that explains significant variation in
costs among different units of services.
Similarly, section 1895(b)(4)(C) of the
Act requires the establishment of wage
adjustment factors that reflect the
relative level of wages, and wage-related
costs applicable to home health services
furnished in a geographic area
compared to the applicable national
average level. These wage-adjustment
factors may be used by the Secretary for
the different geographic wage levels for
purposes of section 1886(d)(3)(E) of the
Act.
Section 1895(b)(5) of the Act gives the
Secretary the option to make additions
or adjustments to the payment amount
otherwise made in the case of outliers
because of unusual variations in the
type or amount of medically necessary
care. Total outlier payments in a given
fiscal year (FY) may not exceed 5
percent of total payments projected or
estimated.
In accordance with the statute, we
published a final rule (65 FR 41128) in
the Federal Register on July 3, 2000 to
implement the HH PPS legislation. This
final rule established requirements for
the new PPS for home health services as
required by section 4603 of the BBA,
and as subsequently amended by
section 5101 of the Omnibus
Consolidated and Emergency
Supplemental Appropriations Act
(OCESAA) for Fiscal Year 1999, (Pub. L.
105–277), enacted on October 21, 1998;
and by sections 302, 305, and 306 of the
Medicare, Medicaid, and SCHIP
Balanced Budget Refinement Act
(BBRA) of 1999, (Pub. L. 106–113),
enacted on November 29, 1999. The
requirements include the
implementation of a PPS for home
health services, consolidated billing
requirements, and a number of other
related changes. The HH PPS described
in that rule replaced the retrospective
reasonable-cost-based system that was
used by Medicare for the payment of
home health services under Part A and
Part B.
For a complete and full description of
the HH PPS as required by the BBA, see
the July 2000 HH PPS final rule.
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B. Deficit Reduction Act of 2005
On February 8, 2006, the Deficit
Reduction Act (DRA) of 2005 (Pub. L.
109–171) was enacted. This legislation
affected updates to HH payment rates
for CY 2006. The DRA also introduces
home health care quality data and its
effects on payments to HHAs beginning
in CY 2007.
Specifically, section 5201 of the DRA
changed the CY 2006 update from the
applicable home health market basket
percentage increase minus 0.8
percentage point to a 0 percent update.
In addition, section 5201 of the DRA
amends section 421(a) of the Medicare
Prescription Drug, Improvement, and
Modernization Act of 2003 (MMA) (Pub.
L. 108–173, enacted on December 8,
2003). The amended section 421(a) of
the MMA requires that for home health
services furnished in a rural area (as
defined in section 1886(d)(2)(D) of the
Act) on or after January 1, 2006 and
before January 1, 2007, that the
Secretary increase the payment amount
otherwise made under section 1895 of
the Act for home health services by 5
percent. The statute waives budget
neutrality for purposes of this increase
since it specifically states that the
Secretary must not reduce the standard
prospective payment amount (or
amounts) under section 1895 of the Act
applicable to home health services
furnished during a period to offset the
increase in payments resulting in the
application of this section of the statute.
The 0 percent update to the payment
rates and the rural add-on provisions of
the DRA were implemented through
Pub. L. 100–20, One Time Notification,
Transmittal 211 issued on February 10,
2006.
In addition, section 5201 of the DRA
requires HHAs to submit data for
purposes of measuring health care
quality. This requirement is applicable
for CY 2007 and each subsequent year.
If an HHA does not submit quality data,
the home health market basket
percentage increase will be reduced 2
percentage points.
C. Updates to the HH PPS
As required by section 1895(b)(3)(B)
of the Act, we have historically updated
the HH PPS rates annually in a separate
Federal Register document. In those
documents, we also incorporated the
legislative changes to the system
required by the statute after the BBA,
specifically the MMA. On November 9,
2006, we published a final rule titled
‘‘Medicare Program; Home Health
Prospective Payment System Rate
Update for Calendar Year 2007 and
Deficit Reduction Act of 2005 Changes
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to Medicare Payment for Oxygen
Equipment and Capped Rental Durable
Medical Equipment; Final Rule’’ (CMS–
1304–F) (71 FR 65884) in the Federal
Register that updated the 60-day
national episode rates and the national
per-visit amounts under the Medicare
PPS for home health services for CY
2007. In addition, this final rule ended
the one-year transition period that
consisted of a blend of 50 percent of the
new area labor marker designations’
wage index and 50 percent of the
previous area labor market designations’
wage index. We also revised the fixed
dollar loss ratio, which is used in the
calculation of outlier payments.
According to section 5201(c)(2) of the
DRA, this final rule also reduced, by 2
percentage points, the home health
market basket percentage increase to
HHAs that did not submit required
quality data, as determined by the
Secretary.
D. System for Payment of Home Health
Services
Generally, Medicare makes payment
under the HH PPS on the basis of a
national standardized 60-day episode
payment rate that is adjusted for casemix and wage index. The national
standardized 60-day episode payment
rate includes the six home health
disciplines (skilled nursing, home
health aide, physical therapy, speechlanguage pathology, occupational
therapy, and medical social services)
and medical supplies. Durable medical
equipment covered under home health
is paid for outside the HH PPS payment.
To adjust for case mix, the HH PPS uses
an 80-category case-mix classification to
assign patients to a home health
resource group (HHRG). Clinical,
functional, and service utilization are
computed from responses to selected
data elements in the OASIS assessment
instrument.
For episodes with four or fewer visits,
Medicare pays on the basis of a national
per-visit amount by discipline, referred
to as a LUPA. Medicare also adjusts the
national standardized 60-day episode
payment rate for certain intervening
events that are subject to a partial
episode payment adjustment (PEP
adjustment) or a significant change in
condition adjustment (SCIC
adjustment). For certain cases that
exceed a specific cost threshold, an
outlier adjustment may also be
available.
E. Summary of Home Health Payment
Research
The objective of a prospective
payment system that is case-mix
adjusted is to predict resource costs of
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providing care to similar types of
patients and to align payments to those
costs. As MEDPAC points out in their
December 2005 Report to Congress, if
the case-mix is not aligned
appropriately to resource costs, then the
PPS may overpay for some services and
underpay for others.
Since the July 3, 2000 final rule, we
have stated our intention to monitor the
new PPS and make refinements to the
system as needed. We believe
refinements are now needed to improve
the performance and appropriateness of
the HH PPS, which has not undergone
major refinements since its
implementation in October of 2000. The
general goal of any refinements would
be to ensure that the payment system
continues to produce appropriate
compensation for providers while
retaining opportunities to manage home
health care efficiently. Also important
in any refinement is maintaining an
appropriate degree of operational
simplicity. The analytic goals of our
refinement research included improving
the accuracy of the case-mix model,
understanding the descriptive
characteristics of the program and the
use of payment adjusters, understanding
variations in HHA margins, and the
simulation of potential changes to
payment methodology.
We contracted with Abt Associates,
Inc., of Cambridge, Massachusetts to
conduct several analyses in order to
achieve these objectives. In particular,
the Abt Associates analyses focused on
the resource needs of long stay patients;
alternatives to the current therapy
threshold; the potential for a more
extensive set of variables to improve the
accuracy of the Clinical on Top (COT)
model used to define the HHRG;
alternative ways to account for nonroutine medical supplies (NRS);
utilization and episode characteristics;
and HHA margins. In order to conduct
these analyses, Abt Associates primarily
used data files created from a 20 percent
sample of claims data collected between
2001 and 2004, Outcome and
Assessment Information Set (OASIS)
data linked to claims, and cost reports.
For measures of resource use, Abt
Associates used weighted minutes for
the case-mix refinements research. For
research on accounting for nonroutine
supplies costs, Abt Associates analyzed
supplies charges reported on claims
after adjusting them using cost-to-charge
ratios from selected cost reports. These
analyses are described in more detail in
section II.A.
In addition to these analyses, two
Technical Expert Panel (TEP) meetings
were conducted, under contract with
Abt Associates, on December 15, 2005,
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and March 14, 2006. These TEP
meetings provided an opportunity for
experts, industry representatives, and
practitioners in the field of home health
care to provide feedback on Abt’s
research examining the HH PPS and
exploration of payment policy
alternatives. Abt considered this
feedback when developing
recommendations for refinements to the
HH PPS. The refinements to the HH PPS
described in the following sections are
the culmination of substantial research
efforts focusing on several areas
identified for possible improvements.
II. Provisions of the Proposed
Regulation
[If you choose to comment on issues
in this section, include the caption
‘‘PROVISIONS OF THE PROPOSED
REGULATIONS’’ at the beginning of
your comments.]
A. Refinements to the Home Health
Prospective Payment System
The Medicare HH PPS has been in
effect since October 1, 2000. As set forth
in the final rule published July 3, 2000
in the Federal Register (65 FR 41128),
the unit of payment under the Medicare
HH PPS is a national standardized 60day episode payment rate. As set forth
in 42 CFR 484.220, we adjust the
national standardized 60-day episode
payment rate by a case-mix grouping
and a wage index value based on the
site of service for the beneficiary. Since
the July 3, 2000 final rule, we have
stated our intention to monitor the new
PPS and make refinements to the system
as needed. We believe refinements are
now required to improve the
performance and appropriateness of
payment for the HH PPS. After
implementation of the HH PPS, we
received a number of public comments
suggesting ways in which the payment
system could be improved. We took
those comments into consideration as
we proceeded to explore the HH PPS for
potential areas for refinement. This
proposed rule sets forth the first major
refinements to the HH PPS since its
implementation in October of 2000.
This proposed rule identifies seven
major areas of the HH PPS that were
identified as possible areas for
refinement. Those areas are: (1) The case
mix model; (2) changes in case mix
coding; (3) the PEP adjustment; (4) the
LUPA; (5) the SCIC adjustment; (6)
method of accounting for NRS, and (7)
the outlier adjustment. While this
proposed rule proposes to implement all
of refinements discussed in this rule
effective January 1, 2008, we recognize
that there may be operational
considerations, affecting CMS or the
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industry, which could necessitate an
implementation schedule that results in
certain refinements becoming effective
on different dates (a splitimplementation). We would like to
solicit suggestions and comments from
the public on this matter.
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1. Current Payment Model
On July 3, 2000, we published a final
rule (65 FR 41128) in the Federal
Register. In that rule, we described a
system for home health case-mix
adjustment developed under a research
contract with Abt Associates, Inc., of
Cambridge, Massachusetts. Using
selected data elements from the OASIS
and an additional data element
measuring receipt of at least 10 visits for
therapy services, the case-mix system
projects patient resource use based on
patient characteristics. These data
elements were selected because they
were shown to influence home health
resource utilization upon statistical
analysis of data from approximately
30,000 episodes. This model used data
from first episodes only and a relatively
small set of clinical, functional, and
service utilization variables. Clinical
judgment, the relative predictive value
of potential case-mix variables, their
susceptibility to gaming and
subjectivity, and administrative
implications were considered in the
final resolution of the elements retained
in the final model.
The data elements are organized into
three dimensions to capture clinical
severity factors, functional severity
factors, and services utilization factors
influencing case-mix. In the clinical and
functional dimensions, each data
element is assigned a score value
derived from multiple regression
analysis of the Abt research data. The
score value measures the impact of the
data element on total resource use.
Scores are also assigned to data
elements in the services utilization
dimension. To find a patient’s case-mix
group, the case-mix grouper software
sums the patient’s scores within each of
the three dimensions. The resulting sum
is used to assign the patient to a severity
level in each dimension. There are four
clinical severity levels, five functional
severity levels, and four services
utilization severity levels. Thus, there
are 80 possible combinations of severity
levels across the three dimensions. Each
combination defines one of the 80
HHRGs in the case-mix system. For
example, a patient with high clinical
severity, moderate functional severity,
and low services utilization severity is
placed in the same group with all other
patients whose summed scores place
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them in the same set of severity levels
for the three dimensions.
We summarized the performance of
the final PPS model for the PPS using
the R-squared statistic. An initial
episode was defined as the first home
health episode of care for a given
beneficiary in a sequence of adjacent
episodes. For the purposes of our
analysis, we defined a sequence of
adjacent episodes for a beneficiary as a
series of claims with no more than 60
days without home care between the
end of one episode, which is the 60th
day (except for episodes that have been
PEP-adjusted), and the beginning of the
next episode. At the time, based on data
from the model development sample,
this model’s R-squared statistic was
0.34. In other words, the model
explained 34 percent of the variation in
resource use.
2. Refinements to the Case-Mix Model
Extensive research has been
conducted to investigate ways to
improve the performance of the casemix model. We found that the addition
of separate regression equations to
account for later episodes and multiple
therapy thresholds (replacing the
current threshold of 10 therapy visits)
significantly improved the fit and
performance of the case-mix model.
Further, we expanded the set of
variables to include new diagnosis
groups, comorbidities, and interactions,
yielding models that performed better in
simulations. We feel that these changes
would improve the HH PPS by allowing
more accurate case-mix adjustment
without providing incentives for
providers to distort appropriate patterns
of care.
As with the original case-mix model,
the general approach to developing a
case-mix model was to use patient data
and other appropriate data to create a
regression model for resource use over
the course of a 60-day episode. Case-mix
refinement analysis focused on
investigating resource use in episodes
that occur later in treatment as well as
the initial episode; testing additional
clinical, functional, and demographic
variables; exploring the effect of
comorbidities; and testing new therapy
thresholds.
The basis for selecting these areas of
analysis will be described in sections
II.2.a., II.2.b., and II.2.c.
As with our case-mix studies that
resulted in the case-mix methodology
discussed in the July 3, 2000 HH PPS
final rule, the dependent variable in
these refinement studies is an estimate
of cost known as resource cost. To
derive the resource cost estimate, the
total minutes reported on the claim for
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each discipline’s visits are converted to
a resource cost. Resource cost results
from weighting each minute by the
national average labor market hourly
rate for the individual discipline that
provided the minutes of care. Bureau of
Labor Statistics data are used to derive
the hourly rate. The sum of the
weighted minutes is the total resource
cost estimate for the claim. This method
standardizes the resource cost for all
episodes in the analysis file.
Based on the findings of our analysis
of the case-mix adjustment under HH
PPS, which we describe in section
II.A.2, we propose that the case-mix
adjustment be refined to incorporate an
expanded set of case-mix variables to
capture the additional clinical
conditions and comorbidities; four
separate regression models that
recognize four different types of
episodes; and a graduated, threethreshold approach to accounting for
therapy utilization. We refer to the four
separate regression models in this
proposed case-adjustment system as the
four-equation model. The first
regression equation is for low-therapy
episodes (less than 14 therapy visits)
that occur as the first or second episode
in a series of adjacent episodes
(Episodes are considered to be
‘‘adjacent’’ if they are separated by no
more than a 60-day period between
claims). The second regression equation
is for high-therapy episodes (14 or more
therapy visits) occurring as the first or
second episode in a series of adjacent
episodes. The third equation is for lowtherapy episodes (under 14 therapy
visits) occurring after the second
episode in a series of adjacent episodes.
And the fourth equation is for hightherapy episodes (14 or more therapy
visits) occurring after the second
episode in a series of adjacent episodes.
As described in further detail below,
these equations incorporate a graduated,
three-threshold approach to accounting
for therapy utilization. The 153 case mix
groups created from the results of the
four-equation model are also described
below, as is the method we used to form
the groups.
a. Analysis of Later Episodes
As a starting point for our analysis,
we examined the performance of our
original model using data, derived from
the National Claims History, reflecting
the period after the HH PPS was
initiated. These data from the period
after the commencement of the HH PPS,
a large random sample of claims from
CY 2003, indicate the performance of
the case-mix model differs from the
original estimate, which reflected data
from the time of the Abt case-mix study.
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The more recent data reflect both the
inclusion of episodes beyond the first
episode as well as behavioral changes of
health care providers under the HH PPS.
The R-squared statistic estimated from
the more recent data is approximately
0.21. An appropriate comparison with
the initial R-square statistic (0.34) is the
R-squared value estimated from the
more recent data’s initial episodes,
which is 0.29. We therefore believe the
data reflect a more modest reduction in
model performance of 0.05. However,
the value of the R-squared statistic
calculated on all the data, 0.21, is an
indication that the case-mix model does
not fit non-initial episodes as well as it
fits initial episodes. Therefore, one
focus of our refinement work was to
investigate resource use in episodes that
occurred later in treatment as well as
early episodes.
Based on exploratory analysis, we
defined ‘‘early’’ episodes to include, not
only the initial episode in a sequence of
adjacent episodes, but also the next
adjacent episode, if any, that followed
the initial episode. ‘‘Later’’ episodes
were defined as all adjacent episodes
beyond the second episode. When we
analyzed the performance of the casemix model for later episodes, we
determined there were two important
differences for episodes occurring later
in the home health treatment compared
to earlier episodes: higher resource use
per episode and a different relationship
between clinical conditions and
resource use.
Using a large, random sample of
episodes, we found that the estimated
resource cost of early episodes is
approximately 7 percent lower than the
estimated resource cost of later
episodes. The current case-mix model
weights all episodes equally.
Furthermore, our exploratory
regression models indicated that the
relationships between case-mix
variables and resource use differed
between earlier and later episodes. This
suggested that a scoring system that
differed for earlier and later episodes
could potentially perform better than a
single scoring system. The system of
four separate regression equations
allows the scores to differ according to
whether the episode is early or later. We
recognize that this approach introduces
more complexity into the case-mix
adjustment system. However, less
complex approaches that did not
depend on separate equations did not
perform as well in terms of predictive
accuracy; for example, we explored
using one equation in which we
modeled additional lump-sum costs due
to the timing of an episode in a
sequence of adjacent episodes. This
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proved to be unsatisfactory because it
addressed only one of the two important
differences presented by later episodes,
that is, their generally higher cost level.
For the purposes of payment, we
propose to make changes to the OASIS
(see section III. Collection of
Information Requirements), by adding a
new OASIS item to capture whether an
episode is an early or later episode. If an
HHA is uncertain as to whether the
episode is an early or later episode, we
propose to base payment as though the
episode were an early episode. Most
patients do not have more than one
episode in a year. Consequently, we
believe that selecting early as the default
is the best guess as to the eventual
outcome of whether an episode is early
or later.
b. Addition of Variables
Since the system for case-mix
adjustment was first implemented, we
have received comments suggesting
ways in which case-mix adjustment may
be improved. Most of these comments
requested that we add specific variables
or conditions to the case-mix model. We
were also asked to examine the
appropriateness of including additional
diagnosis groups, comorbidities in
general and specific comorbidities, for
instance, heart conditions, additional
wound-related indicators, and other
patient characteristics. We considered
these comments as we proceeded to
explore potential case-mix changes. We
also considered comments received
during the initial rulemaking process,
such as comments pertaining to clinical
issues and social characteristics such as
caregiver availability.
We evaluated variables for inclusion
in a refined case-mix model in much the
same way that we did for the 2000 final
rule, in that we analyzed the
relationship between resource use and
patient characteristics. Whereas the
original case-mix study required us to
collect logs from a sample of episodes
for the measure of resource use, for this
analysis, we were able to measure
resource use directly from the claims
sample. The measures of patient
characteristics come from OASIS
assessments. Under a contract with Fu
Associates of Arlington, Virginia,
Standard Analytical Claims Files from
the National Claims History were
cleaned, edited, and linked to the
OASIS assessment associated with the
beginning of each claim period. Abt
Associates subsequently used these
analytic files to draw large samples of
claims for analysis.
In the course of refining the current
case-mix model, we continued to
monitor the performance of two special
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variables in explaining resource use.
These variables are dual-eligibility for
Medicare and Medicaid and caregiver
support. The two variables are of
interest to some agencies because of
their perceived impact on resource use
and overall profitability. Patients dually
eligible for Medicare and Medicaid may
have health care needs that exceed the
average needs due to the health status
and utilization differences associated
with low-income populations. Some
agencies with caseloads containing large
numbers of dual eligibles have
commented that they are penalized
under the HH PPS system because of
their willingness to serve a
disadvantaged population without
payments explicitly recognizing such
agencies’ higher costs. We have also
received comments that episodes
involving patients without a caregiver
were underpaid by the HH PPS, and that
some agencies would be reluctant to
admit such patients because of financial
implications. These commenters believe
that the low admission rate of patients
without caregivers (about 2 percent of
all episodes) is evidence of this
reluctance.
During our development of the
original case-mix model implemented in
the July 2000 final rule, using the Abt
Associates case-mix study sample, we
tested the Medicaid variable (which
indicates whether Medicaid was among
the patient’s payment sources). At that
time, we found that it did not contribute
meaningfully in explaining variation in
resource use. Similarly, we tested the
caregiver variable and it did not
contribute to explaining variation in
resource cost, either. Regarding the
caregiver variable, we recognized in the
July 3, 2000, final rule that adjusting
payment in response to the presence or
absence of a caregiver may be seen as
inequitable. To the extent that
availability of caregiver services,
particularly privately paid services,
reflects socioeconomic status
differences, we indicated that reducing
payment for patients who have caregiver
assistance may be particularly sensitive
in view of Medicare’s role as an
insurance program rather than a social
welfare program. Furthermore, we
stated that adjusting payment for
caregiver factors would risk introducing
new and negative incentives into family
and patient behavior. In the discussion
in the July 3, 2000 final rule (65 FR
41145), we also indicated our belief that
it is questionable whether Medicare
should adopt a payment policy that
could weaken informal familial
supports currently benefiting patients at
times when they are most vulnerable.
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In our analysis for this proposed rule,
we again tested variables for dual
eligibility and caregiver support. We
operationalized the Medicaid variable
from the OASIS, using the presence of
a Medicaid number on the assessment
as the indicator for Medicaid eligibility.
We found that Medicaid remains a
marginal predictor at best, with a very
low score, after accounting for a broad
range of clinical and functional
variables that predict resource use. We
believe adding a Medicaid variable is
not justified in view of these results,
especially considering the added
administrative burdens for both
agencies and Medicare that using such
a variable would entail. These include
costs of ascertaining whether the
reported Medicaid number is correct
and whether the eligibility status as
reported on the assessment is current.
We also operationalized a variable for
support from a caregiver from the
OASIS assessment, item M0350,
Assisting persons other than home
health agency staff. This variable
identified patients without any
caregiver. While analyzing the payment
adequacy of the four-equation model (as
explained further below) for patients
without a caregiver we found that, on
average, episodes without caregivers
would be ‘‘underpaid’’. However, the
score to be gained by adding the
variable is not large (5 to 13 points,
depending on the episode), and the
overall ability of the four-equation
model to explain resource costs is
improved only minimally by adding this
variable.
Therefore, we are not proposing that
this variable be added to the case-mix
model. We continue to believe that
including this kind of variable in the
case-mix system raises significant policy
concerns. We maintain that a case-mix
adjustment should not discourage
assistance from family members of
home care patients, nor should it make
patients feel there is some financial
stake in how they report their familial
supports during their convalescence.
We continue to believe that adjusting
payment in response to the absence of
a caregiver would introduce negative
incentives with adverse affects on home
health Medicare beneficiaries.
Furthermore, we are doubtful that
today’s low rate of episodes without a
caregiver (2 to 3 percent) reflects access
barriers for these patients and nothing
more. We believe part of the reason for
the low rate may be that under a
bundled payment system agencies are
more careful about ascertaining whether
support is available and encourage use
of caregivers within the beneficiary’s
home.
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For exploratory modeling of case-mix
in our refinement work, in addition to
using existing case-mix variables from
the OASIS, new variables were created.
Diagnosis codes reported on both the
claims and the OASIS were used
extensively to form new or revised
diagnosis groups for inclusion in casemix models. As a result, developmental
models included many new variables,
including an expanded set of primary
and secondary diagnoses, as well as
interaction terms that describe the effect
of combinations of patient conditions or
characteristics on resource cost. Using
these new analytic files, it was possible
to explore some conditions that were
too infrequent to study in the original
case-mix sample. For example, as
suggested by commenters, Abt’s analysis
tested the impact on resource use of
having multiple conditions from M0250,
which reports on therapies received at
home, including intravenous infusion,
and enteral and parenteral nutrition.
The results showed that a variable
indicating the simultaneous presence of
multiple conditions from OASIS item
M0250 did not improve the accuracy of
the case-mix model. However, we did
find that having separate scores for
parenteral nutrition and IV therapy were
not necessary.
Abt’s case-mix analysis focused on
various issues, such as changes to the
list of conditions forming our diagnosis
groups, additions of comorbidities,
prediction of therapy resources, and
interactions. The performance of each
variable was scrutinized based on
several criteria. First, variables were
assessed for statistical performance.
Variables that did not enhance the
accuracy of the model were marked for
exclusion.
Variables were also assessed for
policy appropriateness. Some
statistically significant variables were
excluded if they offered incentives for
providers to distort patterns of good care
or posed excessive administrative
burden on HHAs. In addition, some
statistically weak variables considered
important for clinical or policy reasons
were added back to the model for
further analysis.
We note we excluded a variable from
this proposal, based in part on concerns
of excessive administrative burden. We
propose to exclude OASIS item M0175,
which the case-mix system uses to
identify the patient’s pre-admission
location, from the case-mix models.
Under this proposal, there would be no
case-mix score for M0175. Operational
experience with M0175 revealed that
some agencies have encountered
difficulties in ascertaining precise
information about the patient’s pre-
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admission location during the initial
assessment. These difficulties,
suggestive of unforeseen administrative
complexities, contributed to our
proposal to eliminate M0175 from the
case-mix model.
In addition, the M0175 item did not
perform well in the four-equation
model. We found that the results
differed across the equations in ways
that were difficult to interpret.
Moreover, the results showed that the
impact of including information from
M0175 was small, both in terms of casemix scores and the overall payment
accuracy of the case-mix model.
In weighing the indications of
administrative complexities due to
M0175 against the limited performance
of M0175 in our analysis, we do not find
that the contribution of this item in
explaining case-mix justifies the
operational challenge of achieving
perfectly accurate reporting for
payment. Thus, as noted above, we are
proposing to eliminate it from the casemix model. However, we continue to
believe that it is necessary for the
conditions of participation and the
OASIS to require that agencies establish
the patient’s recent history of health
care before determining the plan of care.
This determination must be made with
sufficient accuracy to allow appropriate
planning, even if precise dates and
institutional certifications are not
exactly known. For example, it will be
important to know the amount and
types of rehabilitation treatment the
patient has received, the type of
institution that delivered the treatment,
and how recently it was delivered.
The final set of proposed clinical
conditions resulting from our
exploratory series of analyses covers
more types of conditions than were used
in the original case-mix model (Tables
2a and 2b). We identified conditions
from diagnosis codes on both claims
and OASIS in a linked sample of claims
from FY 2003 (OASIS items M0230 and
M0240, Diagnoses and Severity Index).
For example, heart and mental
conditions are now assigned case-mix
scores. More wound conditions are
assigned scores, based on results from
adding variables to indicate woundrelated diagnosis codes beyond those in
the current HH PPS case-mix model.
(See Table 2b for diagnosis codes that
define each condition in the model.)
We also propose to assign scores to
certain secondary diagnoses, used to
account for cost-increasing effects of
comorbidities. An example is secondary
cancer diagnoses, whose cost-increasing
effects are not as large as those for
primary cancer diagnoses. However,
with most diagnosis groups, we did not
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make a distinction in the final model
between primary placement and
secondary placement of a condition in
the reported list of diagnoses. We made
case-by-case decisions on this question
based on differences in the impact on
resource cost between the primary
diagnosis and secondary diagnosis. If
differences were small, we combined
cases reporting the conditions,
regardless of whether the listed position
of the diagnosis was primary or
secondary. We believe this is an
important protection against unintended
and undesirable incentive effects that
could arise if agencies perceive
opportunities to change the placement
of the diagnosis due to nonclinical
reasons. In a few instances, the reason
for combining the primary or secondary
diagnoses was to improve the
robustness of the scores.
Finally, we also propose that a small
number of interactions—combinations
of conditions in the same episode—be
assigned scores, to capture the
synergistic effect on resource use of
certain conditions that coexist in the
episode. In some instances, a condition
appears as an interaction with a
functional limitation or a treatment
variable such as parenteral therapy. In
Table 2a, the interaction scores are
added to the case-mix score whenever
the two conditions defining the
interaction occur together in the
episode. Interaction scores, therefore, do
not substitute for scores of other
variables in Table 2a that involve either
only one or the other of the two
conditions.
As noted earlier, we also found that,
compared to early episodes, later
episodes could exhibit a different
relationship between resource costs and
a condition. This is reflected in Table 2a
by the absence of a condition-related
score from one or more of the four
equations, or a score that differs from
one equation to another.
During the later phases of testing
alternative formulations of an expanded
list of clinical conditions, we followed
two rules in our formation of diagnosis
groups. These rules would ultimately
affect the operation of the case-mix
grouper which would be created
pursuant to the revisions being
proposed in this proposed rule. First, if
an episode record in our sample file
listed both primary and secondary
diagnoses from the same diagnosis
group, the model estimation procedure
recognized the primary diagnosis
variable for that case but not the
secondary diagnosis variable. This
means that an episode would not be
eligible to earn more than one score for
the same diagnosis group. The primary
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reason for this rule is that we are aware
of diagnosis coding conventions that
would produce repeated instances of the
same or similar codes in the diagnosis
list, and these conventions would build
redundancy into the modeling process.
A major goal of the exploratory
modeling process was to investigate the
impact of comorbidities by recognizing
secondary diagnoses, but redundancy
inhibits our achievement of that goal.
Consequently, we sought to reduce this
type of redundancy. A further reason for
adhering to this rule is to inhibit a
future decline in model performance,
which might come about through
changes in coding behavior. If agencies
were to perceive that redundant coding
boosts the episode score, they might
engage in it more in the future. The
result would be a degradation in the
ability of the case-mix model to provide
for accurate payment.
The second rule we used affected how
we define the interactions between
conditions. The second rule is that, for
purposes of forming diagnosis groups to
test interactions between conditions,
cases with either a primary or secondary
diagnosis from the same diagnosis group
are combined into a single group. This
means that mention of a given diagnosis
anywhere in the diagnosis list puts
episodes in a single group for that
diagnosis, for purposes of analyzing
interactions between conditions. We
believe this rule is consistent with our
goal of isolating effects of comorbidities.
Specifically, because the reason for
studying interactions is to identify the
effects of combinations of conditions,
we believe it is appropriate to measure
the combinations, regardless of the
placement (that is, primary or
secondary) of a diagnosis on the claim.
Further, combining the primary and
secondary diagnoses within groups
increases the ability of the modeling
process to uncover meaningful
interaction effects. The second rule also
works to keep the model as simple as
possible. Simplicity helps to limit the
risk that the model would not fit well
for later data sets. Simplicity also limits
the amount of added administrative
burden that could come from using a
more-complex model.
Changes to the OASIS are needed to
enable agencies to report secondary
case-mix diagnosis codes. Specifically,
the addition of secondary diagnoses to
the case-mix system (see Table 2a, casemix adjustment variables and scores)
requires that the OASIS allow for
reporting of instances in which a V-code
is coded in place of a case-mix diagnosis
other than the primary diagnosis. A
case-mix diagnosis is a diagnosis that
determines the HH PPS case-mix group.
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Currently, the OASIS allows for
reporting of instances of displacement
involving primary diagnosis only
(M0245). Consequently, because of the
nature and significance of the changes
needed, we are proposing to delete the
OASIS item M0245 and replace it with
a new OASIS item. (see section III.
Collection of Information
Requirements).
c. Addition of Therapy Thresholds
As set forth in the July 3, 2000 final
rule (65 FR 1128), patients were
grouped according to their therapy
utilization status in order to ensure that
patients who required therapy would
maintain access to appropriate services.
Specifically, we defined a therapy
threshold of at least 8 hours of
combined physical, speech, or
occupational therapy over the 60-day
episode, to identify ‘‘high’’ therapy
cases. The 8-hour threshold was
converted to a threshold of 10 therapy
visits because the average visit length
for therapy noted in our data was
approximately 48 minutes. We
instituted the threshold based on
clinical judgment about the level of
therapy that reflects a clear need for
rehabilitation services and that would
reasonably be expected to result in
meaningful treatment over the course of
60 days.
Since the implementation of the
therapy threshold in the HH PPS, we
have received comments from the
public requesting that we study and
refine this approach to accounting for
rehabilitation needs in the case-mix
system. Commenters have suggested
that a single therapy threshold did not
fairly reflect the variation in therapy
utilization and need. Some commenters
requested that we re-examine the 10visit threshold. Other commenters
recommended that we work to eliminate
the therapy threshold, in part due to
concerns that the therapy threshold
might introduce incentives to distort
service delivery patterns for payment
purposes.
Our data analysis revealed evidence
of undesirable incentives from the 10visit therapy threshold. Our analysis
suggested that the 10-visit therapy
threshold might have distorted service
delivery patterns. In our analysis
sample, of all episodes at or above the
threshold, half were concentrated in the
range of 10 to 13 therapy visits. This
range had the highest concentration of
therapy episodes among episodes with
at least one therapy visit. In contrast, a
large analysis sample from a period
immediately preceding the HH PPS
indicated that the highest concentration
of therapy episodes was in a range
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below the 10-visit threshold—
approximately 5 to 7 therapy visits.
Under the HH PPS, there were two
peaks in the graphic depiction of
numbers of episodes according to the
number of therapy visits delivered
during the episode. One peak was below
the therapy threshold and the other was
the 10 to 13 visit peak above the therapy
threshold. In the pre-PPS sample, there
was only one peak in the depiction, and
it was the concentration of episodes at
5 to 7 therapy visits—below the current
10-visit therapy threshold. All of these
results suggested that the 10-visit
threshold was responsible for a marked
shift in rehabilitation services delivery
under the HH PPS, a shift that we
believe would probably not have
occurred in the absence of the therapy
threshold. Commenters have reinforced
our belief that the impact of the single
10-visit threshold on therapy provision
frequently distorted the clinically based
decision-making that should drive the
delivery of rehabilitation services.
In our early efforts to address
problems inherent in using a therapy
threshold, we conducted analyses to
identify new predictors of therapy
resource use, with the goal of achieving
large gains in explanatory power that
would render the therapy threshold
unnecessary. We used predictor
variables including pre-admission status
on activities of daily living (ADL), more
diagnoses with a focus on conditions
such as stroke, and more OASIS
variables. However, models that
included these particular explanatory
variables predicted the probability of
using therapy, but not how much
therapy would be used.
Successive studies to account for
therapy resources followed the goal of
reducing the impact of a therapy
threshold on the payment weights. The
main conclusion from these studies was
that therapy resources cannot be
predicted with sufficient accuracy to
eliminate the need for therapy
thresholds in the HH PPS case-mix
system. Although we tried several
alternative approaches, no approach
added sufficient predictive power to the
case-mix model. Therefore, continued
analysis focused primarily on refining
the therapy threshold approach to
reduce undesirable incentives. This
work involved experimentation with
alternative sets of thresholds consisting
of more than one threshold.
After testing several sets of
thresholds, and in consideration of the
comments received, we proceeded to
construct case-mix models with
thresholds at 6, 14, and 20 therapy
visits. We used these thresholds based
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on data analysis and, in part, on policy
considerations.
Data analysis suggested it would be
appropriate to add new thresholds both
below and above the 10-visit level. One
reason was that our review of data from
the HH PPS period showed agencies
provided large numbers of episodes
with therapy visits in an interval below
10 visits. Moreover, data analysis
suggested that, of all episodes with
numbers of therapy visits below the 10visit therapy threshold, some subsets
did not receive an appropriate case-mix
weight under the HH PPS. Specifically,
episodes with 6 to 9 therapy visits had
resource costs that seemingly exceeded
the payment proxied in our analysis by
the predicted resource cost under the
current case mix model. However, we
now believe that several common
treatment plans require only about 6
visits, for example, assessments and
treatment of certain types of patients at
high risk for falls. We are therefore
proposing that one threshold be added
at 6 therapy visits.
In considering thresholds above the
current 10-visit threshold, we observed
that nearly half of episodes involving
therapy comprise episodes with 6 to 13
therapy visits. Therefore, we are
proposing a second threshold at 14
therapy visits, which would have two
advantages. First, this range covers the
two peaks (that is, the one we observed
below the 10-visit therapy threshold and
the one we observed above the 10-visit
threshold) in the distribution of therapy
visits under the HH PPS. By avoiding a
therapy threshold within this range, we
hope to reduce the influence of payment
incentives on treatment decisions.
Second, we believe that the interval of
6 to 13 therapy visits represents a
reasonable range of treatment levels for
most rehabilitation episodes. For
example, the range of 6 to 13 therapy
visits encompasses typical treatment
plans for both knee- and hipreplacement patients. As we describe
later in this section, we propose to use
further steps to address payment
accuracy, by adding payment gradations
within the intervals bounded by the
three thresholds we are proposing.
We further observed that only a
relatively small fraction of patients use
14 or more therapy visits. While no
bright-line tests are available to
distinguish a 14-visit case, we have
received comments indicating that
medical review staff at the fiscal
intermediaries will have less difficulty
judging appropriateness of treatment
plans at this level, because such plans
are intensive and not the norm.
Additionally, although few episodes
require 20 or more therapy visits, we set
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the third therapy threshold at 20 visits.
Our concern is to ensure access to
appropriate treatment in the rare cases
where such intensive treatment is
necessary. Our analysis suggested that
these episodes are extremely costly for
agencies, so a payment adjustment to
accommodate this service level is
appropriate. Furthermore, commenters
indicated that, because only rare cases
should warrant this high number of
therapy visits, monitoring of claims to
prevent abuse of this payment
provision, using our medical review
resources, is feasible operationally.
Adding therapy thresholds in the
revised case-mix regression model
improves the ability of the model to
predict resource use. The R-squared
values for a three-therapy threshold
model increased substantially for both
early and later episodes over the Rsquared values for a single therapy
threshold model. In other words, using
additional therapy thresholds clearly
improved the case-mix system’s ability
to classify episodes into homogeneous
cost groups.
The combined effect of the new
therapy thresholds and payment
gradations (to be described below) is
expected to reduce the undesirable
emphasis in treatment planning on a
single therapy visit threshold, and to
restore the primacy of clinical
considerations in treatment planning for
rehabilitation patients.
During the analysis of the therapy
threshold, we considered ways to
provide for payment gradations between
the therapy thresholds. We sought a way
to implement a gradual increase in
payment (see Table 1) between the
proposed first and third therapy
thresholds. We believe a case-mix
model that increases payment with each
added visit between the proposed first
and third thresholds would achieve two
goals. First, a gradual increase better
matches payments to costs than the
therapy thresholds alone. Second, a
gradual increase avoids incentives for
providers to distort patterns of good care
created by the increase in payment that
would occur at each proposed therapy
threshold. However, as a disincentive
for agencies to deliver more than the
appropriate, clinically determined
number of therapy visits, we are also
proposing that any per-visit increase
incorporate a declining, rather than
constant, amount per added therapy
visit. We implemented this in the casemix model by decreasing slightly the
added amount per therapy visit as the
number of therapy visits grew above the
proposed 6-visit threshold. Specifically,
we began with a value determined from
our sample—the estimated marginal
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resource cost incurred by adding a 7th
therapy visit to the treatment plan. This
is the first additional visit above the
proposed six-visit therapy threshold.
The estimated marginal cost of adding a
7th therapy visit to an episode with six
therapy visits was $36. Using this value
as our starting point, we required the
case-mix model to add a slightly lower
value to the total episode resource cost
with each additional therapy visit
provided, up to the 19th therapy visit.
This proposed approach imposes a
deceleration of the growth in payment
with each additional therapy visit.
However, this proposed approach does
not reduce total payments to home
health providers, because the regression
analysis still predicts the full resource
cost of the episode. Table 1 shows the
values that we imposed in the fourequation model estimation procedure to
implement a deceleration in the added
resource cost for individual therapy
visits between 6 and 20 therapy visits.
The individual values begin at $36 and
then decline at a constant rate of one
resource cost dollar per therapy visit
between 6 and 20 therapy visits. These
values represent the score that was
imposed in the model for adding each
additional therapy visit. The case-mix
model that incorporates the imposed
scores is called a ‘‘restricted regression
model.’’ The results of the restricted
regression model of the four-equation
system, including scores for diagnoses
and conditions, and R-squared statistics,
exhibited little change from imposing
this pattern of deceleration in cost
growth due to additional therapy visits.
TABLE 1.—RESOURCE COST VALUES IMPOSING DECELERATION TREND IN FOUR-EQUATION MODEL
Number of
therapy visits in
severity level
Equation and services utilization severity level
1st and
S3
S4
S5
Resource cost
values imposed
in regression
procedure
2nd Episodes, 6–13 Therapy Visits
.............................................................................................................................................................
.............................................................................................................................................................
.............................................................................................................................................................
7, 8, 9
10
11, 12, 13
36, 35, 34
33
32, 31, 30
1st and 2nd Episodes, 14–19 Therapy Visits
S1* ............................................................................................................................................................
S2 .............................................................................................................................................................
S3 .............................................................................................................................................................
15
16, 17
18, 19
28
27, 26
25, 24
3rd+ Episodes, 6–13 Therapy Visits
S3 .............................................................................................................................................................
S4 .............................................................................................................................................................
S5 .............................................................................................................................................................
7, 8, 9
10
11, 12, 13
36, 35, 34
33
32, 31, 30
3rd+ Episodes, 14–19 Therapy Visits
S1* ............................................................................................................................................................
S2 .............................................................................................................................................................
S3 .............................................................................................................................................................
15
16, 17
18, 19
28
27, 26
25, 24
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* For the second and fourth equations of the four equation model, S1 includes 14 therapy visits, but no value was imposed in the regression
procedure for a 14th therapy visit because the regression intercept estimate automatically includes the resource cost impact.
The case-mix model at this stage was
very detailed, because it included
variables incorporating information
about thresholds and therapy visit
counts. We were concerned that,
without streamlining the therapy-related
information in the case-mix model, the
ultimate system of case-mix groups
would contain an excessive number of
case-mix groups. We recognize an
extremely large number of case-mix
groups would make the HH PPS
complex to administer. Because the
therapy-related details of the case-mix
model are based on numbers of therapy
visits, another issue would be that many
case-mix groups would be differentiated
based on visit counts, thereby making
the system dependent on visits and less
of a bundled system of services.
Therefore, in order to form case-mix
groups from the results of the case-mix
model, we grouped the individual levels
of therapy visits into small aggregates (1,
2, or 3 visits) (see Table 1). By doing so,
we avoided creating a per-visit schedule
of payment to account for therapy visits.
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We implemented these aggregations as
differing severity levels at a subsequent
stage of payment system development,
the payment regression, which is
described later in this section.
The proposed four equation model,
with multiple therapy thresholds and
payment graduation between those
thresholds, adds a certain amount of
complexity to the HH PPS.
Consequently, in order to group
beneficiaries into case-mix groups in
this proposed four equation model, we
propose to make changes to the OASIS
to capture the projected number of total
therapy visits for a given episode (see
section III. Collection of Information
Requirements), as opposed to indicating
if there is a projected need for ten or
more therapy visits (current OASIS item
M0825). Each severity level of the
services utilization dimension
represents a different number of therapy
visits (see also Table 3: Severity Group
Definitions: Four-Equation Model).
An additional aspect of our therapy
threshold research addressed changing
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the unit of measurement of therapy
thresholds from visits to minutes. In the
July 2000 final rule, we indicated our
intention to continue study of the
appropriate unit of measurement for
therapy services.
An important finding of our initial
analyses on this question was that the
length of therapy visits in minutes, on
average, exhibited little change between
the period covered by the original Abt
Associates case-mix study, and the HH
PPS period, based on data through 2003.
We also found that the distribution of
average therapy visit lengths was highly
similar under HH PPS, regardless of the
total number of therapy visits in the
episode. A possible exception was
episodes with 1 to 4 therapy visits,
where a relatively high proportion of
episodes (about 16 percent) had average
therapy visit lengths of 30 minutes or
less; no more than 9 percent of
remaining episodes (more than four
therapy visits) had averages of 30
minutes or less. There was also a slight
tendency for these short average visit
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lengths to become less frequent as the
total therapy visit count per episode
grew. Overall, the data indicated that at
least 85 percent of episodes with
therapy visits involved visits averaging
at least 41 minutes. These results
suggest that therapy practitioners tend
to have consistent session lengths across
many types of episodes.
We are proposing no change in the
current way in which we measure
therapy thresholds, which is based on
counting therapy visits, in light of our
analysis indicating that individual
therapy visits appear to vary little in
their length, regardless of the frequency
of visits during the 60-day episode, and
our analysis indicating that average visit
lengths have remained stable since the
time of the Abt case-mix study.
Additionally, we are concerned
incentive issues would arise if we
changed the definition. The low
variability in visit lengths appears to be
an indication that under current
practices, therapy session lengths are
fairly uniform, regardless of the time
period or intensity of the rehabilitation
course of treatment. These practices
have arisen out of clinical experience in
the rehabilitation professions.
Introducing a minutes or time standard
risks introducing new financial
incentives that might influence these
widely held practices. We are concerned
that changing to a minutes standard
might result in financially driven
pressures on clinical decisions
concerning the number of sessions in a
patient’s course of treatment, with
potentially adverse effects on
beneficiary outcomes.
One of our original concerns in
proposing a visit-based threshold was
that minutes unit reporting on the
claims, which was a relatively new
requirement at that time, might be
unreliable. (Section 1895(c)(2) requires
the claim to report the length of each
billed visit as measured in 15-minute
increments.) Based upon our
experiences using the claims data in our
research, we have no reason to believe
this is a problem. Moreover, we believe
the dual requirements to report both
visit dates and minutes of each visit on
Medicare claims should remain in place
because they provide important
information for program integrity
activities and future research.
Based upon our analysis of the casemodel described in section II.A.2, we
propose to use four separate equations
to derive scores for conditions including
the proposed therapy thresholds. The
proposed first equation is for early
episodes below the 14-visit therapy
threshold. The proposed second
equation is for early episodes at or
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above the 14-visit therapy threshold.
The proposed third equation is for later
episodes below the 14-visit therapy
threshold. The proposed fourth equation
is for later episodes above the 14-visit
therapy threshold. A threshold at 6
visits is accounted for by an indicator
variable in the proposed first and third
equations, and a threshold at 20 visits
is accounted for by an indicator variable
in the proposed second and fourth
equations. In addition, therapy visit
count variables are added to the
equations to model the graduated
payment with each therapy visit
between 6 and 20 visits. Finally, as we
explained above, we imposed specific
values for the coefficients of the therapy
visit count variables. The resulting fourequation model has an improved
statistical performance (an R-squared
statistic of approximately 0.44) over the
current model (an R-squared statistic of
0.21). The primary reason for the
improvement in the proposed case-mix
model fit (compared to the R-square
statistic of 0.21 cited earlier) is the fourequation structure. This structure
recognizes cost differences between
early and later episodes, and between
therapy treatment plans above and
below the proposed 14-visit therapy
threshold. Additional improvements
come from adding other therapy
variables to the case-mix model,
specifically, the two additional
thresholds (6 and 20 visits) and
graduated payment—and from the new
case-mix variables discussed in section
II.A.2.a of this proposed rule.
We believe that in addition to
improved statistical performance, the
proposed model would provide better
incentives for the provision of highquality home health care without an
undue increase in administrative
burden. For a more detailed discussion
of the technical aspects of the fourequation model go to the CMS Web site
(https://www.cms.hhs.gov/hha.asp) for a
link to Abt’s Technical Report.
Table 2a presents the full set of casemix scores (other than the imposed
scores for therapy visits) and all clinical
and functional variables we are
proposing for the refined case-mix
model. In Table 2a, the score is the
value of the regression coefficient for
the variable; it measures the impact of
the data element on total resource cost
of the episode. See Table 2b for an
inclusive list of ICD–9–CM diagnosis
codes applicable for each scored
condition variable in Table 2a. These
codes define the clinical condition
variables in our proposed model. We
intend to continue to evaluate the
appropriateness of these diagnosis codes
in Table 2b. We believe the HH PPS
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case-mix system should avoid, to the
fullest extent possible, nonspecific or
ambiguous ICD–9–CM codes, codes that
represent general symptomatic
complaints in the elderly population,
and codes that lack consensus for clear
diagnostic criteria within the medical
community. We solicit detailed
suggestions from the public concerning
codes that threaten to move the system
away from a foundation of reliable and
meaningful diagnosis codes.
Compared to the original four
diagnosis groups in the case-mix model,
the code groups in Table 2b incorporate
additions and new group placements for
individual ICD–9–CM diagnosis codes.
Two variables from the original case
mix system are not proposed: M0175, as
noted earlier, and M0610, behavioral
problems, which did not perform well
in our studies. We believe that several
additions to our diagnosis groups,
namely, two groups for psychiatric
diagnoses, account for the contribution
of behavioral problems to resource cost
variation.
We are aware that some of the
diagnosis codes listed in Table 2b are
manifestation codes. The ICD–9–CM
Official Guidelines for Coding and
Reporting requires that the underlying
disease or condition code be sequenced
first, followed by the manifestation
code. The underlying disease codes
associated with the manifestation codes
are not listed in Table 2b. However,
appropriate sequencing was accounted
for in our analysis. When reporting
certain conditions that have both an
underlying etiology and a body system
manifestation due to the underlying
etiology, the appropriate sequencing
should be followed according to the
ICD–9–CM Coding Guidelines.
For purposes of determining final
estimates on which to base the data set
used in the final rule for CY 2008, we
intend to update the dataset used for the
four-equation model to CY 2005; as
noted above, the proposal to use the
four-equation model is based on linked
claims and OASIS data from FY 2003.
We are aware that adding data from a
later period may result in some
variations, including some significant
changes, in the scores presented in
Table 2a. Some changes may occur
because, effective October 2003 (FY
2004), diagnosis coding instructions on
the OASIS assessment changed to allow
for the use of ICD–9–CM V-codes.
V-codes, particularly those applicable to
home health services, do not in general
describe disease states; rather, they
describe reasons for using services. The
major use of V-codes in the home health
setting occurs when a person with
current or resolving disease or injury
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encounters the health care system for
specific aftercare of that disease or
injury. For example, V-code V57.21 is
reportable when the reason for the visit
is ‘‘encounter for occupational therapy.’’
As such, V-codes are less specific to the
clinical condition of the patient than are
numeric diagnosis codes. A single
V-code could substitute for various
numeric codes, each of which describes
a specific, different clinical condition.
Medical review activities revealed an
inappropriate utilization of V-codes
following the effective date of V-codes
on OASIS (October, 2003). In response
to RHHI reports of increased provider
non-compliance with correct ICD–9–CM
coding procedures related to V-codes,
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we posted OASIS diagnosis training on
the CMS Web site and promoted RHHI
provider educational efforts.
Nonetheless, medical review activities
continue to report an excessive
utilization of the V–57 codes, signaling
a possible non-compliance with correct
coding practice related to the V-codes.
We are concerned that more use of
V-codes could reduce data adequacy for
modeling the impacts of clinical
conditions we are proposing to use to
predict resource use. One result, for
example, might be a markedly different
score for some conditions with lower
reporting rates under the V-code
instructions effective October 2003.
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At this time, we do not know whether
allowing V-codes on the OASIS, along
with the over-use of V-codes revealed by
medical review activities, significantly
lowered the frequencies of non-V-code,
numeric diagnosis codes for the clinical
conditions we propose to use in the case
mix model. Again, this could have
occurred because of the way V-codes
can displace a numeric code in the
diagnosis list. If we find evidence that
numeric codes’ frequencies were
reduced to the extent that it strongly
influenced the scores we present in this
proposal, we propose to base the refined
system on the data from FY 2003.
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TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT VARIABLES
Manifestation*
Short description of ICD–9–CM code
369.0
369.1
369.2
369.3
369.4
950
281
282
283
284
285
286
287
288
289
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
.........................
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.........................
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.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
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.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
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.........................
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.........................
.........................
.........................
PROFOUND BLIND BOTH EYES
MOD/SEV W PROFND IMPAIR
MOD/SEV IMPAIR-BOTH EYES
BLINDNESS NOS, BOTH EYES
LEGAL BLINDNESS-USA DEF
INJURY TO OPTIC NERVE AND PATHWAYS
OTHER DEFICIENCY ANEMIAS
HEREDITARY HEMOLYTIC ANEMIAS
ACQUIRED HEMOLYTIC ANEMIAS
APLASTIC ANEMIA
OTHER AND UNSPECIFIED ANEMIAS
COAGULATION DEFECTS
PURPURA&OTHER HEMORRHAGIC CONDS
DISEASES OF WHITE BLOOD CELLS
OTH DISEASES BLD&BLD-FORMING ORGANS
MALIGNANT NEOPLASM OF LIP
MALIGNANT NEOPLASM OF TONGUE
MALIG NEOPLASM MAJOR SALIV GLANDS
MALIGNANT NEOPLASM OF GUM
MALIGNANT NEOPLASM FLOOR MOUTH
MALIG NEOPLSM OTH&UNSPEC PART MOUTH
MALIGNANT NEOPLASM OF OROPHARYNX
MALIGNANT NEOPLASM OF NASOPHARYNX
MALIGNANT NEOPLASM OF HYPOPHARYNX
OTH MALIG NEO LIP-MOUTH-PHARYNX
MALIGNANT NEOPLASM OF ESOPHAGUS
MALIGNANT NEOPLASM OF STOMACH
MALIG NEOPLSM SM INTEST INCL DUODUM
MALIGNANT NEOPLASM OF COLON
MAL NEO RECT RECTOSIGMOID JUNC&ANUS
MALIG NEOPLASM LIVER&INTRAHEP BDS
MALIG NEOPLSM GALLBLADD&XTRAHEP BDS
MALIGNANT NEOPLASM OF PANCREAS
MALIG NEOPLASM RETROPERITON&PERITON
MAL NEO DIGES ORGANS&PANCREAS OTH
MAL NEO NASL CAV/MID EAR&ACSS SINUS
MALIGNANT NEO LARYNX*
MALIGNANT NEO TRACHEA/LUNG*
Blindness and low vision .....................................
Blood disorders ....................................................
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Cancer and selected benign neoplasms .............
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ICD–9–CM
code**
Diagnostic category
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25373
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
ICD–9–CM
code**
Diagnostic category
Manifestation*
163
164
165
170
171
172
173
174
175
176
179
180
181
182
183
184
185
186
187
188
189
190
192.0
192.8
192.9
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
213
225.1
225.8
225.9
230
231
232
233
234
250
357.2
362.01
362.02
366.41
787.2
781.2
002
003
004
005
006
007
008
009
530
531
532
533
534
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M .....................
M .....................
M .....................
M .....................
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Diabetes ...............................................................
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Dysphagia ............................................................
Gait Abnormality ..................................................
Gastrointestinal disorders ....................................
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Short description of ICD–9–CM code
MALIGNANT NEOPL PLEURA*
MAL NEO THYMUS/MEDIASTIN*
OTH/ILL-DEF MAL NEO RESP*
MALIG NEOPLASM BONE&ARTICLR CART
MALIG NEOPLSM CNCTV&OTH SOFT TISSUE
MALIGNANT MELANOMA OF SKIN
OTHER MALIGNANT NEOPLASM OF SKIN
MALIGNANT NEOPLASM OF FEMALE BREAST
MALIGNANT NEOPLASM OF MALE BREAST
KAPOSIS SARCOMA
MALIG NEOPLASM UTERUS PART UNSPEC
MALIGNANT NEOPLASM OF CERVIX UTERI
MALIGNANT NEOPLASM OF PLACENTA
MALIGNANT NEOPLASM BODY UTERUS
MALIG NEOPLSM OVRY&OTH UTERN ADNEXA
MALIG NEOPLSM OTH&UNS FE GENIT ORGN
MALIGNANT NEOPLASM OF PROSTATE
MALIGNANT NEOPLASM OF TESTIS
MAL NEOPLSM PENIS&OTH MALE GNT ORGN
MALIGNANT NEOPLASM OF BLADDER
MAL NEO KIDNEY&OTH&UNS URIN ORGN
MALIGNANT NEOPLASM OF EYE
MALIGNANT NEOPLASM, CRANIAL NERVES
MALIGNANT NEOPLASM OTHER NERV SYS
MALIGNANT NEOPLASM, UNS PART NERV SYS
MALIGNANT NEOPLASM OF THYROID GLAND
MAL NEO OTH ENDOCRN GLND&REL STRCT
MALIG NEOPLASM OTH&ILL-DEFIND SITES
SEC&UNSPEC MALIG NEOPLASM NODES
SEC MALIG NEOPLASM RESP&DIGESTV SYS
SEC MALIG NEOPLASM OTHER SPEC SITES
MALIG NEOPLASM WITHOUT SPEC SITE
LYMPHOSARCOMA AND RETICULOSARCOMA
HODGKINS DISEASE
OTH MAL NEO LYMPHOID&HISTCYT TISS
MX MYELOMA&IMMUNOPROLIFERAT NEOPLSM
LYMPHOID LEUKEMIA
MYELOID LEUKEMIA
MONOCYTIC LEUKEMIA
OTHER SPECIFIED LEUKEMIA
LEUKEMIA OF UNSPECIFIED CELL TYPE
BEN NEOPLASM BONE&ARTICLR CARTILAGE
BEN NEOPLSM CRANIAL NERVES
BEN NEOPLSM OTH SPEC SITES
BEN NEOPLSM UNSPEC PART NERV SYS
CA IN SITU—DIGEST
CA IN SITU—RESP
CARCINOMA IN SITU OF SKIN
CA IN SITU—BREAST AND GU
CA IN SITU—OTH
DIABETES MELLITUS
POLYNEUROPATHY IN DIABETES
BACKGROUND DIABETIC RETINOPATHY
PROLIFERATIVE DIABETIC RETINOPATHY
DIABETIC CATARACT
DYSPHAGIA
ABNORM GAIT
TYPHOID AND PARATYPHOID FEVERS
OTHER SALMONELLA INFECTIONS
SHIGELLOSIS
OTHER FOOD POISONING
AMEBIASIS
OTHER PROTOZOAL INTESTINAL DISEASES
INTESTINAL INFS DUE OTH ORGANISMS
ILL-DEFINED INTESTINAL INFECTIONS
DISEASES OF ESOPHAGUS
GASTRIC ULCER
DUODENAL ULCER
PEPTIC ULCER, SITE UNSPECIFIED
GASTROJEJUNAL ULCER
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TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
ICD–9–CM
code**
Diagnostic category
Manifestation*
535
536
537
540
541
542
543
555
556
557
558
560
562
564
567
568
569
570
571
572
573
574
575
576
577
578
579
783.2
410
411
428
401
402
403
404
405
013
047
046
048
049
191
192.2
192.3
225.0
225.2
225.3
225.4
320.0
320.1
320.2
320.3
320.7
320.81
320.82
320.89
320.9
321.0
321.1
321.2
321.3
321.4
321.8
322
323.0
323.1
323.2
323.4
323.5
323.6
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M .....................
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M .....................
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M .....................
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M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
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M .....................
M .....................
M .....................
M .....................
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M .....................
Heart Disease ......................................................
Hypertension ........................................................
cprice-sewell on DSK89S0YB1PROD with RULES
Neuro 1—Brain disorders and paralysis ..............
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Short description of ICD–9–CM code
GASTRITIS AND DUODENITIS
DISORDERS OF FUNCTION OF STOMACH
OTHER DISORDERS OF STOMACH&DUODENUM
ACUTE APPENDICITIS
APPENDICITIS, UNQUALIFIED
OTHER APPENDICITIS
OTHER DISEASES OF APPENDIX
REGIONAL ENTERITIS
ULCERATIVE COLITIS
VASCULAR INSUFFICIENCY OF INTESTINE
OTH NONINF GASTROENTERITIS&COLITIS
INTEST OBST W/O MENTION HERN
DIVERTICULA OF INTESTINE
FUNCTIONAL DIGESTIVE DISORDERS NEC
PERITONITIS
OTHER DISORDERS OF PERITONEUM
OTHER DISORDERS OF INTESTINE
ACUTE&SUBACUTE NECROSIS OF LIVER
CHRONIC LIVER DISEASE AND CIRRHOSIS
LIVER ABSC&SEQUELAE CHRON LIVR DZ
OTHER DISORDERS OF LIVER
CHOLELITHIASIS
OTHER DISORDERS OF GALLBLADDER
OTHER DISORDERS OF BILIARY TRACT
DISEASES OF PANCREAS
GASTROINTESTINAL HEMORRHAGE
INTESTINAL MALABSORPTION
ABNORMAL LOSS OF WEIGHT
ACUTE MYOCARDIAL INFARCTION
OTH AC&SUBAC FORMS ISCHEMIC HRT DZ
HEART FAILURE
ESSENTIAL HYPERTENSION
HYPERTENSIVE HEART DISEASE
HYPERTENSIVE RENAL DISEASE
HYPERTENSIVE HEART&RENAL DISEASE
SECONDARY HYPERTENSION
TB MENINGES&CNTRL NERV SYS
MENINGITIS DUE TO ENTEROVIRUS
SLOW VIRUS INFECTION CNTRL NERV SYS
OTH ENTEROVIRUS DZ CNTRL NERV SYS
OTH NON-ARTHROPOD BORNE VIRL DX-CNS
MALIGNANT NEOPLASM OF BRAIN
MALIG NEOPLSM SPINAL CORD
MALIG NEOPLSM SPINAL MENINGES
BEN NEOPLSM BRAIN
BEN NEOPLSM BRAIN MENINGES
BEN NEOPLSM SPINAL CORD
BEN NEOPLSM SPINAL CORD MENINGES
HEMOPHILUS MENINGITIS
PNEUMOCOCCAL MENINGITIS
STREPTOCOCCAL MENINGITIS
STAPHYLOCOCCAL MENINGITIS
MENINGITIS OTH BACT DZ CLASS ELSW
ANAEROBIC MENINGITIS
MENINGITIS DUE GM-NEG BACTER NEC
MENINGITIS DUE OTHER SPEC BACTERIA
MENINGITIS DUE UNSPEC BACTERIUM
CRYPTOCOCCAL MENINGITIS
MENINGITIS IN OTHER FUNGAL DISEASES
MENINGITIS DUE TO VIRUSES NEC
MENINGITIS DUE TO TRYPANOSOMIASIS
MENINGITIS IN SARCOIDOSIS
MENINGITIS-OTH NONBCTRL ORGNISMS CE
MENINGITIS OF UNSPECIFIED CAUSE
ENCEPHALITIS VIRAL DZ CLASS ELSW
ENCEPHALIT RICKETTS DZ CLASS ELSW
ENCEPHALIT PROTOZOAL DZ CLASS ELSW
OTH ENCEPHALIT DUE INF CLASS ELSW
ENCEPHALIT FOLLOW IMMUNIZATION PROC
POSTINFECTIOUS ENCEPHALITIS
E:\FR\FM\04MYP2.SGM
04MYP2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
25375
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
ICD–9–CM
code**
Manifestation*
Short description of ICD–9–CM code
323.7
323.8
323.9
324
325
326
330.0
330.1
330.2
330.3
330.8
330.9
334.1
335
336.1
336.2
336.3
336.8
336.9
337.3
344.1
344.8
344.9
348
349.82
336.0
344.0
741
780.01
780.03
806
851
852
853
854
907.0
907.1
907.2
907.3
M .....................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
M .....................
M .....................
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.........................
M .....................
M .....................
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907.4
.........................
907.5
.........................
907.9
Diagnostic category
.........................
952
045
332
333
334.0
334.2
334.3
334.4
334.8
334.9
337.0
337.1
337.20
337.21
337.22
337.29
337.9
343
344.2
352
353.0
353.1
353.5
354.5
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M .....................
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M .....................
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TOXIC ENCEPHALITIS
OTHER CAUSES OF ENCEPHALITIS
ENCEPHALITUS NOS
INTRACRANIAL&INTRASPINAL ABSCESS
PHLEBIT&THRMBOPHLB INTRACRAN VENUS
LATE EFF INTRACRAN ABSC/PYOGEN INF
LEUKODYSTROPHY
CEREBRAL LIPIDOSES
CEREB DEGEN IN LIPIDOSIS
CERB DEG CHLD IN OTH DIS
CEREB DEGEN IN CHILD NEC
CEREB DEGEN IN CHILD NOS
HERED SPASTIC PARAPLEGIA
ANTERIOR HORN CELL DISEASE
VASCULAR MYELOPATHIES
SUBACUTE COMB DEGEN SPINL CRD DZ CE
MYELOPATHY OTH DISEASES CLASS ELSW
OTHER MYELOPATHY
UNSPECIFIED DISEASE OF SPINAL CORD
AUTONOMIC DYSREFLEXIA
PARAPLEGIA
LOCKED-IN STATE
PARALYSIS UNSPECIFIED
OTHER CONDITIONS OF BRAIN
OTH&UNSPEC DISORDERS NERVOUS SYSTEM
SYRINGOMYELIA AND SYRINGOBULBIA
QUADRAPLEGIA
SPINA BIFIDA
COMA
PERSISTENT VEGETATIVE STATE
FX VERT COLUMN W/SPINAL CORD INJURY
CEREBRAL LACERATION AND CONTUSION
SUBARACH SUB&XTRADURL HEMOR FLW INJ
OTH&UNS INTRACRAN HEMOR FLW INJURY
INTRACRAN INJURY OTH&UNSPEC NATURE
LATE EFF INTRACRANIAL INJURY
LATE EFFECT OF INJURY TO CRANIAL NERVE
LATE EFFECT OF SPINAL CORD INJURY
LATE EFFECT OF INJURY TO NERVE ROOT(S), SPINAL
PLEXUS(ES), AND OTHER NERVES OF TRUNK
LATE EFFECT OF INJURY TO PERIPHERAL NERVE OF
SHOULDER GIRDLE AND UPPER LIMB
LATE EFFECT OF INJURY TO PERIPHERAL NERVE OF
PELVIC GIRDLE AND LOWER LIMB
LATE EFFECT OF INJURY TO OTHER AND UNSPECIFIED
NERVE
SP CRD INJR W/O EVIDENCE SP BN INJR
ACUTE POLIOMYELITIS
PARKINSONS DISEASE
OTH XTRAPYRAMIDAL DZ&ABN MOVMNT D/O
FRIEDREICH’S ATAXIA
PRIMARY CEREBELLAR DEGEN
CEREBELLAR ATAXIA NEC
CEREBEL ATAX IN OTH DIS
SPINOCEREBELLAR DIS NEC
SPINOCEREBELLAR DIS NOS
IDIOPATH PERIPH AUTONOM NEUROPATHY
PRIPHERL AUTONOMIC NEUROPTHY D/O CE
UNSPEC REFLEX SYMPATHETIC DYSTROPHY
REFLX SYMPATHET DYSTROPHY UP LIMB
REFLX SYMPATHET DYSTROPHY LOW LIMB
REFLX SYMPATHET DYSTROPHY OTH SITE
UNSPEC DISORDER AUTONOM NERV SYSTEM
INFANTILE CEREBRAL PALSY
DIPLEGIA OF BOTH UPPER LIMBS
DISORDERS OF OTHER CRANIAL NERVES
BRACHIAL PLEXUS LESION
LUMBOSACRAL PLEXUS LESION
NEURALGIC AMYLOTROPHY
MONONEURITIS MULTIPLEX
cprice-sewell on DSK89S0YB1PROD with RULES
Neuro 2—Peripheral neurological disorders ........
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25376
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
ICD–9–CM
code**
Diagnostic category
Manifestation*
355.2
355.9
356
357.0
357.1
357.3
357.4
357.5
357.6
357.7
357.82
357.89
357.9
358.00
358.01
358.1
358.2
358.9
359.0
359.1
359.3
359.4
359.5
359.6
359.8
359.9
386.0
386.2
386.3
392
953
954
955.8
956.0
956.1
956.8
342
344.3
344.4
344.6
430
431
432
433.01
433.11
433.21
433.31
433.81
434.01
434.11
781.8
436
438
435
340
341
711.05
711.06
711.07
711.15
711.16
711.17
711.25
711.26
711.27
711.35
711.36
711.37
711.45
711.46
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M .....................
M .....................
M .....................
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M .....................
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M .....................
M .....................
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M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
Neuro 3—Stroke ..................................................
Neuro 4—Multiple Sclerosis ................................
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Ortho 1—Leg Disorders .......................................
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OTHER LESION OF FEMORAL NERVE
LESION OF SCIATIC NERVE
HEREDIT&IDIOPATH PERIPH NEUROPATHY
ACUTE INFECTIVE POLYNEURITIS
POLYNEUROPATHY COLL VASC DISEASE
POLYNEUROPATHY IN MALIGNANT DISEASE
POLYNEUROPATHY OTH DZ CLASS ELSW
ALCOHOLIC POLYNEUROPATHY
POLYNEUROPATHY DUE TO DRUGS
POLYNEUROPATHY DUE OTH TOXIC AGENTS
CRIT ILLNESS NEUROPATHY
INFLAM/TOX NEUROPATHY
UNSPEC INFLAM&TOXIC NEUROPATHY
MYASTHENIA GRAVIS W/O ACUTE
MYASTHENIA GRAVIS W/ACUTE
MYASTHENIC SYNDROMES DZ CLASS ELSW
TOXIC MYONEURAL DISORDERS
UNSPECIFIED MYONEURAL DISORDERS
CONGEN HEREDIT MUSCULAR DYSTROPHY
HEREDITARY PROGRESSIVE MUSC DYSTROPH
FAMILIAL PERIODIC PARALYSIS
TOXIC MYOPATHY
MYOPATHY ENDOCRINE DZ CLASS ELSW
SX INFLAM MYOPATHY DZ CLASS ELSW
OTHER MYOPATHIES
UNSPECIFIED MYOPATHY
MENIERE’S DISEASE
VERTIGO OF CENTRAL ORIGIN
LABYRINTHITIS
RHEUMATIC CHOREA
INJURY TO NERVE ROOTS&SPINAL PLEXUS
INJR OTH NRV TRNK NO SHLDR&PLV GIRD
INJR PERIPH NRV SHLDR GIRDL&UP LIMB
INJR TO SCIATIC NERVE
INJ TO FEMORAL NERVE
INJR TO MULTIPLE PELVIC AND LE NERVES
HEMIPLEGIA AND HEMIPARESIS
MONOPLEGIA OF LOWER LIMB
MONOPLEGIA OF UPPER LIMB
UNSPECIFIED MONOPLEGIA
SUBARACHNOID HEMORRHAGE
INTRACEREBRAL HEMORRHAGE
OTH&UNSPEC INTRACRANIAL HEMORRHAGE
OCCLUSION&STENOSIS BASILAR ART W INFARC
OCCLUSION&STENOSIS CAROTID ART W INFARC
OCCLUSION&STENOSIS VERTEBRAL ART W INFARC
OCCLUSION&STENOSIS MULT BILAT ART W INFARC
OCCLUSION&STENOSIS OTH PRECER ART W INFARC
CEREBRAL THROMBOSIS W INFARCTION
CEREBRAL EMBOLISM W INFARCTION
NEURO NEGLECT SYNDROME
ACUT BUT ILL-DEFINED CEREBRVASC DZ
LATE EFF CEREBROVASCULAR DZ
TRANSIENT CEREBRAL ISCHEMIA
MULTIPLE SCLEROSIS
OTH DEMYELINATING DZ CNTRL NERV SYS
PYOGEN ARTHRITIS-PELVIS
PYOGEN ARTHRITIS-L/LEG
PYOGEN ARTHRITIS-ANKLE
REITER ARTHRITIS-PELVIS
REITER ARTHRITIS-L/LEG
REITER ARTHRITIS-ANKLE
BEHCET ARTHRITIS-PELVIS
BEHCET ARTHRITIS-L/LEG
BEHCET ARTHRITIS-ANKLE
DYSENTER ARTHRIT-PELVIS
DYSENTER ARTHRIT-L/LEG
DYSENTER ARTHRIT-ANKLE
BACT ARTHRITIS-PELVIS
BACT ARTHRITIS-L/LEG
E:\FR\FM\04MYP2.SGM
04MYP2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
25377
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
cprice-sewell on DSK89S0YB1PROD with RULES
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ICD–9–CM
code**
Manifestation*
711.47
711.55
711.56
711.57
711.65
711.66
711.67
711.75
711.76
711.77
711.85
711.86
711.87
711.95
711.96
711.97
712.15
712.16
712.17
712.25
712.26
712.27
712.35
712.36
712.37
712.85
712.86
712.87
712.95
712.96
712.97
716.05
716.06
716.07
716.15
716.16
716.17
716.25
716.26
716.27
716.35
716.36
716.37
716.45
716.46
716.47
716.55
716.56
716.57
716.67
716.85
716.86
716.87
716.95
716.96
716.97
717
718.05
718.06
718.07
718.25
718.26
718.27
718.35
718.36
718.37
718.45
718.46
718.47
718.55
Diagnostic category
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
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M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
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Short description of ICD–9–CM code
BACT ARTHRITIS-ANKLE
VIRAL ARTHRITIS-PELVIS
VIRAL ARTHRITIS-L/LEG
VIRAL ARTHRITIS-ANKLE
MYCOTIC ARTHRITIS-PELVI
MYCOTIC ARTHRITIS-L/LEG
MYCOTIC ARTHRITIS-ANKLE
HELMINTH ARTHRIT-PELVIS
HELMINTH ARTHRIT-L/LEG
HELMINTH ARTHRIT-ANKLE
INF ARTHRITIS NEC-PELVI
INF ARTHRITIS NEC-L/LEG
INF ARTHRITIS NEC-ANKLE
INF ARTHRIT NOS-PELVIS
INF ARTHRIT NOS-L/LEG
INF ARTHRIT NOS-ANKLE
DICALC PHOS CRYST-PELVI
DICALC PHOS CRYST-L/LEG
DICALC PHOS CRYST-ANKLE
PYROPHOSPH CRYST-PELVIS
PYROPHOSPH CRYST-L/LEG
PYROPHOSPH CRYST-ANKLE
CHONDROCALCIN NOS-PELVI
CHONDROCALCIN NOS-L/LEG
CHONDROCALCIN NOS-ANKLE
CRYST ARTHROP NEC-PELVI
CRYST ARTHROP NEC-L/LEG
CRYST ARTHROP NEC-ANKLE
CRYST ARTHROP NOS-PELVI
CRYST ARTHROP NOS-L/LEG
CRYST ARTHROP NOS-ANKLE
KASCHIN-BECK DIS-PELVIS
KASCHIN-BECK DIS-L/LEG
KASCHIN-BECK DIS-ANKLE
TRAUM ARTHROPATHY-PELVIS
TRAUM ARTHROPATHY-L/LEG
TRAUM ARTHROPATHY-ANKLE
ALLERG ARTHRITIS-PELVIS
ALLERG ARTHRITIS-L/LEG
ALLERG ARTHRITIS-ANKLE
CLIMACT ARTHRITIS-PELVIS
CLIMACT ARTHRITIS-L/LEG
CLIMACT ARTHRITIS-ANKLE
TRANS ARTHROPATHY-PELVIS
TRANS ARTHROPATHY-L/LEG
TRANS ARTHROPATHY-ANKLE
POLYARTHRITIS NOS-PELVIS
POLYARTHRITIS NOS-L/LEG
POLYARTHRITIS NOS-ANKLE
MONOARTHRITIS NOS-ANKLE
ARTHROPATHY NEC-PELVIS
ARTHROPATHY NEC-L/LEG
ARTHROPATHY NEC-ANKLE
ARTHROPATHY NOS-PELVIS
ARTHROPATHY NOS-L/LEG
ARTHROPATHY NOS-ANKLE
INTERNAL DERANGEMENT OF KNEE
ART CARTIL DISORDER PELVIS AND THIGH
ART CARTIL DISORDER LOWER LEG
ART CARTIL DIS ANKLE FOOT
PATHOLOGIC DISLOCATION PELVIS AND THIGH
PATHOLOGIC DISLOCATION LOWER LEG
PATHOLOGIC DISLOCATION ANKLE FOOT
RECURRENT DISLOCATION PELVIS AND THIGH
RECURRENT DISLOCATION LOW LEG
RECURRENT DISLOCATION ANKLE FOOT
CONTRACTURE PELVIS AND THIGH
CONTRACTURE LOWER LEG
CONTRACTURE OF JOINT ANKLE FOOT
ANKYLOSIS OF PELVIS AND THIGH
E:\FR\FM\04MYP2.SGM
04MYP2
25378
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
ICD–9–CM
code**
Diagnostic category
Manifestation*
718.56
718.57
718.85
718.86
718.87
719.15
719.16
719.17
719.25
719.26
719.27
719.35
719.36
719.37
727.65
727.66
727.67
727.68
730.05
730.06
730.07
730.15
730.16
730.17
730.25
730.26
730.27
730.35
730.36
730.37
730.75
730.76
730.77
730.85
730.86
730.87
730.95
730.96
730.97
733.14
733.15
733.16
733.42
733.43
808
820
821
822
823
824
825
827
828
835
836
897
928
711.01
711.02
711.03
711.04
711.08
711.09
711.10
711.11
711.12
711.13
711.14
711.18
711.19
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M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
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M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
cprice-sewell on DSK89S0YB1PROD with RULES
Ortho 2—Other Orthopedic disorders ..................
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Short description of ICD–9–CM code
ANKYLOSIS OF LOWER LEG
ANKYLOSIS OF JOINT ANKLE FOOT
OTHER DERANGEMENT OF PELVIS AND THIGH
OTHER DERANGEMENT OF JOINT OF LOWER LEG
OTH DERANGMENT JT NEC ANKLE FOOT
HEMARTHROSIS PELVIS AND THIGH
HEMARTHROSIS LOWER LEG
HEMARTHROSIS ANKLE AND FOOT
VILLONODULAR SYNOVITIS PELVIS AND THIGH
VILLONODULAR SYNOVITIS LOWER LEG
VILLONODULAR SYNOVITIS ANKLE AND FOOT
PALANDROMIC RHEUMATISM PELVIS AND THIGH
PALANDROMIC RHEUMATISM LOWER LEG
PALANDROMIC RHEUMATISM ANKLE AND FOOT
RUPTURE OF TENDON QUADRACEPS
RUPTURE OF TENDON PATELLAR
RUPTURE OF TENDON ACHILLES
RUPTURE OTHER TENDONS FOOT AND ANKLE
AC OSTEOMYELITIS-PELVIS
AC OSTEOMYELITIS-L/LEG
AC OSTEOMYELITIS-ANKLE
CHR OSTEOMYELIT-PELVIS
CHR OSTEOMYELIT-L/LEG
CHR OSTEOMYELIT-ANKLE
OSTEOMYELITIS NOS-PELVI
OSTEOMYELITIS NOS-L/LEG
OSTEOMYELITIS NOS-ANKLE
PERIOSTITIS-PELVIS
PERIOSTITIS-L/LEG
PERIOSTITIS-ANKLE
POLIO OSTEOPATHY-PELVIS
POLIO OSTEOPATHY-L/LEG
POLIO OSTEOPATHY-ANKLE
BONE INFECT NEC-PELVIS
BONE INFECT NEC-L/LEG
BONE INFECT NEC-ANKLE
BONE INFECT NOS-PELVIS
BONE INFECT NOS-L/LEG
BONE INFECT NOS-ANKLE
PATHOLOGIC FRACTURE OF NECK OF FEMUR
PATHOLOGIC FRACTURE OF FEMUR
PATHOLOGIC FRACTURE OF TIBIA OR FIBULA
ASEPTIC NECROSIS OF HEAD AND NECK OF FEMUR
ASEPTIC NECROSIS OF MEDIAL FEMORAL CONDYLE
FRACTURE OF PELVIS
FRACTURE OF NECK OF FEMUR
FRACTURE OTHER&UNSPEC PARTS FEMUR
FRACTURE OF PATELLA
FRACTURE OF TIBIA AND FIBULA
FRACTURE OF ANKLE
FRACTURE 1/MORE TARSAL&MT BNS
OTH MX&ILL-DEFINED FX LOWER LIMB
MX FX LEGS-LEG W/ARM-LEGS W/RIBS
DISLOCATION OF HIP
DISLOCATION OF KNEE
TRAUMATIC AMPUTATION OF LEG
CRUSHING INJURY OF LOWER LIMB
PYOGEN ARTHRITIS-SHLDER
PYOGEN ARTHRITIS-UP/ARM
PYOGEN ARTHRITIS-FOREAR
PYOGEN ARTHRITIS-HAND
PYOGEN ARTHRITIS NEC
PYOGEN ARTHRITIS-MULT
REITER ARTHRITIS-UNSPEC
REITER ARTHRITIS-SHLDER
REITER ARTHRITIS-UP/ARM
REITER ARTHRITIS-FOREAR
REITER ARTHRITIS-HAND
REITER ARTHRITIS NEC
REITER ARTHRITIS-MULT
E:\FR\FM\04MYP2.SGM
04MYP2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
25379
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
cprice-sewell on DSK89S0YB1PROD with RULES
VerDate Nov<24>2008
14:30 Apr 20, 2010
Jkt 220001
ICD–9–CM
code**
Manifestation*
711.20
711.21
711.22
711.23
711.24
711.28
711.29
711.30
711.31
711.32
711.33
711.34
711.38
711.39
711.40
711.41
711.42
711.43
711.44
711.48
711.49
711.50
711.51
711.52
711.53
711.54
711.58
711.59
711.60
711.61
711.62
711.63
711.64
711.68
711.69
711.70
711.71
711.72
711.73
711.74
711.78
711.79
711.80
711.81
711.82
711.83
711.84
711.88
711.89
711.90
711.91
711.92
711.93
711.94
711.98
711.99
712.10
712.11
712.12
712.13
712.14
712.18
712.19
712.20
712.21
712.22
712.23
712.24
712.28
712.29
Diagnostic category
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
PO 00000
Frm 00025
Fmt 4701
Sfmt 4702
Short description of ICD–9–CM code
BEHCET ARTHRITIS-UNSPEC
BEHCET ARTHRITIS-SHLDER
BEHCET ARTHRITIS-UP/ARM
BEHCET ARTHRITIS-FOREAR
BEHCET ARTHRITIS-HAND
BEHCET ARTHRITIS NEC
BEHCET ARTHRITIS-MULT
DYSENTER ARTHRIT-UNSPEC
DYSENTER ARTHRIT-SHLDER
DYSENTER ARTHRIT-UP/ARM
DYSENTER ARTHRIT-FOREAR
DYSENTER ARTHRIT-HAND
DYSENTER ARTHRIT NEC
DYSENTER ARTHRIT-MULT
BACT ARTHRITIS-UNSPEC
BACT ARTHRITIS-SHLDER
BACT ARTHRITIS-UP/ARM
BACT ARTHRITIS-FOREARM
BACT ARTHRITIS-HAND
BACT ARTHRITIS NEC
BACT ARTHRITIS-MULT
VIRAL ARTHRITIS-UNSPEC
VIRAL ARTHRITIS-SHLDER
VIRAL ARTHRITIS-UP/ARM
VIRAL ARTHRITIS-FOREARM
VIRAL ARTHRITIS-HAND
VIRAL ARTHRITIS NEC
VIRAL ARTHRITIS-MULT
MYCOTIC ARTHRITIS-UNSPE
MYCOTIC ARTHRITIS-SHLDE
MYCOTIC ARTHRITIS-UP/AR
MYCOTIC ARTHRIT-FOREARM
MYCOTIC ARTHRITIS-HAND
MYCOTIC ARTHRITIS NEC
MYCOTIC ARTHRITIS-MULT
HELMINTH ARTHRIT-UNSPEC
HELMINTH ARTHRIT-SHLDER
HELMINTH ARTHRIT-UP/ARM
HELMINTH ARTHRIT-FOREAR
HELMINTH ARTHRIT-HAND
HELMINTH ARTHRIT NEC
HELMINTH ARTHRIT-MULT
INF ARTHRITIS NEC-UNSPE
INF ARTHRITIS NEC-SHLDE
INF ARTHRITIS NEC-UP/AR
INF ARTHRIT NEC-FOREARM
INF ARTHRITIS NEC-HAND
INF ARTHRIT NEC-OTH SIT
INF ARTHRITIS NEC-MULT
INF ARTHRITIS NOS-UNSPE
INF ARTHRITIS NOS-SHLDE
INF ARTHRITIS NOS-UP/AR
INF ARTHRIT NOS-FOREARM
INF ARTHRIT NOS-HAND
INF ARTHRIT NOS-OTH SIT
INF ARTHRITIS NOS-MULT
DICALC PHOS CRYST-UNSPE
DICALC PHOS CRYST-SHLDE
DICALC PHOS CRYST-UP/AR
DICALC PHOS CRYS-FOREAR
DICALC PHOS CRYST-HAND
DICALC PHOS CRY-SITE NE
DICALC PHOS CRYST-MULT
PYROPHOSPH CRYST-UNSPEC
PYROPHOSPH CRYST-SHLDER
PYROPHOSPH CRYST-UP/ARM
PYROPHOSPH CRYST-FOREAR
PYROPHOSPH CRYST-HAND
PYROPHOS CRYST-SITE NEC
PYROPHOS CRYST-MULT
E:\FR\FM\04MYP2.SGM
04MYP2
25380
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
.........................
.........................
.........................
.........................
715.36
cprice-sewell on DSK89S0YB1PROD with RULES
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
M .....................
.........................
.........................
715.35
.........................
715.95
715.96
716.00
716.01
716.02
716.03
716.04
716.08
716.09
716.10
716.11
716.12
716.13
716.14
716.18
716.19
716.20
716.21
716.22
716.23
716.24
716.28
716.29
716.30
716.31
716.32
716.33
716.34
Jkt 220001
Short description of ICD–9–CM code
715.26
14:30 Apr 20, 2010
Manifestation*
715.16
715.25
VerDate Nov<24>2008
ICD–9–CM
code**
712.30
712.31
712.32
712.33
712.34
712.38
712.39
712.80
712.81
712.82
712.83
712.84
712.88
712.89
712.90
712.91
712.92
712.93
712.94
712.98
712.99
713.0
713.1
713.2
713.3
713.4
713.5
713.6
713.7
713.8
714
715.15
Diagnostic category
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
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.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
CHONDROCALCIN NOS-UNSPE
CHONDROCALCIN NOS-SHLDE
CHONDROCALCIN NOS-UP/AR
CHONDROCALC NOS-FOREARM
CHONDROCALCIN NOS-HAND
CHONDROCALC NOS-OTH SIT
CHONDROCALCIN NOS-MULT
CRYST ARTHROP NEC-UNSPE
CRYST ARTHROP NEC-SHLDE
CRYST ARTHROP NEC-UP/AR
CRYS ARTHROP NEC-FOREAR
CRYST ARTHROP NEC-HAND
CRY ARTHROP NEC-OTH SIT
CRYST ARTHROP NEC-MULT
CRYST ARTHROP NOS-UNSPE
CRYST ARTHROP NOS-SHLDR
CRYST ARTHROP NOS-UP/AR
CRYS ARTHROP NOS-FOREAR
CRYST ARTHROP NOS-HAND
CRY ARTHROP NOS-OTH SIT
CRYST ARTHROP NOS-MULT
ARTHROP W ENDOCR/MET DI
ARTHROP W NONINF GI DIS
ARTHROPATH W HEMATOL DI
ARTHROPATHY W SKIN DIS
ARTHROPATHY W RESP DIS
ARTHROPATHY W NERVE DIS
ARTHROP W HYPERSEN REAC
ARTHROP W SYSTEM DIS NE
ARTHROP W OTH DIS NEC
RA&OTH INFLAM POLYARTHROPATHIES
OSTEOARTHROSIS, LOCALIZED, PRIMARY, PELVIS AND
THIGH
OSTEOARTHROSIS, LOCALIZED, PRIMARY, LOWER LEG
OSTEOARTHROSIS, LOCALIZED, SECONDARY, PELVIS
AND THIGH
OSTEOARTHROSIS, LOCALIZED, SECONDARY, LOWER
LEG
OSTEOARTHROSIS, LOCALIZED, NOT SPEC PRIMARY OR
SECONDARY, PELVIS AND THIGH
OSTEOARTHROSIS, LOCALIZED, NOT SPEC PRIMARY OR
SECONDARY, LOWER LEG
OSTEOARTHROSIS, UNSPECIFIED, PELVIS AND THIGH
OSTEOARTHROSIS, UNSPECIFIED, LOWER LEG
KASCHIN-BECK DIS-UNSPEC
KASCHIN-BECK DIS-SHLDER
KASCHIN-BECK DIS-UP/ARM
KASCHIN-BECK DIS-FOREARM
KASCHIN-BECK DIS-HAND
KASCHIN-BECK DIS NEC
KASCHIN-BECK DIS-MULT
TRAUM ARTHROPATHY-UNSPEC
TRAUM ARTHROPATHY-SHLDER
TRAUM ARTHROPATHY-UP/ARM
TRAUM ARTHROPATH-FOREARM
TRAUM ARTHROPATHY-HAND
TRAUM ARTHROPATHY NEC
TRAUM ARTHROPATHY-MULT
ALLERG ARTHRITIS-UNSPEC
ALLERG ARTHRITIS-SHLDER
ALLERG ARTHRITIS-UP/ARM
ALLERG ARTHRITIS-FOREARM
ALLERG ARTHRITIS-HAND
ALLERG ARTHRITIS NEC
ALLERG ARTHRITIS-MULT
CLIMACT ARTHRITIS-UNSPEC
CLIMACT ARTHRITIS-SHLDER
CLIMACT ARTHRITIS-UP/ARM
CLIMACT ARTHRIT-FOREARM
CLIMACT ARTHRITIS-HAND
PO 00000
Frm 00026
Fmt 4701
Sfmt 4702
E:\FR\FM\04MYP2.SGM
04MYP2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
25381
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
cprice-sewell on DSK89S0YB1PROD with RULES
VerDate Nov<24>2008
14:30 Apr 20, 2010
Jkt 220001
ICD–9–CM
code**
Manifestation*
716.38
716.39
716.40
716.41
716.42
716.43
716.44
716.48
716.49
716.50
716.51
716.52
716.53
716.54
716.58
716.59
716.60
716.61
716.62
716.63
716.64
716.65
716.66
716.68
716.80
716.81
716.82
716.83
716.84
716.88
716.89
716.90
716.91
716.92
716.93
716.94
716.98
716.99
718.01
718.02
718.03
718.04
718.08
718.09
718.1
718.20
718.21
718.22
718.23
718.24
718.28
718.29
718.30
718.31
718.32
718.33
718.34
718.38
718.39
718.40
718.41
718.42
718.43
718.44
718.48
718.49
718.50
718.51
718.52
718.53
Diagnostic category
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
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PO 00000
Frm 00027
Fmt 4701
Sfmt 4702
Short description of ICD–9–CM code
CLIMACT ARTHRITIS NEC
CLIMACT ARTHRITIS-MULT
TRANS ARTHROPATHY-UNSPEC
TRANS ARTHROPATHY-SHLDER
TRANS ARTHROPATHY-UP/ARM
TRANS ARTHROPATH-FOREARM
TRANS ARTHROPATHY-HAND
TRANS ARTHROPATHY NEC
TRANS ARTHROPATHY-MULT
POLYARTHRITIS NOS-UNSPEC
POLYARTHRITIS NOS-SHLDER
POLYARTHRITIS NOS-UP/ARM
POLYARTHRIT NOS-FOREARM
POLYARTHRITIS NOS-HAND
POLYARTHRIT NOS-OTH SITE
POLYARTHRITIS NOS-MULT
MONOARTHRITIS NOS-UNSPEC
MONOARTHRITIS NOS-SHLDER
MONOARTHRITIS NOS-UP/ARM
MONOARTHRIT NOS-FOREARM
MONOARTHRITIS NOS-HAND
UNSPECIFIED MONOARTHRITIS, PELVIS AND THIGH
UNSPECIFIED MONOARTHRITIS, LOWER LEG
MONOARTHRIT NOS-OTH SITE
ARTHROPATHY NEC-UNSPEC
ARTHROPATHY NEC-SHLDER
ARTHROPATHY NEC-UP/ARM
ARTHROPATHY NEC-FOREARM
ARTHROPATHY NEC-HAND
ARTHROPATHY NEC-OTH SITE
ARTHROPATHY NEC-MULT
ARTHROPATHY NOS-UNSPEC
ARTHROPATHY NOS-SHLDER
ARTHROPATHY NOS-UP/ARM
ARTHROPATHY NOS-FOREARM
ARTHROPATHY NOS-HAND
ARTHROPATHY NOS-OTH SITE
ARTHROPATHY NOS-MULT
ART CARTIL DISORDER SHOULDER
ART CARTIL DIS UPPER ARM
ART CARTIL DIS FOREARM
ART CARTIL DIS HAND
ART CART DIS OTH SITES
ART CART DIS MULT
LOOSE BODY IN JT
PATHOLOGIC DISLOCATION UNSPEC SITE
PATHOLOGIC DISLOCATION SHOULDER
PATHOLOGIC DISLOCATION UPPER ARM
PATHOLOGIC DISLOCATION FOREARM
PATHOLOGIC DISLOCATION HAND
PATHOLOGIC DISLOCATION OTH LOC
PATHOLOGIC DISLOCATION MULT LOC
RECURRENT DISLOCATION UNSPEC SITE
RECURRENT DISLOCATION SHOULDER
RECURRENT DISLOCATION UPPER ARM
RECURRENT DISLOCATION FOREARM
RECURRENT DISLOCATION HAND
RECURRENT DISLOCATION OTH LOC
RECURRENT DISLOCATION MULT LOC
CONTRACTURE OF JOINT UNSPEC SITE
CONTRACTURE SHOULDER
CONTRACTURE OF JOINT UPPER ARM
CONTRACTURE OF JOINT FOREARM
CONTRACTURE OF JOINT HAND
CONTRACTURE OF JOINT OTH LOC
CONTRACTURE OF JOINT MULT LOC
ANKYLOSIS OF JOINT UNSPEC SITE
ANKYLOSIS OF SHOULDER
ANKYLOSIS OF JOINT UPPER ARM
ANKYLOSIS OF JOINT FOREARM
E:\FR\FM\04MYP2.SGM
04MYP2
25382
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
.........................
.........................
.........................
.........................
722.6
cprice-sewell on DSK89S0YB1PROD with RULES
.........................
.........................
.........................
.........................
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M .....................
M .....................
.........................
.........................
.........................
.........................
722.4
722.5
.........................
722.7
722.8
722.9
723.0
723.1
723.2
723.3
723.4
723.5
723.6
723.7
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
.........................
723.8
723.9
724
725
726.0
Jkt 220001
Short description of ICD–9–CM code
722.2
14:30 Apr 20, 2010
Manifestation*
722.1
VerDate Nov<24>2008
ICD–9–CM
code**
718.54
718.58
718.59
718.60
718.7
718.80
718.81
718.82
718.83
718.84
718.88
718.89
718.9
719.1
719.11
719.12
719.13
719.14
719.18
719.19
719.2
719.21
719.22
719.23
719.24
719.28
719.29
719.3
719.31
719.32
719.33
719.34
719.38
719.39
720.0
720.1
720.2
720.8
720.81
720.89
720.9
721
722.0
Diagnostic category
.........................
.........................
.........................
.........................
.........................
ANKYLOSIS OF JOINT HAND
ANKYLOSIS OF JOINT OTH LOC
ANKYLOSIS OF JOINT MULT LOC
UNSPED ’INTRAPELVIC PROTRUSION ACETAB
DEV DISLOC JOINT
OTH DERANGMENT JT NEC UNSPEC SITE
OTHER DERANGEMENT OF SHOULDER
OTH DERANGMENT JT NEC UPPER ARM
OTH DERANGMENT JT NEC FOREARM
OTH DERANGMENT JT NEC HAND
OTH DERANGMENT JT NEC OTH LOC
OTH DERANGMENT JT NEC MULT LOC
UNSPEC DERANGMENT JT
HEMARTHROSIS UNSPECIFIED SITE
HEMARTHROSIS SHOULDER
HEMARTHROSIS UPPER ARM
HEMARTHROSIS FOREARM
HEMARTHROSIS HAND
HEMARTHROSIS OTHER SPECIFIED
HEMARTHROSIS MULTIPLE SITES
VILLONODULAR SYNOVITIS UNSPECIFIED SITE
VILLONODULAR SYNOVITIS SHOULDER
VILLONODULAR SYNOVITIS UPPER ARM
VILLONODULAR SYNOVITIS FOREARM
VILLONODULAR SYNOVITIS HAND
VILLONODULAR SYNOVITIS OTHER SITES
VILLONODULAR SYNOVITIS MULTIPLE SITES
PALANDROMIC RHEUMATISM UNSPECIFIED SITE
PALANDROMIC RHEUMATISM SHOULDER
PALANDROMIC RHEUMATISM UPPER ARM
PALANDROMIC RHEUMATISM FOREARM
PALANDROMIC RHEUMATISM HAND
PALANDROMIC RHEUMATISM OTHER SITES
PALANDROMIC RHEUMATISM MULTIPLE SITES
ANKYLOSING SPONDYLITIS
SPINAL ENTHESOPATHY
SACROILIITIS NEC
OTHER INFLAMMATORY SPONDYLOPATHIES
SPONDYLOPATHY IN OTH DI
OTHER INFLAMMATORY SPONDYLOPATHIES
UNSPEC INFLAMMATORY SPONDYLOPATHY
SPONDYLOSIS AND ALLIED DISORDERS
DISPLACEMENT OF CERVICAL INTERVERTEBRAL DISC
WITHOUT MYELOPATHY
DISPLACEMENT
OF
THORACIC
OR
LUMBAR
INTERVERTEBRAL DISC WITHOUT MYELOPATHY
DISPLACEMENT OF INTERVERTEBRAL DISC, SITE UNSPECIFIED, WITHOUT MYELOPATHY
DEGENERATION OF CERVICAL INTERVERTEBRAL DISC
DEGENERATION
OF
THORACIC
OR
LUMBAR
INTERVERTEBRAL DISC
DEGENERATION OF INTERVERTEBRAL DISC, SITE UNSPECIFIED
INTERVERTEBRAL DISC DISORDER WITH MYELOPATHY
POSTLAMINECTOMY SYNDROME
OTHER AND UNSPECIFIED DISC DISORDER
SPINAL STENOSIS OF CERVICAL REGION
CERVICALGIA
CERVICOCRANIAL SYNDROME
CERVICOBRACHIAL SYNDROME
BRACHIA NEURITIS OR RADICULITIS
TORTICOLLIS, UNSPECIFIED
PANNICULITIS SPECIFIED AS AFFECTING NECK
OSSIFICATION OF POSTERIOR LONGITUDINAL LIGAMENT IN CERVICAL REGION
OTHER SYNDROMES AFFECTING CERVICAL REGION
UNSPEC MUSCULOSKEL SX OF NECK
OTHER&UNSPECIFIED DISORDERS OF BACK
POLYMYALGIA RHEUMATICA
ADHESIVE CAPSULITIS
PO 00000
Frm 00028
Fmt 4701
Sfmt 4702
E:\FR\FM\04MYP2.SGM
04MYP2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
25383
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
cprice-sewell on DSK89S0YB1PROD with RULES
VerDate Nov<24>2008
14:30 Apr 20, 2010
Jkt 220001
ICD–9–CM
code**
Manifestation*
726.10
726.11
726.12
726.19
727.61
728.0
728.10
728.11
728.12
728.13
728.19
728.2
728.3
728.4
728.5
728.6
730.00
730.01
730.02
730.03
730.04
730.08
730.09
730.10
730.11
730.12
730.13
730.14
730.18
730.19
730.20
730.21
730.22
730.23
730.24
730.28
730.29
730.30
730.31
730.32
730.33
730.34
730.38
730.39
730.70
730.71
730.72
730.73
730.74
730.78
730.79
730.80
730.81
730.82
730.83
730.84
730.88
730.89
730.90
730.91
730.92
730.93
730.94
730.98
730.99
731.0
731.1
731.2
731.8
732
Diagnostic category
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Short description of ICD–9–CM code
DISORDERS OF BURSAE AND TENDONS
CALCIFYING TENDINITIS
BICIPITAL TENOSYNOVITIS
ROTATOR CUFF SYNDROME OTHER
COMPLETE RUPTURE OF ROTATOR CUFF
INFECTIVE MYOSITIS
CALCIFICATION AND OSSIFICATION, UNSPECIFIED
PROGRESSIVE MYOSITIS OSSIFICANS
TRAUMATIC MYOSITIS OSSIFICATIONS
POST OP HETEROTOPIC CALCIFICATION
OTHER MUSCULAR CALCIFICATION AND OSSIFICATION
MUSCULAR WASTING AND DISUSE ATROPHY
OTHER SPECIFIC MUSCLE DISORDERS
LAXITY OF LIGAMENT
HYPERMOBILITY SYNDROME
CONTRACTURE OF PALMAR FASCIA
AC OSTEOMYELITIS-UNSPEC
AC OSTEOMYELITIS-SHLDER
AC OSTEOMYELITIS-UP/ARM
AC OSTEOMYELITIS-FOREAR
AC OSTEOMYELITIS-HAND
AC OSTEOMYELITIS NEC
AC OSTEOMYELITIS-MULT
CHR OSTEOMYELITIS-UNSP
CHR OSTEOMYELIT-SHLDER
CHR OSTEOMYELIT-UP/ARM
CHR OSTEOMYELIT-FOREARM
CHR OSTEOMYELIT-HAND
CHR OSTEOMYELIT NEC
CHR OSTEOMYELIT-MULT
OSTEOMYELITIS NOS-UNSPE
OSTEOMYELITIS NOS-SHLDE
OSTEOMYELITIS NOS-UP/AR
OSTEOMYELIT NOS-FOREARM
OSTEOMYELITIS NOS-HAND
OSTEOMYELIT NOS-OTH SIT
OSTEOMYELITIS NOS-MULT
PERIOSTITIS-UNSPEC
PERIOSTITIS-SHLDER
PERIOSTITIS-UP/ARM
PERIOSTITIS-FOREARM
PERIOSTITIS-HAND
PERIOSTITIS NEC
PERIOSTITIS-MULT
POLIO OSTEOPATHY-UNSPEC
POLIO OSTEOPATHY-SHLDER
POLIO OSTEOPATHY-UP/ARM
POLIO OSTEOPATHY-FOREAR
POLIO OSTEOPATHY-HAND
POLIO OSTEOPATHY NEC
POLIO OSTEOPATHY-MULT
BONE INFECT NEC-UNSPEC
BONE INFECT NEC-SHLDER
BONE INFECT NEC-UP/ARM
BONE INFECT NEC-FOREARM
BONE INFECT NEC-HAND
BONE INFECT NEC-OTH SIT
BONE INFECT NEC-MULT
BONE INFEC NOS-UNSP SIT
BONE INFECT NOS-SHLDER
BONE INFECT NOS-UP/ARM
BONE INFECT NOS-FOREARM
BONE INFECT NOS-HAND
BONE INFECT NOS-OTH SIT
BONE INFECT NOS-MULT
OSTEITIS DEFORMANS W/O BN TUMR
OSTEITIS DEFORMANS DZ CLASS ELSW
HYPERTROPH PULM OSTEOARTHROPATHY
OTH BONE INVOLVEMENT DZ CLASS EL
OSTEOCHONDROPATHIES
E:\FR\FM\04MYP2.SGM
04MYP2
25384
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
ICD–9–CM
code**
Diagnostic category
Manifestation*
733.10
733.11
733.12
733.13
733.19
800
801
802
803
804
805
807
809
810
811
812
813
814
815
816
817
818
819
831
832
833
837
838
846
847
295
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PATHOLOGIC FRACTURE UNSPEC
PATHOLOGIC FRACTURE HUMERUS
PATHOLOGIC FRACTURE DISTAL RADIUS ULNA
PATHOLOGIC FRACTURE OF VERTEBRAE
PATHOLOGIC FRACTURE OTH SPEC SITE
FRACTURE OF VAULT OF SKULL
FRACTURE OF BASE OF SKULL
FRACTURE OF FACE BONES
OTHER&UNQUALIFIED SKULL FRACTURES
MX FX INVLV SKULL/FACE W/OTH BNS
FX VERT COLUMN W/O SP CRD INJR
FRACTURE RIB STERNUM LARYNX&TRACHEA
ILL-DEFINED FRACTURES BONES TRUNK
FRACTURE OF CLAVICLE
FRACTURE OF SCAPULA
FRACTURE OF HUMERUS
FRACTURE OF RADIUS AND ULNA
FRACTURE OF CARPAL BONE
FRACTURE OF METACARPAL BONE
FRACTURE ONE OR MORE PHALANGES HAND
MULTIPLE FRACTURES OF HAND BONES
ILL-DEFINED FRACTURES OF UPPER LIMB
MX FX UP LIMBS&LIMBS W/RIB&STERNUM
DISLOCATION OF SHOULDER
DISLOCATION OF ELBOW
DISLOCATION OF WRIST
DISLOCATION OF ANKLE
DISLOCATION OF FOOT
SPRAINS&STRAINS SACROILIAC REGION
SPRAINS&STRAINS OTH&UNS PART BACK
SCHIZOPHRENIA
296
297
298
311
331.0
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AFFECTIVE PSYCHOSES
DELUSIONAL DIS
OTH PSYCHOSES
DEPRESSIVE DISORDER NEC
ALZHEIMER’S DISEASE
331.11
331.19
331.2
331.3
331.4
331.7
331.81
331.82
331.89
331.9
290.0
290.10
290.11
290.12
290.13
290.20
290.21
290.3
290.40
290.41
290.42
290.43
291.1
291.2
292.8
294.0
294.1
294.8
294.9
491
492
493.2
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M .....................
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M .....................
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M .....................
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M .....................
M .....................
M .....................
M .....................
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PICK’S DISEASE
OTH FRONTO-TEMPORAL DEMENTIA
SENILE DEGENERAT BRAIN
COMMUNICAT HYDROCEPHALUS
OBSTRUCTIV HYDROCEPHALUS
CEREB DEGEN IN OTH DIS
REYE’S SYNDROME
DEMENTIA WITH LEWY BODIES
CEREB DEGENERATION NEC
CEREB DEGENERATION NOS
SENILE DEMENTIA, UNCOMPLICATED
PRESENILE DEMENTIA UNCOMP
PRESENILE DEMENTIA WITH DELIRIUM
PRESENILE DEMENTIA WITH DELUSIONAL FEATURES
PRESENILE DEMENTIA WITH DEPRESSIVE FEATURES
SENILE DEMENTIA WITH DELUSIONAL FEATURES
SENILE DEMENTIA WITH DEPRESSIVE FEATURES
SENILE DEMENTIA WITH DELIRIUM
VASCULAR DEMENTIA, UNCOMPLICATED
VASCULAR DEMENTIA, WITH DELIRIUM
VASCULAR DEMENTIA, WITH DELUSIONS
VASCULAR DEMENTIA, WITH DEPRESSED MOOD
ALCOHOL PSYCHOSIS
ALCOHOL DEMENTIA
DRUG PSYCHOSES
AMNESTIC DISORD OTH DIS
DEMENTIA
MENTAL DISOR NEC OTH DIS
MENTAL DISOR NOS OTH DIS
CHRONIC BRONCHIT
EMPHYSEMA
ASTHMA
Psych 1—Affective and other psychoses, depression.
cprice-sewell on DSK89S0YB1PROD with RULES
Psych 2—Degenerative and other organic psychiatric disorders.
Pulmonary disorders ............................................
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04MYP2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
25385
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
ICD–9–CM
code**
Manifestation*
496
870
.........................
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CHRONIC AIRWAY OBSTRUCTION NEC
OPEN WOUND OF OCULAR ADNEXA
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
890
891
892
893
894
895
896
941
942
943
944
945
946
948
949
927
951
955.0
955.1
955.2
955.3
955.4
955.5
955.6
955.7
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955.9
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956.2
956.3
956.4
956.5
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956.9
Diagnostic category
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998.1
998.2
998.3
998.4
998.5
998.6
998.83
440.23
707.1
707.8
707.9
681
683
684
685
686
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OPEN WOUND OF EAR
OTHER OPEN WOUND OF HEAD
OPEN WOUND OF NECK
OPEN WOUND OF CHEST
OPEN WOUND OF BACK
OPEN WOUND OF BUTTOCK
OPEN WND GNT ORGN INCL TRAUMAT AMP
OPEN WOUND OTH&UNSPEC SITE NO LIMBS
OPEN WOUND OF SHOULDER&UPPER ARM
OPEN WOUND OF ELBOW FOREARM&WRIST
OPEN WOUND HAND EXCEPT FINGER ALONE
OPEN WOUND OF FINGER
MX&UNSPEC OPEN WOUND UPPER LIMB
TRAUMATIC AMPUTATION OF THUMB
TRAUMATIC AMPUTATION OTHER FINGER
TRAUMATIC AMPUTATION OF ARM&HAND
OPEN WOUND OF HIP AND THIGH
OPEN WOUND OF KNEE, LEG , AND ANKLE
OPEN WOUND OF FOOT EXCEPT TOE ALONE
OPEN WOUND OF TOE
MX&UNSPEC OPEN WOUND LOWER LIMB
TRAUMATIC AMPUTATION OF TOE
TRAUMATIC AMPUTATION OF FOOT
BURN OF FACE, HEAD, AND NECK
BURN OF TRUNK
BURN UPPER LIMB EXCEPT WRIST&HAND
BURN OF WRIST AND HAND
BURN OF LOWER LIMB
BURNS OF MULTIPLE SPECIFIED SITES
BURN CLASS ACCORD-BODY SURF INVOLVD
BURN, UNSPECIFIED SITE
CRUSHING INJURY OF UPPER LIMB
INJURY TO OTHER CRANIAL NERVE
INJURY TO AXILLARY NERVE
INJURY TO MEDIAN NERVE
INJURY TO ULNAR NERVE
INJURY TO RADIAL NERVE
INJURY TO MUSCULOCUTANEOUS NERVE
INJURY TO CUTANEOUS SENSORY NERVE, UPPER LIMB
INJURY TO DIGITAL NERVE
INJURY TO OTHER SPECIFIED NERVE(S) SHOULDER
GIRDLE AND UPPER LIMB
INJURY TO UNSPEC NERVE(S) SHOULDER GIRDLE AND
UPPER LIMB
INJURY TO POSTERIOR TIBIAL NERVE
INJURY TO PERONEAL NERVE
INJURY TO CUTANEOUS SENSORY NERVE, LOWER LIMB
INJURY TO OTHER SPECIFIED NERVE(S) OF PELVIC GIRDLE AND LOWER LIMB
INJURY TO UNSPECIFIED NERVE OF PELVIC GIRDLE
AND LOWER LIMB
HEMORR/HEMAT/SEROMA COMP PROC NEC
ACC PUNCT/LACRATION DURING PROC NEC
DISRUPTION OF OPERATION WOUND NEC
FB ACC LEFT DURING PROC NEC
POSTOPERATIVE INFECTION NEC
PERSISTENT POSTOPERATIVE FIST NEC
NON-HEALING SURGICAL WOUND NEC
ATHEROSCLER-ART EXTREM W/ULCERATION
ULCER LOWER LIMBS EXCEPT DECUBITUS
CHRONIC ULCER OTHER SPECIFIED SITE
CHRONIC ULCER OF UNSPECIFIED SITE
CELLULITIS&ABSCESS OF FINGER&TOE
ACUTE LYMPHADENITIS
IMPETIGO
PILONIDAL CYST
OTH LOCAL INF SKIN&SUBCUT TISSUE
cprice-sewell on DSK89S0YB1PROD with RULES
Skin 1—Traumatic wounds, burns and post-operative complications.
Skin 2—Ulcers and other skin conditions ............
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E:\FR\FM\04MYP2.SGM
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25386
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT
VARIABLES—Continued
ICD–9–CM
code**
Manifestation*
440.24
785.4
565
566
682
680
Diagnostic category
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M .....................
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Short description of ICD–9–CM code
ATHERSCLER-ART EXTREM W/GANGRENE
GANGRENE
ANAL FISSURE AND FISTULA
ABSCESS OF ANAL AND RECTAL REGIONS
OTHER CELLULITIS AND ABSCESS
CARBUNCLE AND FURUNCLE
*We are aware that some of these codes or code categories involve manifestation codes. The ICD–9–CM Official Guidelines for Coding and
Reporting requires that the underlying disease or condition code be sequenced first followed by the manifestation code. The underlying disease
codes associated with the manifestation codes are not listed in Table 2b, and these underlying codes were not specified in the analysis process.
However, when reporting certain conditions that have both an underlying etiology and body system manifestations due to the underlying etiology,
the appropriate sequencing must be followed according to the ICD–9–CM Coding Guidelines. Equally important, the reported etiology must be
valid for the manifestation specified.
**Note: ‘‘ICD–9–CM Official Guidelines for Coding and Reporting’’ dictate that a three-digit code is to be used only if it is not further subdivided. Where fourth-digit subcategories and/or fifth-digit subclassifications are provided, they must be assigned. A code is invalid if it has not
been coded to the full number of digits required for that code. Codes with three digits are included in ICD–9–CM as the heading of a category of
codes that may be further subdivided by the use of fourth and/or fifth digits, which provide greater detail. The category codes listed in Table 2b
include all the related 4- and 5-digit codes.
d. Determining the Case-Mix Weights
cprice-sewell on DSK89S0YB1PROD with RULES
In the case-mix model adopted in July
2000, we examined the sum of scores for
the clinical dimension of the system,
and the sum of scores for the functional
dimension, and determined ranges of
scores to assign a severity level. For
example, in the original case-mix model
adopted in July 2000, severity levels
ranged from minimum to high for the
clinical dimension. Severity levels were
used to derive regression coefficients for
calculating case-mix relative weights.
The calculated coefficients from this
regression, which we call the payment
regression, were displayed in the July 3,
2000 Federal Register (65 FR 41201)
(‘‘Regression Coefficients for Calculating
Case-Mix Relative Weights’’).
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Now using the proposed fourequation case-mix model, we again
derived severity levels for the clinical,
functional, and services utilization
dimensions. We classified activities of
daily living variables as functional
variables, diagnostic, interaction, and
other OASIS variables as clinical
variables, and therapy-related variables
(threshold variables and visit count
variables) as services utilization
variables. For each episode in the
sample, we summed the variables’
scores by dimension. Then, we
examined the range of summed scores
within each equation and threshold
group of the sample, in order to
determine severity level intervals. We
determined how many severity levels to
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define for each of the equation/
threshold groups based on the relative
number of episodes in a potential
severity level, and on the clustering of
summed scores. In addition, for the
services utilization dimension, which is
based only on therapy visit utilization,
we defined severity intervals based on
relatively small aggregates (ones, twos,
and threes) of therapy visits above the
six-visit threshold up to 13 visits
(equations 1 and 3) and above the 14visit therapy threshold, up to 19 therapy
visits (equations 2 and 4). Our goal was
to ensure payment graduation due to
added numbers of therapy visits
between thresholds, without creating
too many severity levels.
BILLING CODE 4120–01–P
E:\FR\FM\04MYP2.SGM
04MYP2
BILLING CODE 4120–01–C
We derived the relative payment
weights for the proposed four-equation
model using the same kind of payment
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regression we employed in July 2000.
The sample episodes were classified
into severity levels as just described. We
defined indicator variables for the
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25387
payment regression based on these
severity classifications. The major
difference between the July 2000
payment regression and the one in this
E:\FR\FM\04MYP2.SGM
04MYP2
EP04MY07.006
cprice-sewell on DSK89S0YB1PROD with RULES
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
cprice-sewell on DSK89S0YB1PROD with RULES
25388
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
proposal is that additional indicator
variables were defined to identify the
episodes classified into each equation of
the four-equation model, as well as
certain thresholds and therapy visit
intervals. Including the indicator
variables allows us to combine
information derived from the fourequation model into a single payment
regression equation. For example, an
indicator variable was created for the
group of later episodes below 14 therapy
visits and, within this group, indicator
variables were created for the six-visit
therapy threshold and successive
therapy-visit aggregates. See the table of
regression coefficients (Table 4) for the
remaining indicator variables; the
indicator variables for the underlying
four equations are denoted by the terms
‘‘constant’’ and ‘‘intercept.’’ An
additional indicator variable denoted by
a constant was used for all episodes
with at least 20 therapy visits; it is
explained further below.
As with the original HH PSS rule,
regression coefficients in Table 4
represent the average addition to
resource cost due to each severity level.
(To show the coefficients in actual, as
opposed to resource cost, dollars, the
coefficients were scaled by a multiplier
representing the ratio of the HH PPS
average payment level to the Abt
Associates average resource cost level.)
However, the severity level coefficients
in Table 4 are specific to the
classification of the episode in the fourequation model; for example, only for
early episodes below 14 therapy visits
are the severity level coefficients
$861.74 for the third clinical severity
level, and $219.44 for the second
functional severity level.
The lowest-severity case-mix group is
the base group for the payment
regression, whose predicted cost is the
regression intercept value of $1,265.18.
This group consists of the lowest
clinical, functional, and services
utilization severity levels for episodes
classified as early episodes below the
14-visit therapy threshold (Equation 1 of
the four-equation model). The service
severity level for this group is severity
level 1 (S1), which comprises episodes
of 0 to 5 therapy visits.
To use the results of the payment
regression for determining payments,
find the severity level coefficients for
the applicable equation and add those
amounts to the regression intercept and
to the constant for the applicable
equation. There is no constant for the
first equation/group, the early episodes
below the 14-visit therapy threshold; for
this group, the constant is the regression
intercept. For example, later episodes
below the 14-visit therapy threshold
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with clinical severity level 2, functional
severity level 1, and service severity
level 2 have the following scaled
coefficients summed to represent the
resource cost: $1,265.18 for the
regression intercept; $139.26 for the
second clinical severity level; $645.90
for the second service severity level (6
therapy visits); and $210.94, a constant
amount for all later episodes below 14
therapy visits. The constant
incorporates the predicted average
resource cost for the lowest functional
severity group. The predicted average
resource cost, $2,261.28, is the sum of
these four coefficients from the
regression. Table 5 shows the results of
the computational procedure for all
combinations of severity levels within
each equation/threshold group.
TABLE 4.—REGRESSION COEFFICIENTS
FOR CALCULATING CASE-MIX RELATIVE WEIGHTS
Intercept (constant for all case
mix groups) ...........................
$1,265.18
1st and 2nd Episodes, 0 to 13
Therapy Visits
C2 .............................................
C3 .............................................
F2 ..............................................
F3 ..............................................
S2 (6 therapy visits) .................
S3 (7–9 therapy visits) .............
S4 (10 therapy visits) ...............
S5 (11–13 therapy visits) .........
380.66
861.74
219.44
379.06
499.96
935.02
1,375.38
1,755.92
1st and 2nd Episodes, 14 to 19
Therapy Visits
Constant ...................................
C2 .............................................
C3 .............................................
F2 ..............................................
F3 ..............................................
S2 (16–17 therapy visits) .........
S3 (18–19 therapy visits) .........
2,171.56
534.70
1,246.47
268.36
425.68
425.49
698.92
3rd+ Episodes, 0 to 13 Therapy Visits
Constant ...................................
C2 .............................................
C3 .............................................
F2 ..............................................
F3 ..............................................
S2 (6 therapy visits) .................
S3 (7–9 therapy visits) .............
S4 (10 therapy visits) ...............
S5 (11–13 therapy visits) .........
210.94
139.26
613.76
414.74
818.25
645.90
1,083.30
1,507.60
1,890.78
3rd+ Episodes, 14 to 19 Therapy Visits
Constant ...................................
C2 .............................................
C3 .............................................
F2 ..............................................
F3 ..............................................
S2 (16–17 therapy visits) .........
S3 (18–19 therapy visits) .........
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2,178.93
672.65
1,392.59
390.72
687.07
292.06
712.62
TABLE 4.—REGRESSION COEFFICIENTS
FOR CALCULATING CASE-MIX RELATIVE WEIGHTS—Continued
All Episodes, 20+ Therapy Visits
Constant ...................................
C2 .............................................
C3 .............................................
F2 ..............................................
F3 ..............................................
3,996.82
578.49
1,383.67
485.73
1,043.13
Note: Regression coefficients were scaled
by multiplier representing the ratio of the HH
PS average payment level to the Abt Associates average resource cost level.
The payment regression in Table 4
reflects a decision to group together
early and later episodes for purposes of
deriving the payment regression
coefficients for episodes at or above the
20-visit therapy threshold. This has the
advantage of producing a lower number
of case-mix groups than we would have
had without grouping. Earlier analysis
had revealed that the coefficients,
predicted average resource cost, and
relative weights of the case-mix groups
for episodes of 20 or more therapy visits
in Equations 2 (early episodes) and 4
(later episodes) had very similar values.
Specifically, of the 9 case groups
defined for these noted episodes in each
equation (a total of 18 groups), the
relative weights did not differ by more
than 3.5 percent for 7 pairs of groups;
in the remaining two pairs of groups,
the difference was slightly more than 7
percent. Because of the virtually
identical values, we specified our
payment regression procedure to
produce a single set of case-mix groups
for all episodes in the 20-visit threshold
group, with the result that the relative
case-mix weights do not differ according
to whether the episode is early or later.
This final step produced a total of 153
case-mix groups.
The predicted average resource cost
for each case-mix group is shown in
Table 5. As with the coefficients in
Table 4, these values are scaled up from
the resource cost values used to model
the case-mix, using a single multiplier.
The multiplier allows us to report the
coefficients and the predicted average
resource cost using dollars of the same
magnitude as the payments we would
make. It does not change the
relationships among the predicted
average resource costs, which are the
values that determine the relative case
mix weights.
We used the predicted average
resource costs for the 153 case-mix
groups to calculate the relative case-mix
weights. The relative case-mix weight
for a case-mix group is simply the
predicted average resource cost for the
group divided by the sample’s overall
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adjustment for nominal changes in case-
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in this section II.A.2.c.
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average resource cost. Table 5 shows the
final relative case-mix weights, after we
applied two further adjustments, the
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*Note: Case-mix weight is after applying
budget neutrality adjustment factor (see text
for description of adjustment of the weights).
Predicted average cost is calculated from the
regression coefficients in Table 4.
The budget neutrality adjustment to
the relative case-mix weights is required
to achieve no change in outlays when
moving from the original case-mix
system to the proposed new case-mix
system. The process of revising the casemix system results in relative weights
with an average value of 1.0 over all
1,656,551 sample episodes we used to
represent the totality of reimbursable
episodes in the first year of the new
case-mix system. The budget neutrality
adjustment restores the average casemix weight that results from the
revision process to the average level
observed before implementing the
proposed new case-mix system. To
implement the budget neutrality
adjustment, we used the constant
budget neutrality factor to increase the
weights for all 153 case-mix groups to
the prior average level. The resulting
adjusted case-mix weights prevent total
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payments under the proposed revised
HH PPS system from dropping below a
budget-neutral level. The budget
neutrality adjustment factor is
1.194227193.
Based upon our review of trends in
the national average case-mix index
(CMI), we are proposing an additional
adjustment to the HH PPS national
standardized rate to account for casemix upcoding that is not due to change
in the underlying health status of home
health users. Section 1895(b)(3)(B)(iv) of
the Act specifically provides the
Secretary with the authority to adjust
the standard payment amount (or
amounts) if the Secretary determines
that the case-mix adjustments resulted
(or would likely result in) a change in
aggregate payments that are the result of
changes in the coding or classification
of different units of services that do not
reflect real changes in case-mix. The
Secretary may then adjust the payment
amount to eliminate the effect of the
coding or classification changes that do
not reflect real changes in case-mix. To
identify whether such an adjustment
factor was needed, we first determined
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the current average case-mix weight per
paid episode.
The most recent available data from
which to compute an average case-mix
weight, or case mix index, under the HH
PPS is from 2003. Using the most
current available data from 2003, the
average case-mix weight per episode for
initial episodes is 1.233. To proceed
with this analysis, next we determined
the baseline year needed to evaluate the
trend in the average case-mix per
episode.
There are two different baseline years
that could be used to measure the
increase in case-mix:
1. A Cohort Admitted to Home Care
From October 1997 to April 1998 (the
Abt Case-Mix Study Sample Which Was
Used To Develop the Current Case-Mix
Model)
There are several advantages to using
data from this period of time as the
baseline from which we measure the
increase in case-mix. This time period is
free from any anticipatory response to
the HH PPS, and data from this time
period were used to develop the original
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HH PPS model. Also, this is the only
nationally representative dataset from
the 1997–1998 time period that
measures patient characteristics using
an OASIS assessment form comparable
to the one adopted for the HH PPS.
Because the Abt case-mix dataset was
used to determine the current set of
case-mix weights, the average case-mix
weight in the sample equals 1.0. The
sample’s value of 1.0 provides a starting
point from which to measure the
increase in case-mix. The increase in the
average case-mix using this time period
as the baseline results in a 23.3 percent
increase (from 1.0 to 1.233).
However, agencies included in the
sample were volunteers for the study
and cannot be considered a perfectly
representative, unbiased sample.
Furthermore, the response to Balanced
Budget Act of 1997 provisions such as
the home health interim payment
system (HH IPS) during this period
might produce data from this sample
that reflect a case-mix in flux; for
example, venipuncture patients were
suddenly no longer eligible, and longterm-care patients were less likely to be
admitted. Therefore, we are not
confident the trend in the CMI between
the time of the Abt Associates study and
2003 reflects only changes in nominal
coding practices, as will be explained in
more detail further below in this
section. Therefore, we are not proposing
to use this baseline year to determine
the baseline.
2. 12 Months Ending September 30,
2000 (HH IPS Baseline)
Analysis of a 1 percent sample of
initial episodes from the 1999–2000
data under the HH IPS revealed an
average case-mix weight of 1.125.
Standardized to the distribution of
agency type (freestanding proprietary,
freestanding not-for-profit, hospitalbased, government, and SNF-based) that
existed in 2003 under the HH PPS, the
average weight was 1.134. We note this
time period is likely not free from
anticipatory response to the HH PPS,
because we published our initial HH
PPS proposal on October 28, 1999. The
increase in the average case-mix using
this time period as the baseline results
in an 8.7 percent increase (from 1.134 to
1.233; 1.233–1.134=0.099; 0.099/
1.134=0.087; 0.087*100=8.7%).
Since the HH IPS, reported severity
has increased as episodes have shifted
from low severity groups to high
severity groups. Concurrently, there has
been a reduction in resource utilization.
For example, the number of visits per
episode has significantly declined under
the HH PPS since 1999. This decline is
illustrated in Table 6.
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TABLE 6.—AVERAGE NUMBER OF
HOME HEALTH VISITS PER EPISODE
Total home
health hisits
(excluding
LUPAs)
Year
1997 ..........................................
1998 ..........................................
IPS ............................................
2001 ..........................................
2002 ..........................................
2003 ..........................................
36.04
31.56
25.51
21.78
21.44
20.98
We believe that change in case-mix
between the time of the Abt Associates
case-mix study and the end of the HH
IPS period reflected substantial change
in real case-mix. First, throughout most
of this period, HHAs had no incentive
to bring about nominal changes in casemix because case-mix was not a part of
the payment system at that time.
Dramatic changes in the home health
benefit also became evident under the
HH IPS as a result of provisions of the
Balanced Budget Act of 1997.
Venipuncture patients were suddenly
no longer eligible; members of this
group often had multiple comorbidities
and commonly used substantial
amounts of personal care. In addition,
according to a study in the literature,
beneficiaries age 85 and older, as well
as beneficiaries dually eligible for
Medicare and Medicaid, were slightly
less likely to be admitted to home care
(McCall et al., 2003). Both of these
groups are associated with high needs
for personal care services, suggesting
that long-term care patients were less
likely to be admitted under the HH IPS.
The agency closure rates in States
associated with high utilization (for
example, Louisiana, Oklahoma, and
Texas) also suggests that admissions
among long-term care patients
experienced decline. The OASIS data
comparing the case-mix sample and the
HH IPS period exhibit some consistency
with these ideas, in that they indicate
substantial decline in admission of the
kinds of patients likely to be long-term
homebound beneficiaries with chronic
medical care needs—patients with
diabetes, impaired vision, parenteral
nutrition, bowel and urinary
incontinence, behavioral problems,
toileting dependency, and more-severe
transferring dependency.
Various studies are consistent with
the incentives created by the HH IPS
per-beneficiary cost cap—particularly
an incentive to admit many different
patients with low care needs and/or for
short periods to keep per-beneficiary
costs low (MedPac, 1999; GAO, 1998;
GAO, 1999; Smith et al., 1999).
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An important implication of these
studies and our comparative OASIS data
is that patients with intensive or lengthy
needs for nursing and personal care
services as opposed to short-term or
rehabilitative needs were less likely to
be found in the national home care
caseload as a result of the HH IPS. This
would mean that a larger share of
patients in the caseload would have
acute, post-acute, and rehabilitative
needs. Practice patterns began to change
concomitantly with the share of visits
shifting towards rehabilitation services
and, to a lesser extent skilled nursing.
In 1997 through 1998, the average
number of therapy visits per 60-day
period was about 3, whereas by the last
year of the HH IPS, it rose to 4.4, with
growth moderating thereafter. Skilled
nursing visits declined from more than
12 at the beginning of the HH IPS, and
stabilized at slightly more than 9 under
the HH PPS. Aide visits declined by 44
percent from 1997 to 2000, the last year
of the HH IPS, and continued to decline
at a slower rate under the HH PPS. An
issue in interpreting these trends in the
utilization data is the uncertainty about
how much of the startling change in
therapy provision was driven by patient
case-mix, and how much was driven by
an anticipatory response of the practice
pattern itself to our proposals for the
original HH PPS case-mix system. By
using a 10-visit therapy threshold, the
proposal installed a substantial payment
increase for high-therapy episodes. If
providers started responding to the
incentives in the anticipated HH PPS
even before it became effective, then our
measure of case-mix change between the
time of the Abt Associates case-mix
study sample and the HH IPS baseline
is affected by provider behavioral
change that is not strictly reflective of
the case-mix of the treated population.
In contrast to the 13.4 percent
increase that we consider a real casemix change, we believe that the 8.7
percent increase in the national casemix index between the HH IPS baseline
and CY 2003 cannot be considered a
real increase in case-mix. The trend data
on visits (Table 6), resource data
(presented below), and our analysis of
changes in rates of health characteristics
on OASIS assessments and changes in
reporting practices (presented in section
II.A.3.c of this proposed rule) all lead to
the conclusion that the underlying casemix of the population of home health
users actually was essentially stable
between the IPS baseline and CY 2003.
Our research shows that HHAs have
reduced services (see Tables 6 and 7)
while the CMI continued to rise (see
Table 7). We would normally expect
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growth in the CMI to be accompanied by
more consumption of services; but, to
the contrary, we measure slightly lower
resource consumption. This is indicated
by the data in Table 7 that illustrates, by
quarter, the average resource cost per
episode as well as the average CMI for
initial (admissions) episodes and all
episodes. (Note: In Table 7, the CMI data
for the HH IPS quarters are not adjusted
for distribution of agency types; that is,
they do not reflect the adjustment to the
HH IPS baseline that we cited earlier,
which caused the HH IPS baseline to
increase to 1.134 from 1.125). In
addition, in Table 7, the average
resource cost is not adjusted for wage
inflation. If the average resource cost
had been adjusted for wage inflation,
there would be an even larger reduction
in resource cost between the HH IPS
and HH PPS.)
TABLE 7.—AVERAGE RESOURCE COST AND CMI
Average resources
Period
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HH IPS:
1999Q4
2000Q1
2000Q2
2000Q3
HH PPS:
2000Q4
2001Q1
2001Q2
2001Q3
2001Q4
2002Q1
2002Q2
2002Q3
2002Q4
2003Q1
2003Q2
2003Q3
2003Q4
CMI all
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
$477.06
467.70
466.59
469.52
1.1278
1.1074
1.1223
1.1453
1.0823
1.0815
1.0982
1.1138
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
.................................................................................................................................
N/A
432.84
440.73
445.59
446.93
452.48
453.89
456.69
460.10
453.74
459.97
458.86
462.59
N/A
1.1841
1.1910
1.1965
1.2003
1.2052
1.1999
1.2099
1.2213
1.2152
1.2295
1.2302
1.2465
N/A
1.1622
1.1774
1.1724
1.1818
1.1800
1.1835
1.1832
1.1957
1.1889
1.2018
1.2002
1.2159
According to the data in Table 7, in
Year 2 (2002) of HH PPS, home health
resources per episode for new
admissions were approximately 2
percent lower than they were in the year
immediately before implementation of
HH PPS. At the same time, the national
case-mix index for new admissions rose
by approximately 0.02 per year. (The
national case-mix index for all episodes,
new and continuing, rose by
approximately 0.01 per year.) By Year 3
(2003) of the HH PPS, home health
resources per admission episode rose
slightly above the Year 2 level, and then
stabilized at levels similar to the HH
IPS. The national CMI for new
admissions continued to rise by about
0.02 per year (with the CMI for all
episodes rising by about 0.01 per year).
Therefore, based upon our trend
analysis described above, we believe the
change in the case-mix index between
the Abt case-mix sample (a cohort
admitted between October 1997 and
April 1998) and the HH IPS period (the
12 months ending September 30, 2000)
is due to real case-mix change. We take
this view, even though we understand
that there may be some issue as to
whether this period was affected by
nominal case-mix change due to
providers’ anticipating, in the last year
of HH IPS, the forthcoming case-mix
system, with its incentives to intensify
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rehabilitation services. This change
from these two periods is from 1.00 to
1.134, an increase of 13.4 percent.
However, we are not proposing to adjust
for case-mix change based on this
change in values. However, we are
proposing that the 8.7 percent of casemix change that occurred between the
12 months ending September 30, 2000
(HH IPS baseline, CMI=1.134), and the
most recent available data from 2003
(CMI=1.233), be considered a nominal
change in the CMI that does not reflect
a ‘‘real’’ change in case-mix.
In addition to the trend analysis
above, we conducted several additional
kinds of analyses of data and
documentary materials related to home
health case mix coding change. These
analyses are described in detail in
section II.A.3.e. The results support our
view that the change in the CMI since
the HH IPS baseline mostly reflects
provider responses to the changes that
accompanied the HH PPS, including
particulars of the payment system itself
and changes to OASIS reporting
requirements. Our analyses indicated
generally modest changes in overall
OASIS health characteristics between
the two periods noted above, a specific
pattern of changes in scaled OASIS
responses that is not indicative of
material worsening of presenting health
status, various changes in the OASIS
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reporting instructions that help account
for numerous coding changes we
observe, and a large increase in postsurgical patients with their traditionally
lower case-mix index.
Our past experience establishing other
prospective payment systems also led us
to believe a proposal to make this
adjustment for nominal change in casemix is warranted. In other systems,
Medicare payments were almost
invariably found to be affected by
nominal case-mix change. We are
considering several options for
implementing this case-mix adjustment.
These options include incorporating the
entire ¥8.7 percent adjustment in CY
2008, incorporating an adjustment of
¥5.0 percent in CY 2008 and an
adjustment of ¥2.7 percent in CY 2009,
and incorporating an adjustment of
¥4.35 percent in CY 2008 and an
adjustment of ¥4.35 percent in CY
2009. However, because of the potential
impact our proposed adjustment may
have on providers, we are proposing
and requesting comment on whether to
adjust for the nominal increase in
national average CMI by gradually
reducing the national standardized 60day episode payment rate over 3 years.
During that period we would continue
to update our estimate of nominal casemix change and adjust the national
standardized 60-day episode payment
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rate accordingly for any nominal change
in case-mix that might occur. We
propose to implement a 3-year phase-in
of the total downward adjustment for
nominal changes in case-mix by
reducing the national standardized 60day episode payment rate by 2.75
percent each year up to and including
CY 2010. This annual reduction percent
is based on our current estimate of the
nominal change in case-mix that has
occurred between the HH IPS baseline
(+0.099) and 2003. However, if, at the
time of publication of the final CY 2008
HH PPS rule, updates of the national
claims data to 2005 indicate that the
nominal change in case-mix between
the HH IPS baseline and 2005 is not
+0.099, we would revise the percentage
reduction in the next year’s update. The
revision would be determined by the
ratio of the updated 3-year annual
reduction factor to the previous year’s
annual reduction factor. For example,
the scheduled annual reduction factor is
now estimated to be 0.9725 (equivalent
to a 2.75 percent reduction); for CY 2008
we would multiply this reduction factor
by the ratio of the updated reduction
factor to 0.9725. For the CY 2010 rule,
which governs the third and final year
of the case-mix adjustment transition
period, we would obtain the CY 2007
national average CMI to compute the
updated value for nominal case-mix
adjustment. Again, we would form the
ratio of the updated adjustment factor to
the previous year’s effective adjustment
factor. The annual updating procedure
avoids a large reduction for the final
year of the phase-in, in the event that
the CY 2007 national average case-mix
index reflects continued growth since
CY 2005. The calculation of the adjusted
national prospective 60-day episode
payment rate for case-mix and area wage
levels is set forth in § 484.220. We are
proposing to revise § 484.220 to address
changes to case-mix that are not a real
change in case-mix.
CMS proposes to adjust the national
prospective 60-day episode payment
rate to account for the following:
• HHA case-mix using a case-mix
index to explain the relative resource
utilization of different patients. To
address changes to the case-mix that
were a result of changes in the coding
or classification of different units of
service that did not reflect real changes
in case-mix, the national prospective 60day episode payment rate will be
adjusted downward as follows:
—For CY 2008 the adjustment is 2.75
percent.
—For CY 2009 and CY 2010, the
adjustment is 2.75 percent in each
year.
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• Geographic differences in wage
levels using an appropriate wage index
based on the site of service of the
beneficiary.
We plan to continue to monitor
changes in the national average CMI to
determine if any adjustment for nominal
change in case-mix is warranted in the
future.
Accordingly, based upon our analysis
and conclusions, we are proposing a
new set of case-mix weights that reflect
the four-equation model and a payment
adjustment for the nominal change in
the case-mix index described above. We
arrived at these weights, listed in Table
5, by first determining relative weights
for each of the 153 groups using the
four-equation model and the payment
regression. The definition for each of
these groups based on clinical,
functional, and service severity levels is
described in Table 5. Each of these
relative weights was adjusted by
multiplying it by an adjustment factor to
make the proposed payments budgetneutral to current estimated payments
for CY 2008. This budget neutrality
factor raised the proposed average casemix weight to the case-mix index
reflected by the most recent data
available from 2003. The proposed
budget-neutrality factor for 2008 is
1.194227193. Each budget neutral,
adjusted, weight in Table 5 was
calculated in the following manner:
Relative Weight × 1.194227193.
References to literature cited in this
section:
N. McCall et al., ‘‘Utilization of Home Health
Services before and after the Balanced
Budget Act of 1997: What Were the
Initial Effects?’’ Health Services
Research, Feb. 2003: 85–106.
MedPac, Report to the Congress: Selected
Medicare Issues, June 1999: 105–115.
General Accounting Office (GAO), ‘‘Medicare
Home Health Benefit: Impact of Interim
Payment System and Agency Closures on
Access to Services,’’ GAO/HEHS–98–
238, Sept. 1998.
General Accounting Office (GAO), ‘‘Medicare
Home Health Agencies: Closures
Continue, with Little Evidence
Beneficiary Access Is Impaired,’’ GAO/
HEHS–99–120, May 1999.
B.M. Smith et al., ‘‘An Examination of
Medicare Home Health Services: A
Descriptive Study of the Effects of the
Balanced Budget Act Interim Payment
System on Access to and Quality of
Care,’’ Center for Health Services
Research and Policy, George Washington
University, Sept. 1999.
3. Description and Analysis of Case-Mix
Coding Change under the HH PPS
As stated in section II.A.2.c of this
proposed rule, under section
1895(b)(3)(B)(iv) of the Act, we are
proposing a reduction in HH PPS
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national standardized 60-Day episode
payment rate to offset a change in
coding practice that has resulted in
significant growth in the national casemix index (CMI) since the inception of
the HH PPS that is not related to ‘‘real’’
change in case mix. The factor was
determined by calculating the change in
the national CMI between the HH IPS
and the HH PPS.
In this section II.A.3, for purposes of
illuminating the sources of CMI increase
in terms of the case-mix system itself,
we identify the severity levels with the
largest growth between the two periods.
We will provide, in Table 8, the
percentage change in volume for each of
the 80 case-mix groups, and summary
statistics of the changes. Table 9 shows
the rates of all OASIS assessment items
in the two time periods. We will explain
below our inferences from Table 9 about
the comparative health status of the
populations treated in the two time
periods. Subsequent to that, we will
explain our analysis of the changes to
OASIS reporting instructions that were
likely to have affected reported case
mix. We also describe analyses we
performed to quantify the effect on the
CMI of increases in post-surgical
episodes in the national caseload, and
our interpretation of the analyses. We
conclude with a summary and
interpretation of our key findings from
the descriptive analysis of OASIS
assessment data, analysis of OASIS
reporting instructions, and analysis of
changes in post-surgical volume.
In making these analyses, we
reviewed data from two samples. The
first, the HH IPS sample, is the same
sample used in section II.A.2.c of this
proposed rule for determining the IPS
baseline that we used to determine the
proposed adjustment for nominal
change in case-mix. The HH IPS sample
is a 1 percent random sample of claims
(total number of 18,480) with its
matched start of care OASIS
assessments from the 12 months
immediately preceding HH PPS. We
matched the assessments to determine
what the patient’s case-mix group
would have been had HH PPS been in
effect. To simulate 60-day episodes from
actual claims we used the same method
that was used to create the initial
development sample for the HH PPS
case-mix system. In performing the
simulation, we took into account the
timing of the start of care in relation to
previous service periods, and used only
60-day periods that would have
corresponded to initial episodes in a
sequence of adjacent episodes that
consisted of one or more simulated
episodes. We considered initial
episodes as the first episodes that follow
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periods of at least 60 days without
receiving home health service.
The second sample is a 20 percent
sample of FY 2003 claims for initial
episodes again matched to start of care
OASIS assessments. In both samples, we
corrected any initial errors in
determining the beneficiary’s preadmission location that affected the
HHRG before determining the HHRG.
We made the correction by consulting
the sample member’s claims history for
information about previous inpatient
stays.
a. Change in Case-Mix Group
Frequencies
Table 8 presents the share of the
population assigned to each severity
level of the case-mix system’s three
dimensions (clinical, functional, and
service). The table indicates there was a
strong shift away from the lowest-
severity case-mix groups towards higher
severity level between the two sample
periods. Growth of the two highest
severity levels of the clinical domain
was approximately 23 percent; for every
100 beneficiaries, 8 additional
beneficiaries were classified to the
highest two clinical dimensions in 2003
compared to the HH IPS period.
Growth of the functional severity
levels F2 and F3 totaled 12 percent. The
12 percent growth in share was
concentrated in F2. Share growth for F2
and F3 was offset by a decline for the
two lowest functional severity levels
and, potentially, a tiny decline in share
for the severest functional level, F4.
Notwithstanding the small decrease in
the share assigned to F4, for every
hundred beneficiaries, about 7
additional beneficiaries were classified
to the higher severity levels F2 and F3.
The data also indicate that the
proportion of patients with a prior SNF
or rehabilitation facility discharge in the
14 days before admission, but no
hospital discharge in that period, grew
by 25 percent for episodes below the 10visit therapy threshold, and 64 percent
for episodes above the 10-visit therapy
threshold. These patients receive a
higher case-mix score than patients from
all other pre-admission locations on the
OASIS (including inpatient discharge).
In addition, the table indicates growth
in the high-therapy groups (levels S2
and S3) of 30 percent. This means that
for every hundred beneficiaries, 8
additional beneficiaries were assigned
to receive at least 10 therapy visits in
2003 compared to the HH IPS period.
Under the HH PPS, approximately 35
percent of patients in their initial
episode received at least 10 therapy
visits.
TABLE 8.—COMPARISON OF SEVERITY LEVEL PREVALENCE, HH IPS SAMPLE AND 2003 HH PPS SAMPLE
HH IPS
(percent)
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All
All
All
All
All
All
All
All
All
All
All
All
All
C0
C1
C2
C3
F0
F1
F2
F3
F4
S0
S1
S2
S3
..............................................................
..............................................................
..............................................................
..............................................................
...............................................................
...............................................................
...............................................................
...............................................................
...............................................................
...............................................................
...............................................................
...............................................................
...............................................................
Table 9 shows the shares of total
episodes for the complete set of 80
original case-mix groups, during both
the HH IPS and the HH PPS FY 2003.
Table 9 also displays each group’s casemix weight. Ten groups had no change
in their share of episodes between the
HH IPS period and the HH PPS period
in the table. Of the remaining 70 groups,
38 groups, slightly more than half, had
a larger share of total episodes under
HH PPS than the HH IPS. However,
decline in share of total episodes was
associated with minimal or low clinical
severity (C0 and C1). Only 8 of 40
groups with moderate (C2) or high (C3)
clinical severity had decrease in their
share of episodes under HH PPS, with
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Min ..................................................................
Low .................................................................
Mod ................................................................
High ................................................................
Min ..................................................................
Low .................................................................
Mod ................................................................
High ................................................................
Max .................................................................
Min ..................................................................
Low .................................................................
Mod ................................................................
High ................................................................
most of the remaining moderate or high
clinical severity groups having a share
increase. As noted above, growth in
functional severity level F2 almost
entirely offset the loss of population
from groups F0 and F1. Only three of 16
groups in the functional severity level
F2 experienced a decline in episode
shares, and this was concentrated
entirely in the two lowest clinical
severity groups.
We summarized the association
between case-mix group severity and
change in episode share by calculating
the rate ratio for growth in episode
shares. We sorted the groups by casemix weight and divided the groups into
the top 40 weights of the 80-group case-
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HH PPS 2003
(percent)
29.69
36.49
28.91
4.91
9.27
28.57
45.18
10.39
6.60
65.74
7.40
19.94
6.92
22.07
36.19
35.50
6.25
6.15
25.40
51.30
10.83
6.33
55.87
9.22
23.59
11.32
Difference
-7.62
-0.31
6.58
1.34
-3.12
-3.17
6.12
0.44
-0.27
-9.87
1.83
3.64
4.40
mix system and the remaining 40
weights. The rate ratio was determined
by dividing the growth in total share of
the top 40 weights by the growth in total
share for the remaining 40 weights. The
groups with the 40 smallest weights
have mostly reductions in episode
shares (24 of 40 have reductions), and
the groups with the largest 40 weights
have mostly increases in episode shares
(24 of 40 groups). The rate ratio for
positive changes was 1.71, which means
that as a group the top 40 case-mix
weights were about 70 percent more
likely than the bottom 40 to have an
increase in share of total episodes.
BILLING CODE 4120–01–P
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b. Health Characteristics Reported on
the OASIS
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To further our understanding of the
relative roles of case-mix change and
coding changes that might be
responsible for the .0991 increase of the
national HHRG CMI, we analyzed the
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HH IPS and HH PPS samples’ health
characteristics, based on the start-of-care
OASIS assessment. We compared the
proportion of start-of-care assessments
that had each OASIS characteristic,
using data from our HH IPS and HH PPS
2003 samples. We used the woundrelated OASIS data to compute statistics
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on changes in numbers of wounds. The
results are shown in Table 10 and
discussed below. (Items scored in the
HH PPS 80 group case-mix system are
shown in bold.) Table 10: Comparison
of rates of response categories on OASIS
Start of Care Assessments, HH IPS
Sample and 2003 HH PPS Sample
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In general, the results showed that
health characteristics as measured by
the OASIS items were stable or changed
little. Exceptions to the general findings
were indications that the HH PPS
population included:
• More post-acute and more postsurgical patients;
• More patients that had a recent
history of post-acute institutional care;
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• More patients with a recent change
in medical or treatment regimen;
• More patients in the orthopedic
diagnosis group defined under the PPS
system’s clinical dimension; and
• More patients assessed with
dependencies in Activities of Daily
Living (ADLs) and Instrumental
Activities of Daily Living (IADLs) as of
14 days before the assessment. The
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proportion of patients using at least 10
therapy visits also rose noticeably.
Otherwise, the rate comparisons of
OASIS items are generally
unremarkable. Several measures usually
reflective of a more compromised health
status, including ADL limitations,
incontinence, pain, short life
expectancy, and diagnosis severity had
a somewhat higher rate in the HH PPS
sample than the HH IPS sample.
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However, various physiologic measures
and risk factors showed little or no
change, including urinary tract
infection, visual and aural functioning,
dyspnea, bowel ostomy, bowel
incontinence, obesity, alcoholism, drug
dependence, depressive symptoms,
behavioral problem frequency, use of
home oxygen, infusion therapy, and
nutritional therapies. In addition, the
probability that a patient used
psychiatric nursing was reduced, from 2
percent to 1 percent.
The current HH PPS case-mix system
recognizes four types of diagnoses for
purposes of assigning patients to casemix groups: diabetes, orthopedic
conditions, neurological conditions, and
burns and trauma. These diagnoses were
found to be associated with higher-thanaverage resource costs in the original
case-mix research. The data in Table 10
indicate that the share of patients
assigned to the four case-mix diagnosis
groups grew by 23 percent. This change
was due to an additional 7 per hundred
patients assigned to the orthopedic
diagnosis group, and an additional 2 per
hundred assigned to the diabetes
diagnosis group. The share of patients
assigned to the neurological diagnosis
group remained unchanged (at 8 per
hundred), and the share of patients
assigned to the burns/trauma diagnosis
group declined by 2 per hundred.
There are two important reasons why
we believe these changes reflect mostly
nominal, as opposed to real, underlying
case-mix change. First, the notable
increase in the proportion of orthopedic
diagnoses is due at least in part to the
listing of the diagnosis code for
abnormality of gait in this diagnosis
group. The diagnosis code for
abnormality of gait (781.2) is commonly
used to indicate that the primary reason
for the home health treatment is
rehabilitation services (for example,
physical therapy). Detailed analysis
shows that this use of this code grew by
50 percent between the HH IPS period
and the early years of the HH PPS. We
believe agencies had an incentive to use
this code on Medicare claims to support
treatment plans that included large
amounts of rehabilitation services. This
code could be used even if the
underlying condition was not
orthopedic. Second, the decline in
burns/trauma assignment may be due in
part to agencies’ early confusion about
how to use the ICD–9–CM coding
system when a patient has an open
wound not due to an injury. We believe
traumatic open wounds were thus
overreported early in HH PPS. However,
with educational efforts initiated by
CMS and the home health industry after
HH PPS began, understanding and
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application of the coding instructions
for traumatic wound diagnoses
improved, resulting in a lower, and
more accurate, rate of reported burns/
trauma cases, which we believe is now
more representative and not an actual
change in case-mix.
Other wound-related items varied in
the types of change they experienced.
The basic wound-related item
measuring the presence of a skin
disturbance or lesion (M0440) increased
by 15 percentage points; however, this
measure is general and covers a broad
range of both clinically significant and
insignificant problems. We note the
three detailed series of OASIS items
following M0440, that is, surgical
wounds, pressure ulcers, and stasis
ulcers, had varying results. The
proportion of patients with pressure
ulcers increased from 5.4 percent to 6.6
percent with more than half of the
pressure ulcers at Stage 2. (Pressure
ulcers are staged using four levels, 1 to
4, in order of increasing severity.) The
average number of pressure ulcers per
hundred patients increased from 9.2 to
11.1. Pressure ulcers per 100 persons
with any pressure ulcers were 1.70 in
the HH IPS sample and 1.68 in HH PPS
sample. Excluding the approximately 5
percent of pressure ulcers that were
unobservable, the average number of
stage 1 and stage 2 pressure ulcers per
patient with pressure ulcers did not
change; the number of stage 3 and stage
4 pressure ulcers per patient with
pressure ulcers declined by 13 percent
and 27 percent, respectively. In terms of
the overall population, stage 1 and stage
2 pressure ulcers per beneficiary
increased by about 23 percent between
the HH IPS and HH PPS; stage 3
pressure ulcers per beneficiary
increased 7 percent; and stage 4
pressure ulcers decreased by 11 percent.
There was no change in the item
measuring the healing status of the most
problematic pressure ulcer.
Review of these data suggest to us that
the population of home health
beneficiaries was more likely to include
pressure ulcer patients under HH PPS,
that such patients had about the same
number of pressure ulcers per person in
both periods, and that the pressure ulcer
stage tended to be of lower severity, on
average, under HH PPS compared to the
HH IPS. We note that under OASIS
coding policy, there is ‘‘no reverse
staging’’ of pressure ulcers, which means
that a healed pressure ulcer could be
recorded and contribute to the statistics.
Therefore, because of such policy, from
these statistics it is difficult to draw
conclusions about change in the burden
of care related to pressure ulcers under
the HH PPS.
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We also found little change in
numbers of stasis ulcers reported or
their overall seriousness. The
proportion of patients with any stasis
ulcers was 3 percent under the HH IPS
and 2 percent under HH PPS.
Furthermore, while some patients have
more than one stasis ulcer, the number
of stasis ulcers per 100 patients
decreased from approximately 5.0 to
4.5. The status of the most problematic
stasis ulcer (if any) did not change. The
stasis ulcer decline may be attributable
in part to improved knowledge among
agency clinical staff in distinguishing
among different types of ulcers.
Based on the HH IPS and the HH PPS
samples, the case-mix of the population
of home health beneficiaries clearly
shifted towards more post-surgical
patients, with a possible indication that
the average patient’s healing status
worsened. The proportion of patients
with any surgical wounds increased
from 22.7 percent to 30.0 percent. The
number of surgical wounds per hundred
patients increased from 37.4 to 49.2, due
entirely to the increased numbers of
post-surgical patients; there was no
change in the estimated average number
of surgical wounds per person with any
surgical wound (our estimate assumed
patients recorded as having at least one
unobservable surgical wound had only
one such wound). There was a 6
percentage point increase in the
probability that the most problematic
surgical wound’s healing status would
be in an early stage of healing (indicated
on the OASIS by the response category
‘‘early/partial granulation,’’ which refers
to the type of newly forming tissue
which may be visible in a healing
wound), and a 1 percentage point
increase in the probability that the
wound’s healing status would be ‘‘not
healing’’. This amounts to a 13 percent
increase in the share of mostproblematic surgical wounds assigned
to the two less-favorable healing
categories, early and partial granulation
or not healing.
Our review of current functional
measures also showed mixed results,
with some (grooming, upper body
dressing, meal preparation, laundry,
telephone use, independence with
inhalant, and injective medications)
exhibiting minor or little change. Other
measures experienced negative and
sometimes substantial change
(transferring, ambulation, feeding, and
housekeeping). In both the HH IPS and
the HH PPS sample periods, prior
functional measures were almost
invariably reflective of a better average
prior status (as of the 14 days before the
assessment) compared to the current
status. However, in the HH PPS sample,
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the overall difference between prior and
current status is less than in the HH IPS
sample. In other words, average current
status is reported as generally more
functionally impaired under HH PPS
than under the HH IPS, and accordingly,
average prior status reflects a different
relationship to current status in the two
sample periods. We believe this pattern
may reflect better understanding of the
definition and interpretation of the prior
status items as agencies became more
familiar with the assessment.
We also found that quite a few items
with scaled responses indicated a
decline in the numbers of patients at the
best end of the scale (for example,
independent in bathing), as well as a
decline or stability in the numbers
(usually very small numbers) at the
worst end of the scale (for example,
totally dependent in bathing). Often, the
decline in numbers of patients at the
best end was offset by increased
numbers rated just below the best end
of the scale. This pattern was evident
with measures of primary and
secondary diagnosis symptom severity,
cognitive functioning, confusion,
hearing, speech, current upper and
lower body dressing, current bathing,
current toileting, current transferring,
current ambulation, and several of the
prior function-related items.
Table 10 results indicated a pattern of
change in functional severity away from
the two lowest severity groups and
towards the middle severity group. The
shift towards the middle severity group
could be explainable by seemingly
minimal changes in a person’s ADL
ratings. The examples below show how
an incremental change in reported
dependency on a single functional item
in the HHRG system could change the
case-mix group functional severity to F2
from F1. For a hypothetical individual
in the second-lowest functional severity
group (F1), a single added limitation
(that is, going from independence to a
minimal limitation) could result in the
individual moving from severity
category F1 into severity category F2.
Similarly, in the case of transferring or
locomotion, a score change that is due
only to going from one level of
limitation to the next worst level could
possibly result in the individual moving
from severity category F1 into severity
category F2.
The three prognosis-related items also
showed mixed results, with the overall
and rehabilitative prognosis items
changing minimally and the life
expectancy item indicating a more than
two-fold increase in the proportion of
the population of home health
beneficiaries with a life expectancy
below 6 months. We believe that as
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agencies increasingly recognized that
the life expectancy item was used in
measuring adverse events under the
Outcome-based Quality Improvement
(OBQM) system, which commenced in
the early years of HH PPS, agencies
became more careful to record the
prognosis accurately.
We discuss below some of the
influences on the reporting of the
OASIS health characteristics since the
HH PPS began. Our conclusion from
review of the changes in rates of OASIS
characteristics, however, is that it is far
from certain that the essential health
status and service needs of the
population of home health beneficiaries
changed dramatically under the HH
PPS. A very substantial majority of the
OASIS characteristics rates noted for
2003 in Table 10 were within 2
percentage points of their initial value at
the HH IPS baseline. Also, few OASIS
items experienced more than moderate
adverse change. Included within our
analysis of adverse changes were several
items unrelated to the HHRG system,
including diagnosis symptom severity,
recent regimen or treatment change,
feeding, housekeeping, laundry, life
expectancy, and various prior functional
status items. Items with adverse change
that are related to the HHRG system
include use of post-acute institutional
care, orthopedic cases, incontinence,
pain, surgical wound healing status, and
transferring.
c. Impact of the Context of OASIS
Reporting
As noted above, some items with
adverse changes are related to the HHRG
system. We believe that some of these
changes are a likely result of more care
being taken in conducting the
assessment. Agencies were exposed to
OASIS training and educational
initiatives in the early HH PPS period
and, beginning with the HH PPS,
agencies had an incentive to ensure they
did not overlook items that could affect
the HHRG. The new emphasis on proper
application of OASIS guidelines was
later reinforced when CMS began to
implement outcome-based quality
reporting (OBQI) in early 2002.
We further believe that, to some
extent, incentives brought by the
payment and quality program changes
interacted with the subjective aspects of
the assessment process to cause nominal
coding change. The process of coding,
especially diagnosis coding and
determining certain rating scales, entails
some discretion by the agency. With
diagnosis coding, patients may have
more than one diagnosis that can
reasonably be called the primary
diagnosis. The significant growth in
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orthopedic diagnosis codes partly
reflects the ambiguity in the diagnosis
assignment process itself, particularly in
the context of a system where financial
incentives to choose one diagnosis over
another may be operating. Furthermore,
scales of ADL functioning can be
difficult to apply with some patients
because of daily variability in their
status and the multiple dimensions of
the functional item. This difficulty may
also result in a bias towards selecting a
more-severe rating in the context of the
financial incentives of the HH PPS. We
believe that such bias was likely
reinforced by the financial incentive
created by the 10-visit therapy
threshold. As a result of that incentive,
high-therapy treatment plans became
more common under HH PPS. OASIS
coding practices regarding ‘‘functional
status’’ could have changed in ways to
make coding more harmonious with the
new emphasis on therapy in treatment
plans.
Not only is the process of coding
likely subject to discretion, several
issuances providing official guidance on
specific OASIS items released early in
the HH PPS could have caused some
clinicians to downgrade patients in their
assessment of the specific item.
Instructions regarding the dressing,
bathing, toileting, transferring, and
locomotion items, assessment items all
used in the HH PPS case-mix system,
were amended in August 2000 in such
a way that the concept of performing the
function safely was highlighted
prominently in the item-by-item
instructions. (See M0650 to M0700 in
Chapter 8 at https://www.cms.hhs.gov/
apps/hha/usermanu.asp).
This change alone arguably
emphasized the concept that ‘‘safety’’ is
a consideration in assessing the
patient’s ability to perform the activity
and in determining the functional item
on the OASIS. Thus, it seems a likely
contributing factor in explaining why
the OASIS data in Table 10 show a
strong tendency for several ADL
statistics to shift away from the
completely independent level. In terms
of impact on the patient’s case-mix
group, it should be noted that the casemix score for most of these items
becomes a positive value if the assessing
clinician selects any response category
other than the one indicating that the
patient is able to function
independently. (Note: Selecting
‘‘unknown’’ does not add to the case-mix
score.)
Another change in OASIS instructions
affected the pain item, M0420, in
August 2000. The section on
Assessment Strategies offered additional
strategies for assessing pain in a
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nonverbal patient, such as facial
expression and physiological indicators
(for example, perspiration, pallor). If
many clinicians were not using these
strategies during the HH IPS period, it
is likely that fewer patients would have
been assessed to have pain. The
strategies section also introduced the
term ‘‘well controlled’’ in referring to
pain assessment, by adding the
following sentence: ‘‘Pain that is well
controlled with treatment may not
interfere with activity or movement at
all.’’ If, as a result of this guidance,
clinicians began taking into account
patient adherence to pain medication,
one result could have been more
patients were assessed with pain.
Adherence to pain medication is an
important issue in medicine, because
many patients experience side effects
that may cause them to trade off pain
control for diminution of side effects.
The assessment instructions for
incontinence were also amended in
August 2000. The Assessment Strategies
section for M0520 included a new
statement: ‘‘Urinary incontinence may
result from multiple causes, including
physiologic reasons, cognitive
impairments, or mobility problems.’’
This clarification could have potentially
sensitized clinicians to the idea that the
definition of incontinence is not simply
about physiologic status (that is, bladder
control), but instead involves
considerations such as mobility and
cognition that can intervene to produce
wetting on clothing. Because more
patients were assessed as incontinent in
the HH PPS period according to M0520
(which is not used in the case-mix
system), the OASIS skip pattern drew
more responses for M0530, the case-mix
item used to assess the type of
incontinence. A similar change in the
Assessment Strategies section was made
for M0540, bowel incontinence, with
the potentially similar impact of
increasing the reported rate.
Finally, two changes to the OASIS
manual in August 2000 could have
expanded the number of patients
reported to have surgical wounds. The
first change affecting surgical wounds
was to expand the definition to read:
‘‘Medi-port sites and other implanted
infusion devices or venous access
devices are considered surgical
wounds.’’ The possible impact on the
national case-mix index of broadening
this instruction is that more openings in
the skin would be considered surgical
wounds, requiring more assessments to
respond to OASIS item M0488, a casemix variable, provided that the site is
the most problematic surgical wound
under the expanded definition. It is
possible for the healing status of these
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types of openings to be ‘‘fully
granulating’’ (with no case-mix score
available), at a stage of ‘‘early or partial
granulation’’ (a score of 7), or even ‘‘not
healing’’ (a score of 15). For example, a
central line site being held open by the
line itself may not reach a fully
granulating state, or a site that has
become infected may be assessable as
‘‘not healing.’’ Before these
clarifications, it may not have occurred
to many assessing clinicians to classify
these device-related sites as surgical
wounds, so it seems reasonable to
assume that more surgical wounds
would be reported after the manual
change, and to assume that some of
these would add to the higher rates of
wounds reported to be not healing or in
early healing stages.
The second manual change was a new
bulleted item in the OASIS responsespecific instructions: ‘‘A muscle flap
performed to surgically replace a
pressure ulcer is a surgical wound and
is no longer a pressure ulcer.’’ We note
it is not uncommon for home health
patients to be admitted after
hospitalization for pressure ulcer
procedures, such as debridements or
grafts. While the OASIS manual change
noted that debridements do not change
the classification of the pressure ulcer to
a surgical wound, the muscle flap does
change the classification. Again, we
would expect this technical clarification
to have added to the reported number of
surgical wounds.
Another OASIS manual change added
the statement that ‘‘A PICC line is not a
surgical wound, as it is peripherally
inserted, although it is considered a skin
lesion (see M0440).’’ The PICC line is a
common method of delivering antibiotic
treatment intravenously at home.
However, using the same reasoning
about the perception of device-related
openings before the issuance of the
August 2000 manual, we believe it is
unlikely that the peripherally inserted
central catheters (PICC) line clarification
caused reduction in reported surgical
wounds as it would not have originally
occurred to many assessing clinicians to
have classified it as such in the first
place.
The changes to the OASIS manual
instructions noted in this section
present concrete potential causes of
increased OASIS reporting rates for
case-mix items measuring ADL
dependencies, pain, incontinence, and
surgical wounds. While it is difficult to
know with data available how much of
the reported increase is traceable to
these clarifications, we believe that in
the environment at the time the HH PPS
was initiated, which included strong
efforts in the public and private sectors
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to educate home health agencies on the
proper application of OASIS, the
changes must have had some impact. To
the extent that the result was a new
approach to classifying patients for
purposes of the OASIS items involved,
we note the increased item reporting
rates may not represent an actual
material change in the health status of
the population under treatment in home
care. Given the potential impact of
OASIS reporting instructions on casemix, we will continue to monitor
appropriate requirements in an effort to
promote effectiveness in the HH PPS
payment methodology. Clarifications to
the ‘‘OASIS Implementation Manual’’
are issued administratively through
normal operating procedures.
• Impact of more post-surgical
patients
We also reviewed the increase in rates
of post-surgical patients that occurred
under the HH PPS to improve our
understanding of how this increase
contributed to the growth in the casemix index between the IPS baseline and
the 2003 HH PPS period. Being a patient
with a surgical wound does not in and
of itself increase the case-mix score.
However, if the surgical wound is not
assigned to the best healing status on
the OASIS assessment, the score will
increase. Therefore, an increase in the
proportion of post-surgical patients
makes more episodes eligible for an
addition to the score based on the
healing status. Furthermore, data shown
in Table 10 indicate that under the HH
PPS, post-surgical patients were more
likely to be assessed with a healing
status that impacts upon a case-mix
score. Because surgical patients have
historically had other characteristics
associated with relatively low resource
use, we hypothesized that a higher
occurrence of surgical wound patients
would not necessarily lead to a rise in
the overall CMI.
We analyzed the extent to which the
severity of HHRG-related OASIS items
is due to the increased presence of postsurgical patients, of whom many would
have mobility restrictions, pain, and an
evolving surgical wound status in the
early post-acute phase. First, we
analyzed the relationship between
having a surgical wound and having a
characteristic indicative of increased
severity. Second, we recalculated the
average case-mix change under two
alternative assumptions: (1) The higher
share of post-surgical cases is entirely
responsible for the changed CMI; (2)
growth in the CMI for post-surgical
patients was the same as growth in the
CMI for non-surgical patients. The
second assumption would reveal the
potential effect of a faster worsening of
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presenting health status through time
among post-surgical patients compared
to non-surgical patients.
As expected, post-surgical patients
exhibited certain characteristics at
different rates. Specifically, compared to
non-surgical patients, they were slightly
less likely to have no home therapies
(M0250), about 40 percent more likely
to have frequent pain (M0420), nearly
three times as likely to have a bowel
ostomy, nearly twice as likely to have
come from an inpatient rehabilitation
facility and to have intractable pain, and
15 percent less likely to be independent
in lower body dressing. Many other
characteristics were less prevalent
among post-surgical patients, such as
having any pressure or stasis ulcers;
dyspnea; urinary and bowel
incontinence; behavioral problems
(M0610); upper body dressing, toileting,
and ambulation functional limitations.
If we make the first assumption, that
the only cause of change in the national
CMI under the HH PPS was the
increased share of post-surgical patients
in the population of home health users,
then the national case-mix under the
HH PPS sample should have been
slightly below the CMI of the HH IPS
sample. This is because the CMI for
post-surgical patients is smaller than the
CMI for non-surgical patients, and
because even under the HH PPS the
share of post-surgical patients is a
minority of all patients. However, in
actuality, as stated in section II.A.2.b of
this proposed rule, the national CMI
increased by 0.099 between the HH IPS
sample and the 2003 HH PPS sample.
Post-surgical patients’ CMI grew
slightly faster than non-surgical
patients’ CMI over this period. This may
represent a change in the mix of postsurgical patients, or it may represent
stronger effects of changed coding
practices on post-surgical patients than
on non-surgical patients. If we make the
second assumption—that the growth
rate of post-surgical patients’ case mix
was the same as the growth rate of nonsurgical patients’ case mix—then the
increase in the national CMI should
have been marginally smaller than 0.099
(smaller by about one-half of 1 percent).
Because our second assumption caused
a very small reduction in the CMI
increase, we conclude that only a very
small portion of the substantial growth
in CMI might be attributable to having
more severe surgical patients under HH
PPS compared to HH IPS.
We believe one possible contributing
factor in the slightly faster growth in the
CMI for surgical patients was
uncertainty about how to assess the
healing status of a surgical wound. As
noted above, twice as many surgical
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wounds judged ‘‘most problematic’’ were
assigned a status of ‘‘not healing’’ under
the HH PPS than under the HH IPS.
Fifty percent more surgical wounds
were assigned a status of ‘‘early and
partial granulation,’’ under the HH PPS.
A recent clarification in the guidance for
assessing healing status is significant,
we believe, in understanding this
change. In July 2006 the Wound Ostomy
and Continence Nurses Society
(WOCN), a national source of expertise
in wound assessment, and one that CMS
encouraged agencies to consult, issued a
change in guidance on surgical wound
assessment. Before that time, criteria for
a status of ‘‘non-healing’’ in a wound
closed by primary intention were the
following: ‘‘incisional separation OR
incisional necrosis OR signs or
symptoms of infection OR no palpable
healing ridge’’ (WOCN Society OASIS
Guidance Document—Spring 2001).
Criteria for a status of ‘‘fully granulating/
healing’’ were: ‘‘incision wellapproximated with complete
epithelialization of incision; no signs or
symptoms of infection; healing ridge
well-defined.’’ The July 2006 revision
removed all references to a ‘‘healing
ridge’’ due to the lack of scientific
evidence supporting its use as a sign of
wound healing. Many surgical wounds
will not exhibit a healing ridge, though
the wound is actually healing. To the
extent that assessing clinicians paid
heightened attention to the nowoutdated WOCN guidance in adapting to
the HH PPS, it is likely that they applied
the pre-2006 criteria, with the result that
the national OASIS rate for the healing
status of surgical wounds indicated
more wounds ‘‘not healing’’ or at a stage
of ‘‘early and partial granulation.’’
In summary, based upon our above
discussion of review of the data on
OASIS items and our discussion of
reasons for coding change, we conclude
that growth in the national average CMI
reflects, to a very large extent, coding
practice changes against a background
of new financial incentives. The impact
of these forces is evidenced by mostly
incremental changes in home health
population rates of case-mix relevant
items and not to actual changes in
health status. Other than the increase in
reported numbers of surgical wound
patients, changes in numbers and
characteristics of wound care patients
documented on the OASIS were modest.
While there was substantially more use
of aggressive treatment plans involving
at least 10 therapy visits, the pattern of
decline in many ADL, IADL and other
scale ratings is suggestive of added
numbers of marginally limited patients,
not severely limited patients. Moreover,
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scale ratings for ADL measures, an
important part of the case-mix system,
were likely affected by the manual
changes noted above emphasizing that
safety is a consideration in determining
the rating. Lastly, we found that the
higher rate of reported post-surgical
patients does not contribute to CMI
change. Accordingly, as noted
previously, we are proposing to adjust
the national standardized 60-day
episode payment amount to reflect the
nominal change in the CMI.
4. Partial Episode Payment Adjustment
(PEP Adjustment) Review
In our July 3, 2000 final rule (65 FR
41128), we described a PEP adjustment
under the PPS. The PEP adjustment
provides a simplified approach to the
episode definition and accounts for key
intervening events in a patient’s care
defined as a beneficiary elected transfer,
or a discharge and return to the same
HHA that warrants a new start of care
for payment purposes, OASIS, and
physician certification of the new plan
of care. When a new 60-day episode
begins, the original national
standardized 60-day episode payment
rate is proportionally adjusted to reflect
the length of time the beneficiary
remained under the agency’s care before
the intervening event. The proportional
payment is the PEP adjustment.
The PEP-adjusted episode is paid
based on the span of days including
start of care date or first billable service
date through and including the last
billable service date under the original
plan of care before the intervening
event. The PEP-adjusted payment is
calculated by using the span of days
(first billable service date through the
last billable service date) under the
original plan of care as a proportion of
60. The proportion is then multiplied by
the original case-mix and wage-adjusted
national standardized 60-day episode
payment rate. This method of proration
in relation to the span of days between
the first and last billable service date
assumes that the rate of visits through
time is constant during the episode
period.
Since the July 2000 final rule, we
have received comments and
correspondence pertaining to the PEP
adjustment. These have guided our
research efforts since the HH PPS has
been in place. Through a contract with
Abt Associates, descriptive analysis has
been conducted on a large sample of
claims linked to OASIS assessments
from the first 3 years of the HH PPS in
an effort to better understand the patient
characteristics associated with PEPadjusted episodes and the
circumstances under which PEP-
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adjusted episodes occur. Analysis of
patient characteristics revealed no
appreciable differences between
patients in normal episodes and patients
in PEP episodes with regard to
conditions or clinical characteristics.
(Normal episodes are defined as home
health episodes of care that are not
subject to any of the payment systems
adjustments (for instance, LUPAs, PEPs,
SCICs).) The mix of visits for PEP
episodes is similar to that of normal
episodes.
Additionally, analysis of a 20 percent
sample of 2003 episodes showed that
approximately 3 percent of all episodes
were PEP-adjusted. Of those, three types
of PEP-adjusted episodes were
identified: approximately 55 percent of
PEP-adjusted episodes involved a
discharge and return to the same HHA;
about 42 percent involved transfers to
other agencies; and approximately 3
percent involved a move to managed
care. Regarding the circumstances under
which PEP-adjusted episodes occur,
analysis showed the incidence of
inpatient utilization during the 60 days
following the first day of a PEP-adjusted
episode was 14.5 percent which is lower
than the incidence during normal
episodes (21.4 percent). The lower
incidence of hospitalizations for
patients with PEP-adjusted episodes
may indicate that these patients are in
better health than the average home
health patient. Along with the patient
characteristics we examined, this seems
to suggest that patients experiencing
PEP episodes are not necessarily very
different from the overall population of
home health beneficiaries.
As part of our research efforts, we also
examined the different components that
make up PEP episodes. Our analysis
showed that PEP-adjusted episodes have
significantly shorter service periods on
average (approximately 23.4 days) than
all episodes other than LUPAs and SCIC
episodes (42.0 days). The average of
23.4 days was calculated by dividing
PEP episodes into their four
components. The number of days
between the start of the episode and the
first billable visit averaged 0.2 days, or
0.4 percent of a full 60-day episode. The
paid days, or the days between the first
billable and last billable visit days,
averaged 23.4 days or 38.9 percent of a
full 60-day episode. The number of days
between last billable visit to the new
episode from-date averaged 17.9 days,
or 29.9 percent of a full 60-day episode.
Finally, the number of days between the
from-date of the new episode from-date
to the first episode’s original day 60
averaged 18.5 days or 30.8 percent of a
full 60-day episode. Under the current
system, payment for a PEP episode is
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adjusted to reflect the paid days only
(23.4 days on average).
We further examined the number of
visits that occurred during PEP
episodes. We found that an average of
13.8 visits occur during PEP episodes.
We recognize that this average
represents 75 percent of the average
number of visits for normal episodes,
while the number of paid days
represents less than 40 percent of the
normal 60-day episode. Thus, the
average proration fraction is about 40
percent of the normal episode payment
while the number of visits is
approximately 75 percent of the number
delivered during the average normal
episode. Additionally, the average
number of minutes per visit during a
PEP episode is slightly longer than that
of a normal episode for most types of
visits. Both results provide evidence
that there is some front-loading of visits
compared to normal episodes, causing
PEP episodes to have a faster average
rate of visits during the span of days
used to prorate the episode payment.
Because the PEP adjustment proration
methodology does not take visit
occurrence into account, commenters
have argued that, PEP episodes appear
to be systematically ‘‘underpaid’’.
As we described in the July 3, 2000
final rule, the decision to use the span
of billable visit dates was made because
of the HHA’s involvement in decisions
influencing the intervening events for a
beneficiary who elected transfer or
discharge and returned to the same
HHA during the same 60-day episode
period. Agencies have some flexibility
in discharge decisions that affect the
likelihood of incurring a partial episode,
whether or not a hospital stay
intervenes. They also have indirect
influence on a beneficiary’s decision to
transfer to another home care provider
through the quality of care they provide.
Current data suggest that PEP episodes
are rare and, therefore, the current PEP
policy may be serving as a deterrent to
premature discharge. We believe that
the PEP adjustment is provided in a
manner that maintains the opportunity
for Medicare patients to choose the
provider with which they feel most
comfortable. Therefore, we are
proposing that the current system of
proportional payments based on billable
visit dates continue to be the payment
methodology for PEP episodes. It should
also be noted that in many cases, an
HHA receives payment for an additional
full episode which it might not have
received had the first episode not been
subject to a PEP adjustment. We will
continue to research the nature of HHA
resource use during and following PEP
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episodes, as well as explore alternative
methodologies for payment adjustment.
At this time, our analysis of PEP
episodes does not suggest a more
appropriate alternative payment policy.
We believe that many alternative
proration rules that we could devise
would likely introduce adverse
incentives into the HH PPS. For
example, a proposal to pay PEP
episodes amounts proportional to the
average visit accrual rate we observe for
PEP episodes would provide agencies
with a financial incentive to reduce
visits in the first few weeks of the
episode and/or to time the date
discharge in relation to the new,
prorated schedule of payments. For
many types of patients, such a delivery
pattern would likely worsen patient
outcomes. We would like to solicit
suggestions and comments from the
public on this matter to guide our
continued efforts to improve the PEP
adjustment policy.
5. Low-Utilization Payment Adjustment
(LUPA) Review
In our July 3, 2000 final rule (65 FR
4117), we described a low-utilization
payment to be implemented under the
HH PPS. The LUPA was established to
reduce the national standardized 60-day
episode payment rate regardless if the
episode is adjusted as a PEP adjustment
or SCIC adjustment when minimal
services are provided during a 60-day
episode. LUPAs are episodes with four
or fewer visits. Payments under a LUPA
episode are made on a per-visit basis by
discipline. For the July 2000 final rule,
the per-visit rates were determined from
the audited cost report sample we used
to design the HH PPS. (The same rates
were used in calculating the standard
episode amount.)
The per-visit amounts include
payment for (1) Non-routine medical
supplies (NRS) paid under a home
health plan of care, (2) NRS possibly
unbundled to Part B, and (3) a per-visit
ongoing OASIS reporting adjustment as
discussed in the July 3, 2000 final rule
(65 FR 41180). The LUPA payment rates
are not case-mix adjusted. As discussed
in the July 3, 2000 HH PPS final rule,
a standardization factor used to adjust
the LUPAs was calculated using
national claims data for episodes
containing four or fewer visits. This
standardization factor includes
adjustments only for the wage index.
The per-visit rates originally listed in
the July 2000 rule have been updated in
the same manner as the standard
episode amount. Additionally, the
payments are adjusted by the wage
index in the same manner as the
standard episode amount.
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As part of our ongoing research of the
HH PPS and to analyze the general
appropriateness of an adjustment for
low-utilization episodes, Abt Associates
analyzed a 20 percent sample of home
health episodes covering more than
three years of experience under the HH
PPS. The analysis file was the Fu
Associates analytical file linking OASIS
with home health claims. This allowed
the grouping of LUPAs into categories
for analysis of patient characteristics.
There were approximately 179,845
LUPA episodes in this file, accounting
for approximately 13 percent of
episodes.
The analysis revealed minor
differences between patients in LUPA
episodes and patients in normal
episodes. Although, overall, patients in
LUPA episodes on average had
somewhat lower clinical and functional
severity, a substantial number of
patients were in high severity groups.
LUPA episodes were also just as likely
as normal episodes to include a hospital
stay during the 60-day episode. We
believe that some LUPAs result from the
hospitalization of the patient before a
significant number of visits have been
delivered.
One indication from these data is that
LUPAs are serving as a low-end outlier
payment for certain episodes that incur
unexpectedly low costs. Other LUPAs
result from expected care patterns for
patients with conditions such as
neurogenic bladder and pernicious
anemia. The incidence of LUPAs has
changed little since the HH PPS began,
which suggests that LUPA episodes are
not excessively vulnerable to incentives
to manipulate care plans for payment
purposes. However, we continue to
believe that the distinction between
LUPAs and full episodes requires
sustained monitoring through medical
review and other activities. Further, we
are aware of the potential for
inappropriate admissions into LUPA
episodes among patients with
questionable medical necessity for home
health care.
Since the HH PPS went into effect, we
have received comments and
correspondence pertaining to the LUPA
policy. In particular, these have focused
on the suggestion that LUPA payment
rates do not adequately account for the
front-loading of costs in an episode.
Further, commenters suggested that
because of the small number of visits in
a LUPA episode, HHAs have little
opportunity to spread the costs of
lengthy initial visits over a full episode.
CMS has also received comments
regarding the appropriateness of the 4visit threshold for LUPAs. CMS is not
proposing to modify the 4-visit
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threshold for LUPA episodes in this
proposed rule. We did look at, and
consider, the 4-visit threshold and
possible alternatives to that threshold in
our analysis of LUPA episodes.
Increasing the 4-visit threshold to some
number greater than 4 would result in
a HH PPS in which we have an even
greater percentage of LUPA, which are
per-visit reimbursed episodes and could
be interpreted as a move closer toward
a per-visit payment system. This is not
the direction we want to go with a
bundled prospective payment system as
is the HH PPS. Conversely, decreasing
the 4-visit threshold to some number
less than 4 would result in an
overpayment of episodes, in that
episodes with 4 visits would then
receive a full episode payment. As a
result, we have concentrated our efforts
to address the payment of certain types
of LUPA episodes, in particular, LUPA
episodes occurring as the only episode
and circumstances where a LUPA
episode is the initial episode in a
sequence of adjacent episodes.
To examine this assertion, Abt
Associates conducted a descriptive
analysis of LUPA episodes. Of particular
interest are the findings pertaining to
the average visit length of LUPAs
occurring in the initial episode of a
sequence of adjacent episodes or
occurring as the only episode
(constituting approximately 59 percent
of all LUPA episodes). An examination
of visit log data predating the HH PPS,
used for the original Abt case-mix study
(July 2000 Final Rule), revealed that the
average visit length for nursing for an
initial assessment is, on average, twice
as long as the length for other nursing
visits. Likewise, an initial assessment
visit made by a physical therapist
averaged 25 percent more than other
physical therapy visits. These estimates
paralleled findings from a 2001
Government Accountability Office
(GAO) study that reported that the
OASIS added an average of 40 minutes
to a typical start of care visit. We found
that the average visit lengths in initial
and only episode LUPAs are 16 to 18
percent higher than the average visit
length in initial non-LUPA episodes. In
comparison, the average visit length for
LUPA episodes that occurred between
initial and ending episodes in a
sequence of adjacent episodes
(approximately 24 percent of all LUPAs)
or at the end of a sequence of adjacent
episodes (approximately 17 percent of
all LUPAs) is less than or about equal
to average visit lengths for
corresponding non-LUPA episodes.
The results of this data analysis
suggest that initial and only episode
LUPAs require longer visits, on average,
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than non-LUPA episodes, and that the
longer average visit length is due to the
start of care visit, when the case is
opened and the initial assessment takes
place. We agree with commenters to the
extent that these analyses of initial and
only episode LUPA episodes indicate
that payments for such episodes may
not offset the full cost of initial visits.
This is likely due to the fact that the
LUPA per-visit payment rates were
originally set based on the costs of an
average visit, not the costs of the subset
of visits incurred by patients receiving
four or fewer visits during an initial or
only episode LUPA; for these patients,
a large share of total visits comprises
initial visits. However, the comparisons
of average minutes per visit for LUPA
episodes occurring within or at the end
of a sequence of episodes do not support
a proposal for payment increases for
those types of LUPAs.
Based upon our initial review that
initial or only episode LUPAs may not
reflect the full costs incurred for the
visits delivered, we then conducted
further analysis to determine an
appropriate payment increase for initial
or only episode LUPAs. Analyzing a 10
percent sample of 2003 episodes, we
found that 75 percent of LUPA episodes
involved nursing without physical
therapy while 15 percent of LUPAs
involved physical therapy without
skilled nursing. Almost all of the
remaining 10 percent of episodes
involved a mix of physical therapy and
skilled nursing. Although the discipline
that delivered the initial visit may not
be identified in the sample file, for
deriving payment rates based upon our
analysis noted above, we have assumed
the share of initial assessment visits
from skilled nursing is 80 percent and
the share of initial assessment visits
from physical therapy is 20 percent. We
then used these percentages to calculate
the estimated value of 40 minutes added
to the initial visit for start of care
episodes. We relied upon the GAO
report noted above, as the basis for the
estimate of 40 minutes. For this
calculation, we multiplied the current
per-visit rate by the percentage increase
in the average visit length. The average
visit length was calculated from all nonLUPA episodes in the Abt sample file.
Specifically, we multiplied, for the
value of extra skilled nursing visits, the
LUPA base rate of $105.07 for skilled
nursing (trended forward from the
original rate of $98.85) by the
percentage over average skilled nursing
visit length (0.860215) and by the share
of initial assessment visits from skilled
nursing (0.80). The product was $72.31.
Next, we multiplied, for the value of
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extra physical therapy minutes, the
LUPA base rate of $114.89 for physical
therapy (trended forward to CY 2008
from the original rate of $108.08) by the
percentage over average physical
therapy visit length (0.858369) and by
the share of initial assessment visits
from physical therapy (0.20). The
product was $19.72. Finally, we
summed these weighted values to
calculate a total average value of $92.03
($72.31 + $19.72 = $92.03).
In the July 3, 2000, HH PPS final rule
(65 FR 41187), we adjusted the per-visit
rate by 1.05 to account for outlier
payments. Therefore, we are proposing
to multiply the $92.03 by 1.05 and then
reduce this amount to account for the
estimated percentage of outlier
payments as a result of the current FDL
ratio of 0.67 (see section II.A.8. of this
proposed regulation), resulting in an
amount of $92.63.
Given the findings from the
descriptive analysis of LUPA episodes
and total average value of excess visit
length for initial visits in certain LUPA
episodes, we propose an increase of
$92.63 for LUPA episodes that occur as
the only episode or the initial episode
during a sequence of adjacent episodes.
Again, as defined in section II.A.2 of
this proposed rule, a sequence of
adjacent episodes is defined as a series
of claims with no more than 60 days
between the end of one episode and the
beginning of the next episode (except
for episodes that have been PEPadjusted). In § 484.230, we are
proposing to add a third, fourth, and
fifth sentence after the second sentence
to define the term ‘‘sequence of adjacent
episodes’’ for the purpose of identifying
situations where the LUPA is the
beneficiary’s only episode or the initial
episode in a sequence of adjacent
episodes. We propose to pay an
additional low-utilization payment
adjustment LUPA episodes which are
either the only episode or the initial
episode in a sequence of adjacent
episodes, and note the additional
payment for such LUPA episodes will
be updated annually by the home health
market basket percentage increase. As
with the other components of the LUPA
methodology, this increase for situations
where a LUPA is the only episode or the
initial episode in a sequence of adjacent
episodes will be wage-adjusted. We
believe this increase allows HHAs fair
compensation for the cost of lengthier
start of care visits in LUPA episodes. To
maintain budget neutrality, we further
propose that the national standardized
60-day episode payment rate be
reduced. We determined the budget
neutral national standardized 60-day
episode payment rate that compensates
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for the extra payment of $92.63, as well
as for other proposed changes in this
proposed rule, from simulating the new
payment system on our 2003 claims
sample. The results are shown in the
section II. D.
We are soliciting comments on our
methodology for arriving at an
adjustment to achieve fair compensation
for the cost of lengthier start of care
visits in LUPA episodes. An alternative
methodology is basing the estimated
additional time on claims-based reports
of lengths of the first visit in initial and
only episode LUPAs. We expect to test
the adequacy of such an alternative
methodology using a large,
representative CY 2005 claims sample
that would be available before the final
rule. We are specifically soliciting
comments on alternative methodologies.
6. Significant Change in Condition
(SCIC) Review
The SCIC adjustment occurs when a
beneficiary experiences a SCIC during
the 60-day episode that was not
envisioned in the original plan of care.
In our final rule published July 3, 2000
in the Federal Register (65 FR 41128),
we established the SCIC adjustment to
be the proportional payment adjustment
reflecting the time both before and after
the patient experienced a SCIC during
the 60-day episode. In order to receive
a new case-mix assignment for purposes
of SCIC payment during the 60-day
episode, the HHA must complete an
OASIS and obtain the necessary
physician orders reflecting the
significant change in treatment in the
patient’s plan of care.
Currently, the SCIC adjustment is
calculated in two parts. The first part of
the SCIC adjustment reflects the
adjustment to the level of payment
before the significant change in the
patient’s condition during the 60-day
episode. The second part of the SCIC
adjustment reflects the adjustment to
the level of payment after the significant
change in the patient’s condition occurs
during the 60-day episode.
The first part of the SCIC adjustment
is determined by taking the span of days
(first billable service date through the
last billable service date) before the
patient’s SCIC as a proportion of 60
multiplied by the original episode
payment amount. The original episode
payment level is proportionally adjusted
using the span of time the patient was
under the care of the HHA before the
SCIC that required an OASIS, physician
orders indicating the need for a change
in the treatment plan, and the new casemix assignment for the remainder of the
60-day episode.
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The second part of the SCIC
adjustment reflects the time the patient
is under the care of the HHA after the
patient experienced a SCIC during the
60-day episode that required the new
case-mix assignment. The second part of
the SCIC adjustment is a proportional
payment adjustment reflecting the time
the patient will be under the care of the
HHA after the SCIC and continuing
until another significant change or until
the end of the 60-day episode. Once the
HHA completes the OASIS, determines
the new case-mix assignment, and
obtains the necessary physician change
orders reflecting the need for a new
course of treatment, the second part of
the SCIC adjustment begins. The second
part of the SCIC adjustment is
determined by taking the span of days
(first billable service date through the
last billable service date) after the
patient experiences the SCIC through
the balance of the 60-day episode (or
until the next significant change, if any)
as a proportion of 60 multiplied by the
new episode payment level resulting
from the significant change.
Since we proposed the SCIC
adjustment in October 1999 (64 FR
58134), we have received comments and
correspondence regarding the
appropriateness and the complexity of
the SCIC adjustment methodology.
These suggestions expressed concerns
that SCIC adjustments may be difficult
to apply appropriately. Additionally,
analysis of HHA margins using a sample
of approximately 2,500 cost reports
suggested that SCIC episodes did not
necessarily account for the cost
associated with a patient in a SCIC
episode. These concerns guided our
descriptive analysis of SCIC episodes
and our investigation of possible
alternatives to SCIC adjustment.
The SCIC policy was designed and
implemented primarily to protect HHAs
from receiving a lower, inadequate
payment for a patient that unexpectedly
got worse and became more expensive
to the agency during the course of a 60day episode. While it is also possible
that a patient could become
unexpectedly better, resulting in a
patient needing far fewer resources and
costing the agency less, such instances
were expected to be few. For patients
who experienced an unexpected adverse
significant change in condition, but the
agency would actually receive lower
payments when applying the
computation for deriving a SCIC
payment, agencies were instructed that
they did not have to report a SCIC.
Abt Associates, under contract to
CMS to conduct analysis and simulation
of refinements to HH PPS, first
conducted several descriptive analyses
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examining the payment accuracy for
SCIC-adjusted episodes. As with the
LUPA, they used the Fu Associates’
large analytic file consisting of home
health claims linked to OASIS. Analyses
included examination of trends in rates
and other utilization statistics relating to
SCIC episodes, OASIS characteristics for
SCIC episodes, and estimation of
margins for SCIC episodes.
Results of the analyses indicated that
SCIC episodes have been declining
since HH PPS began. Approximately 3.7
percent of episodes were reported as
SCIC episodes in the first quarter of the
HH PPS (October 1, 2000, to December
31, 2000); they decreased to 2.1 percent
of episodes by the first quarter of CY
2004. SCIC episodes tended to be longer
than the average episode (excluding
LUPAs), and were more likely to occur
in facility-based agencies and rural
agencies. There was some evidence that
the percentage of episodes in the highest
category of the services utilization
dimension of the case-mix system
increased for SCIC episodes over time.
SCIC episodes had a higher likelihood
of using at least 10 therapy visits, and
this excess grew over time. Overall,
patients experiencing SCIC episodes
differed little in terms of case-mix
characteristics from the average home
health patient, except for a higher
incidence of dyspnea, ADL limitations,
and those recently discharged from
acute care.
The margin analysis suggested that,
on average, SCIC episodes had negative
margins, even though the SCIC payment
policy allows agencies to avoid
declaring a SCIC if an episode that
experiences an adverse significant
change in condition would be paid less
than the original case-mix adjusted
payment. One reason for the negative
margin estimate appears to be that in
some cases agencies inappropriately
applied the SCIC adjustment for patients
experiencing a significant adverse
change, when in doing so the agency
actually received lower payments for
those patients. Also, the proportional
payment policy, which reduces
payment in proportion to the number of
days between the last visit before the
significant change in condition and the
first visit following the significant
change, results in increasingly lower
payments as the number of days
between the last and next visit
increases. In contrast, a normal episode
payment is not affected by periods when
visits do not occur.
As noted above, we believe that HHAs
have had difficulty in interpreting when
to apply the SCIC adjustment policy.
Agencies also reported additional
administrative burdens from adhering to
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the policy. Furthermore, there has been
a 2 percent decline in use of the SCIC
adjustments since the implementation
of the HH PPS. We have received
comments that stated eliminating the
SCIC policy altogether might be better
than having a SCIC policy that is
difficult to understand and adhere to.
Given these concerns, we decided to
focus our analysis on simulating the
impact of eliminating the SCIC
adjustment policy. We performed this
simulation by repricing SCIC claims to
use the first HHRG during the episode
for determining the payment, and
eliminating any proration. We then
compared the total expenditures before
and after making this change.
The results of eliminating the SCIC
policy suggested little impact on
outlays—an increase of 0.5 percent of
total payments. The difference in total
payments was less than one-half of one
percent for all categories of agencies
(urban versus rural, by size, and
ownership).
Based on these findings, we are
proposing to eliminate the SCIC
adjustment from the HH PPS.
Specifically, we are proposing in
§ 484.205 to remove paragraph (e)
concerning the SCIC adjustment policy
from the HHA PPS. We are also
proposing to redesignate paragraph (f) as
paragraph (e). In addition, we are
proposing to amend our regulations at
§ 484.205 by removing paragraph (a)(3)
and redesignating paragraph (a)(4) as
paragraph (a)(3). Furthermore, we
proposing to revise paragraph (b)
introductory text to read as follows: ‘‘(b)
Episode payment. The national
prospective 60-day episode payment
represents payment in full for all costs
associated with furnishing home health
services previously paid on a reasonable
cost basis (except the osteoporosis drug
listed in section 1861(m) of the Act as
defined in section 1861(kk) of the Act)
as of August 5, 1997 unless the national
60-day episode payment is subject to a
low-utilization payment adjustment set
forth in § 484.230, a partial episode
payment adjustment set forth at
§ 484.235, or an additional outlier
payment set forth in § 484.240. All
payments under this system may be
subject to a medical review adjustment
reflecting beneficiary eligibility, medical
necessity determinations, and HHRG
assignment. DME provided as a home
health service as defined in section
1861(m) of the Act continues to be paid
the fee schedule amount.’’ We are also
proposing to remove § 484.237 relating
to the methodology used for the
calculation of the significant change in
condition payment adjustment.
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Episodes that are currently SCIC
adjusted would be treated as normal
episodes and will receive payment for
the entire 60-day period based on the
initial, and only, HHRG code. The
national standardized 60-day episode
payment rate in section II.A.2.c of the
proposed rule takes into account this
proposed change in SCIC policy and is,
therefore, slightly lower than it would
have been without proposing this
change. We believe the elimination of
the SCIC adjustment policy would have
a minor impact on home health agency
operations and revenues, because SCIC
episodes are very infrequent. Our
estimate of the cost of eliminating the
SCIC policy, implemented in a budget
neutral manner as a reduction to the
national standardized 60-day payment
rate, is presented in section II.D and
reported in the accompanying table
(Table 23b). The estimated reduction is
$15.71. We discussed this proposal at a
meeting with the contractor’s TEP in
March 2006. We received favorable
feedback noting that our proposal would
be an appropriate simplification of the
HH PPS.
7. Non-Routine Medical Supply (NRS)
Amounts Review
As described in the HH PPS final rule
published in the Federal Register (65
FR 41180) and modified in the June 1,
2001, correction notice (66 FR 32777),
the NRS amounts included in the perepisode payment and initially paid on a
reasonable cost basis under a home
health plan of care, were calculated by
summing the NRS costs using audited
cost reports from 1997. The NRS costs
for all the providers in that audited cost
report sample were then weighted to
represent the national population and
updated to FY 2001. That weighted total
was divided by the number of episodes
for the providers in the audited cost
report sample, to obtain the average cost
per episode of NRS reported as costs on
the cost report. This amount was $43.54.
The possible unbundled NRS, billed
under Medicare Part B and not reflected
in on the home health cost report, were
also included in the HH PPS national
standardized 60-day episode payment
rate by summing the allowed charges for
176 Healthcare Common Procedure
Coding System (HCPCS) codes,
reflecting NRS codes, in CY 1998 for
beneficiaries under a home health plan
of care. That total was divided by the
total number of episodes in CY 1998
from the episode database, to obtain the
average cost of unbundled NRS per
episode. This amount was $6.08.
The total of the two amounts $43.54
and $6.08, or $49.62, was added to the
national total prospective payment
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amount per 60-day episode for CY 2001
(before standardization). The
standardized amount has been
subsequently updated annually.
Since the proposal and adoption of
this methodology for payment of NRS,
we have received comments expressing
concern about the cost of supplies for
certain patients with ‘‘high’’ supply
costs. In particular, commenters were
concerned about the adequacy of
payment for some patients with
pressure ulcers, stasis ulcers, other
ulcers, wounds, burns or trauma,
cellulitis, and skin cancers.
In general, NRS use is unevenly
distributed across episodes of care in
home health. While most patients do
not use NRS, many use a small amount,
and a small number of patients use a
large amount of NRS. The payment for
NRS included in the HH PPS
standardized payment rate does not
reflect this distributional variation.
Furthermore, the current case-mix
adjustment of the standardized amount,
which effectively adjusts the NRS
payment we originally included, may
not be the most appropriate way to
account for NRS costs.
In order to investigate the
performance of the payment
methodology for NRS and to explore an
approach to case-mix adjustment of the
NRS component of the payment, our
contractor, Abt Associates, performed
several analyses of the current system.
The analysis file was constructed by Abt
Associates from a sample of 2001 cost
reports, which were needed to
determine cost-to-charge ratios. The cost
reports were then linked to claims. The
claims came from an analytic file
constructed by Fu Associates that links
home health claims and OASIS.
The cost report sample was analyzed
to detect or correct extremely
implausible cost data (that is, if cost
report erroneously inverted ratio of
costs to charges, this was corrected).
Many cost reports were dropped after
this initial analysis because the cost-tocharge ratio for nonroutine medical
supplies was zero. Then, we retrieved
Medicare claims for patients admitted to
the agencies with remaining cost
reports, in order to ensure that the cost
report totals for non-routine supplies
were consistent with total charges for
non-routine supplies that we obtained
from the provider’s claims. Additional
cost reports were dropped from the
sample at this step. At the end of this
process, from an initial sample of 2,864
cost reports, 1,207 cost reports were
considered usable.
The cost report data were then merged
with a random sample of data from
496,237 ‘‘normal’’ home health episodes
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from the same set of agencies used in
the sample data. Normal episodes were
defined as episodes that did not include
additional adjustments such as LUPAs
or PEP adjustments. ‘‘Cost-to-charge’’
ratios generated from the cost reports
were used to estimate NRS costs for the
episodes in the sample.
The exploration of case-mix
adjustment for NRS costs was
conducted in a manner similar to the
way Abt Associates developed the
initial case-mix model. We created
regression equations that used OASIS
measures to predict episode-level NRS
costs. One equation used the current
case-mix variables. This equation
explained approximately 10 percent of
the variation in NRS costs in this data
sample. This provided a baseline against
which to judge the performance of set
variables that differ from the set used in
the current HH PPS case-mix system.
Models were developed after creating
additional variables from OASIS items
and targeting certain conditions
expected to be predictors of NRS use
based on clinical considerations. Many
of these conditions were skin-related.
The end result of the model
exploration process was two versions of
the ‘‘best-fitting’’ variable set. This best
fitting variable set consisted of more
than two dozen indicators for diagnoses,
wound conditions, and certain
prosthetics captured on the OASIS. The
variables could be used as the basis for
improved prediction of NRS costs.
These variables represent measurable
conditions that have been the subject of
extensive education by CMS in its
administration of the OASIS system,
and by others such as the ICD–9–CM
coding committee with its interest in
coding accuracy. Therefore, we believe
this variable set would be the basis for
a methodology to account for NRS costs
that is feasible to administer and does
not create significant new payment
concerns.
The first alternative model using the
best-fitting variables divided episodes
into two episode groups, with one group
containing first and second episodes
(early), and the second containing third
and later episodes (later). The second
alternative model does not distinguish
between early and later episodes. These
‘‘best fit’’ models were then used to
construct a scoring system. Each
condition in the best-fit models was
assigned one point for each $5
increment in NRS cost as determined
from the model results. For example, if
a variable representing a clinical
condition predicted a $50 increase in
cost, an episode with that variable
would be given 10 points. We summed
the condition-specific scores for each
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episode. We then placed those sums
into five severity groups. For the model
that separated early from later episodes
we defined 10 severity groups, five for
early episodes and 5 for later episodes.
This system explained about 13.7
percent of NRS cost variation in the
sample. The model that pooled all
episodes had 5 severity groups and
explained 13.0 percent of the variation
in NRS costs.
We note, because there is a limited
performance advantage of the twoepisode group model over the single
model, we are proposing to use the
simpler model that pays all episodes,
whether early or later episodes, using
the same set of severity groups. Table 11
shows the relative weights and payment
weights for the five severity levels in the
proposed NRS model, and Table 12a
sets forth the NRS scores for the fivegroup model. We will continue to
evaluate the ICD–9–CM codes listed for
each group (Table 12b) to ensure as
much as possible that condition-related
scores are based on ICD–9–CM codes
that are specific, unambiguous, and use
diagnostic criteria widely accepted
within the medical community. In
addition to refining the list of
conditions contained within each
diagnostic group (Table 12b), we intend
to continue to study ways of improving
the statistical performance of all the
variables represented in Table 12a. We
solicit public comment to help inform
our efforts. We also intend to update the
data base upon which our payment
proposal for NRS is based. Our ability
to update the data files will depend on
the quality of data available in claims
and cost reports for succeeding years. If
the data are not found to be sufficiently
complete and accurate, we would use
the existing data for any final revisions
that result from further analysis and
public comments.
In addition to computing the R-square
statistic as a summary of the system’s
performance, we examined the
improvements in payment accuracy for
NRS costs per episode, according to
selected characteristics of the episode.
The magnitude of change is difficult to
report with a high degree of certainty
because of the limited data resources
available for these analyses.
We found that under our proposal
NRS payments for episodes reporting no
NRS charges on the episode claim
would better reflect the absence of NRS
costs incurred in such an episode, by
having their payment for NRS reduced.
For the remaining claims—those
reporting any amount of NRS costs—on
average we estimate that NRS payments
would come significantly closer to their
estimated NRS costs under the proposed
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new system of accounting for NRS. For
the subgroups of episodes with the
OASIS conditions listed in Table 11,
under our proposal, the difference
between the estimate of average NRS
costs incurred and the proposed amount
to account for those NRS costs would
decrease in a similar manner, with some
differences becoming even smaller.
However, our ability to predict NRS
costs remains limited. We have not yet
developed a statistical model that has
performed with a high degree of
predictive accuracy. Some of the
reasons for this result include the
limited data available to model NRS
costs, and the likelihood that OASIS
does not have any measures available
for some kinds of NRS. Nevertheless, we
are proposing to change the payment
system because the majority of episodes
do not incur any NRS costs, and the
current payment system
overcompensates these episodes.
Further, we believe the proposed
approach is appropriate to the extent
that we have developed a way to
account for NRS costs that is based on
measurable conditions, is feasible to
administer, and offers HHAs some
protection against episodes with
extremely high NRS costs. As we noted
earlier in this section, we will continue
to look into ways to improve the
predictive model we are proposing to
account for NRS costs. We solicit
suggestions and comments from the
public on this matter.
In the course of conducting the NRS
analysis, we discovered a possible
source of error in reporting on claims.
Data analysis suggested that enteral
nutrition patients were incurring higher
NRS costs than average and, in our
model, could be assigned a moderate
score for NRS cost. However, we did not
find evidence from our analyses that
any category of NRS other than enteral
supplies would systematically account
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for the NRS finding in the model for
enteral nutrition patients. These
patients often have a very compromised
health status, including skin and other
conditions that are already accounted
for in our model. Further, we explored
other possibilities to determine if
information was missing from the
model. If available, such information
could be added to the model to explain
the scores we found for the enteral
nutrition variable. However, we did not
gather any information that produced
any additional hypotheses. An
important remaining hypothesis is that
some providers are reporting enteral
supplies charges for these patients in
error; in fact, at least one large provider
has indicated this was the case. We are
proposing to exclude the enteral
nutrition variable from the model to
ensure compliance with the statute and
regulations governing enteral nutrition,
as noted below; but, we welcome
comments on this issue.
As we stated in the final HH PPS rule
dated July 3, 2000 (65 FR 41139), ‘‘Part
B services such as parenteral or enteral
nutrition are neither currently covered
as home health services nor defined as
non-routine medical supplies.
Parenteral or enteral nutrition would
therefore not be subject to the
requirements governing home health
consolidated billing.’’
If the patient requires medical
supplies that are currently covered and
paid for under the Medicare home
health benefit during a certified episode
under HH PPS, the billing for those
medical supplies falls under the
auspices of the HHA due to the
consolidated billing requirements. As
parenteral and enteral nutrition are not
covered or paid for under the Medicare
home health benefit, they should be
billed separately by the supplier or
provider. Because we assumed that
some providers are reporting these
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supplies in error, we believe it is
important to again note the Medicare
coverage requirements for parenteral
and enteral nutrition to prevent any
potential future reporting errors.
Medicare’s coverage guidelines for
enteral nutrition state: ‘‘Coverage of
nutritional therapy as a Part B benefit is
provided under the prosthetic device
benefit provision which requires that
the patient must have a permanently
inoperative internal body organ or
function thereof. Therefore, enteral and
parenteral nutritional therapy is not
covered under Part B in situations
involving temporary impairments.’’ The
National Coverage Decision (NCD)
provides guidance in applying the
definition of temporary impairment:
‘‘Coverage of such therapy, however,
does not require a medical judgment
that the impairment giving rise to the
therapy will persist throughout the
patient’s remaining years. If the medical
record, including the judgment of the
attending physician, indicates that the
impairment will be of long and
indefinite duration, the test of
permanence is considered met.’’ (See
Medicare National Coverage
Determinations [NCD] Manual, Pub.
100–03, Section 180.2, Chapter 1 (Part
3). Section 1842(s) of the Act
implements the fee schedule for
parenteral and enteral nutrition (PEN)
nutrients, equipment and supplies. The
general payment rules for PEN effective
on or after January 1, 2002, are
stipulated in § 414.102 and § 414.104.
The following is the list of HCPCS
codes which may be used to claim
reimbursement for enteral nutrition.
Providers may claim reimbursement for
it on the UB–92 claim form if they
report the appropriate HCPCS code and
revenue center code. Payment is made
by the RHHI under the Medicare Fee
Schedule.
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conditions included as NRS, we now
describe our proposed revision to the
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payment methodology to account for
NRS costs. We propose to account for
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NRS costs based on five severity groups
and a national conversion factor. Table
12a shows the condition-specific scores
derived from the NRS model. Table 12b
shows the ICD–9–CM diagnosis codes
used to define conditions that are based
on diagnosis codes. The sum of scores
for each episode is then used to group
episodes into one of five severity
groups, as follows: Group 0 if the sum
is zero; group 1 for 1 to 16; group 2 for
17 to 34; group 3 for 35 to 59; and group
4 for 60 or more. We defined these five
scoring levels from examining the
distribution of scores in our analysis
sample. Most of the episodes (64
percent, see Table 11) fell into the group
with a score of zero (that is, no
conditions listed in Table 12b were
reported on the OASIS assessment). For
purposes of payment, relative weights
were calculated for each severity group
based on the estimated average NRS
cost, divided by the overall average in
the sample. The relative weights are
listed below in Table 11.
To derive payment, each relative
weight is multiplied by the conversion
factor. We calculated the conversion
factor by inflating the original allowance
included in the episode base rate
($49.62) by the total percentage increase
since October 2000 using the statutory
market basket updates. We take the
inflated conversion factor of $53.91 and
multiply it by 1.05 to account for the
initial outlier payment noted in the July
3, 2000 final rule (65 FR 41187). We
then take that product and multiply it
by 0.958614805 to account for the
estimated percentage of outlier
payments as a result of the current FDL
ratio of 0.67. To further adjust for the
nominal change in case-mix, we
multiply the $54.26 by 0.9725 for a
proposed NRS conversion factor of
$52.77. Because the market for most
NRS is national, we do not propose to
have a geographic adjustment to the
conversion factor. We plan to continue
to monitor NRS costs to determine if
any adjustment for the NRS weights is
warranted in the future.
We determined the budget-neutral
national standardized 60-day episode
payment rate that compensates for the
payments for NRS under the proposed
new case-mix-adjusted HH PPS as part
of the simulation of all proposed
changes on our 2003 claims sample. The
results are shown in section II.D.
For an example of calculating an HH
PPS payment using the NRS proposed
payment methodology see section II.D.
We do not propose to apply the fivelevel NRS payment approach to LUPA
episodes. In the original design of the
HH PPS, $1.94 was built into the pervisit rates used to pay for visits in a
LUPA episode. This amount was the
sum of $1.71, the average cost per visit
for NRS reported as costs on the cost
report, and $.23, the average cost per
visit for NRS possibly unbundled and
billed separately to Part B and
reimbursed on the fee schedule. Recent
analysis shows that NRS charges for
non-LUPA episodes are almost 3 times
higher than that for LUPA episodes. In
general, approximately 1 in 5 LUPAs
report NRS while 1 in 3 non-LUPA
episodes report NRS. Our proposal is to
redistribute the $53.96 currently paid to
all non-LUPA episodes. Given that
LUPA episodes, by nature, are of
extremely low visit volume, we do not
propose to redistribute that $1.94 now
paid to LUPA episodes. We believe an
attempt to develop a model for
redistributing the small amount of NRS
payments ($1.94) paid to LUPA
episodes would be unproductive.
Furthermore, we are also concerned
that additional payment for LUPAs to
account for NRS costs could promote
increases in medically unnecessary
home health episodes. In proposing
refinements for LUPA payments, as
discussed in section II.A.5 of this
proposed rule, we are aware of the
potential for increases in medically
unnecessary LUPA episodes that could
result from our proposal for increased
LUPA payment for only or initial LUPA
episodes. Providing for additional NRS
payments for such LUPAs could only
adversely add to this potential.
Consequently, we are not proposing any
additional payments for NRS costs for
LUPA episodes. However, we are
specifically soliciting comment on
alternative approaches for NRS payment
in LUPAs.
We also considered proposing an
outlier policy for NRS costs, but we
believe one is not administratively
feasible at this time. An outlier policy
for NRS costs would depend on having
an infrastructure, including a reporting
system for the extensive range of
nonroutine supplies used in home
health care, and a basis for assigning
allowable costs for those supply items.
At this time, this kind of infrastructure
is not sufficiently developed. Many
types of NRS cannot be coded under the
existing reporting system, the HCPCS
system, and reliable cost data are
limited. Therefore, at this time, we also
believe an outlier policy for NRS cost
would be premature. We also recognize
the additional administrative burdens
on agencies that would exist under such
an outlier policy.
While we are not proposing an outlier
policy for NRS costs, we nonetheless
urge agencies to provide cost data on
cost reports and charge data on all
claims (including LUPA claims) with
the utmost precision for possible future
use in developing payment proposals
for NRS under the HH PPS.
TABLE 11.—PROPOSED RELATIVE WEIGHTS FOR NON-ROUTINE MEDICAL SUPPLIES
Percentage of
episodes
Severity level
0
1
2
3
4
.......................................................................................................................
.......................................................................................................................
.......................................................................................................................
.......................................................................................................................
.......................................................................................................................
Points
(scoring)
63
17
12
5
3
0
1–16
17–34
35–59
60+
Relative
weight
0.2456
1.0356
2.0746
4.0776
6.9612
Payment
amount
$12.96
54.65
109.48
215.17
367.34
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Note: Proposed conversion factor = $52.77.
TABLE 12a.—NRS CASE-MIX ADJUSTMENT VARIABLES AND SCORES
Description
1
2
3
4
..........
..........
..........
..........
Score
SELECTED SKIN CONDITIONS:
Primary diagnosis = Anal fissure, fistula and abscess ..............................................................................................................
Primary diagnosis = Cellulitis and abscess ...............................................................................................................................
Primary diagnosis = Gangrene ..................................................................................................................................................
Primary diagnosis = Malignant neoplasms of skin ....................................................................................................................
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13
11
16
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TABLE 12a.—NRS CASE-MIX ADJUSTMENT VARIABLES AND SCORES—Continued
Description
5 ..........
6 ..........
7 ..........
8 ..........
9 ..........
10 ........
11 ........
12 ........
13 ........
14 ........
15 ........
16 ........
17 ........
18 ........
19 ........
20 ........
21 ........
22 ........
23 ........
24 ........
25 ........
26 ........
27 ........
28 ........
29 ........
30 ........
31 ........
32 ........
33
34
35
36
37
........
........
........
........
........
Score
Primary diagnosis = Non-pressure and non-stasis ulcers .........................................................................................................
Primary diagnosis = Other infections of skin and subcutaneous tissue ....................................................................................
Primary diagnosis = Post-operative Complications 1 ................................................................................................................
Primary diagnosis = Post-operative Complications 2 ................................................................................................................
Primary diagnosis = Traumatic Wounds and Burns ..................................................................................................................
Other diagnosis = Anal fissure, fistula and abscess .................................................................................................................
Other diagnosis = Cellulitis and abscess ...................................................................................................................................
Other diagnosis = Gangrene ......................................................................................................................................................
Other diagnosis = Non-pressure and non-stasis ulcers ............................................................................................................
Other diagnosis = Other infections of skin and subcutaneous tissue .......................................................................................
Other diagnosis = Post-operative Complications 1 ...................................................................................................................
Other diagnosis = Post-operative Complications 2 ...................................................................................................................
Other diagnosis = Traumatic Wounds and Burns .....................................................................................................................
M0450 = 1 pressure ulcer, stage 1 or 2 ....................................................................................................................................
M0450 = 2 or 3 pressure ulcers, stage 1 or 2 ...........................................................................................................................
M0450 = 4+ pressure ulcers, stage 1 or 2 ................................................................................................................................
M0450 = 1 or 2 pressure ulcers, stage 3 or 4 ...........................................................................................................................
M0450 = 3 pressure ulcers, stage 3 or 4 ..................................................................................................................................
M0450 = 4+ pressure ulcers, stage 3 or 4 ................................................................................................................................
M0450 = 5+ pressure ulcers, stage 3 or 4 ................................................................................................................................
M0450e = 1(unobserved pressure ulcer(s)) ..............................................................................................................................
M0476 = 2 (status of most problematic stasis ulcer: early/partial granulation) .........................................................................
M0476 = 3 (status of most problematic stasis ulcer: not healing) ............................................................................................
M0488 = 3 (status of most problematic surgical wound: not healing) ......................................................................................
M0488 = 2 (status of most problematic surgical wound: early/partial granulation) ...................................................................
OTHER CLINICAL FACTORS:
M0550 = 1 (ostomy not related to inpt stay/no regimen change) .............................................................................................
M0550 = 2 (ostomy related to inpt stay/regimen change) .........................................................................................................
Any ‘‘Selected Skin Conditions’’ (see rows 1 to 29 above) AND M0550=1(ostomy not related to inpt stay/no regimen
change).
Any ‘‘Selected Skin Conditions’’ (see rows 1 to 29 above) AND M0550=2 (ostomy related to inpt stay/regimen change) .....
M0250 (Therapy at home) =1 (IV/Infusion) ...............................................................................................................................
M0470 = 2 or 3 (2 or 3 stasis ulcers) ........................................................................................................................................
M0470 = 4 (4 stasis ulcers) .......................................................................................................................................................
M0520 = 2 (patient requires urinary catheter) ...........................................................................................................................
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19
32
22
16
9
6
11
8
7
15
15
7
12
20
31
41
75
80
143
18
18
28
18
5
21
35
24
8
11
17
34
17
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*Note: ‘‘ICD–9–CM Official Guidelines for
Coding and Reporting’’ dictate that a threedigit code is to be used only if it is not
further subdivided. Where fourth-digit
subcategories and/or fifth-digit
subclassifications are provided, they must be
assigned. A code is invalid if it has not been
coded to the full number of digits required
for that code. Codes with three digits are
included in ICD–9–CM as the heading of a
category of codes that may be further
subdivided by the use of fourth and/or fifth
digits, which provide greater detail. The
category codes listed in Table 12b include all
the related 4- and 5-digit codes.
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8. Outlier Payment Review
Section 1895(b)(5) of the Act allows
for the provision of an addition or
adjustment to the regular 60-day casemix and wage-adjusted episode
payment amount in the case of episodes
that incur unusually large costs due to
patient home health care needs. This
section further stipulates that total
outlier payments in a given CY may not
exceed 5 percent of total projected
estimated HH PPS payments.
In the July 2000 final rule, we
described a method for determining
outlier payments. Under this system,
outlier payments are made for episodes
whose estimated cost exceeds a
threshold amount. The episode’s
estimated cost is the sum of the national
wage-adjusted per-visit payment
amounts for all visits delivered during
the episode. The outlier threshold for
each case-mix group, PEP adjustment, or
total SCIC adjustment is defined as the
national standardized 60-day episode
payment rate, PEP adjustment, or total
SCIC adjustment for that group plus a
fixed dollar loss (FDL) amount. Both
components of the outlier threshold are
wage-adjusted.
The wage-adjusted FDL amount
represents the amount of loss that an
agency must experience before an
episode becomes eligible for outlier
payments. The FDL is computed by
multiplying the wage-adjusted national
standardized 60-day episode payment
amount by the FDL ratio, which is a
proportion expressed in terms of the
national standardized episode payment
amount. The outlier payment is defined
to be a proportion of the wage-adjusted
estimated costs beyond the wageadjusted threshold. The proportion of
additional costs paid as outlier
payments is referred to as the loss-
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sharing ratio. The FDL ratio and the
loss-sharing ratio were selected so that
the estimated total outlier payments
would not exceed the 5 percent level.
For a given level of outlier payments,
there is a trade-off between the values
selected for the FDL ratio and the losssharing ratio. A high FDL ratio reduces
the number of episodes that may receive
outlier payments, but makes it possible
to select a higher loss-sharing ratio and,
therefore, increase outlier payments for
outlier episodes. Alternatively, a lower
FDL ratio means that more episodes
may qualify for outlier payments, but
outlier payments per episode must be
lower. As a result of public comments
on the October 28, 1999 proposed rule,
and in our July 2000 final rule, we made
the decision to attempt to cover a
relatively high proportion of the costs of
outlier cases for the most expensive
episodes that would qualify for outlier
payments within the 5 percent
constraint.
We chose a value of 0.80 for the losssharing ratio, which is relatively high,
but preserves incentives for agencies to
attempt to provide care efficiently for
outlier cases. It was also consistent with
the loss-sharing ratios used in other
Medicare PPS outlier policies. Having
made this decision, we estimated the
value of the FDL ratio that would yield
estimated total outlier payments that
were projected to be no more than 5
percent of total HH PPS payments. The
resulting value for the FDL ratio was
1.13.
When the data became available, we
performed an analysis of CY 2001 home
health claims data. This analysis
revealed that outlier episodes
represented approximately 3 percent of
total episodes and 3 percent of total HH
PPS payments. Additionally, we
performed the same analysis on CY
2002 and CY 2003 home health claims
data and found the number of outlier
episodes and payments held at
approximately 3 percent of total
episodes and total HH PPS payments,
respectively. Based on these analyses
and comments we received, we decided
that an update to the FDL ratio would
be appropriate.
To that end, for the October 2004 final
rule, we performed data analysis on CY
2003 HH PPS analytic data. The results
of this analysis indicated that a FDL
ratio of 0.70 is consistent with the
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existing loss-sharing ratio of 0.80 and a
projected target percentage of estimated
outlier payments of no more than 5
percent. Consequently, we updated the
FDL ratio from the initial ratio of 1.13
to the FDL ratio of 0.70. Our analysis
showed that reducing the FDL ratio
from 1.13 to 0.70 would increase the
percentage of episodes that qualified for
outlier episodes from 3.0 percent to
approximately 5.9 percent. A FDL ratio
of 0.70 also better met the estimated 5
percent target of outlier payments to
total HH PPS payments. We believed
that this updated FDL ratio of 0.70
preserved a reasonable degree of cost
sharing, while allowing a greater
number of episodes to qualify for outlier
payments.
Our CY 2006 update to the HH PPS
rates (70 FR 68132) changed the FDL
ratio from 0.70 to 0.65 to allow even
more home health episodes to qualify
for outlier payments and to better meet
the estimated 5 percent target of outlier
payments to total HH PPS payments.
For the CY 2006 update, we used CY
2004 home health claims data.
In our CY 2007 update to the HH PPS
rates (71 FR 65884) we again changed
the FDL ratio from 0.65 to 0.67 to better
meet the estimated 5 percent target of
outlier payments to total HH PPS
payments. For the CY 2007 update, we
used CY 2005 home health claims data.
Under the HH PPS, outlier payments
have thus far not exceeded 5 percent of
total HH PPS payments. However,
preliminary analysis shows that outlier
payments, as a percentage of total HH
PPS payments, have increased on a
yearly basis. With outlier payments
having increased in recent years, and
given the unknown effects that the
proposed refinements of this rule may
have on outliers, we are proposing to
maintain the FDL ratio of 0.67. By
maintaining the FDL ratio of 0.67, we
believe we will continue to meet the
statutory requirement of having an
outlier payment outlay that does not
exceed 5 percent of total HH PPS
payments, while still providing for an
adequate number of episodes to qualify
for outlier payments. Some preliminary
analysis shows the FDL ratio could be
as low as 0.42 in a refined HH PPS. We
believe that analysis of more recent data
could indicate that a change in the FDL
ratio is appropriate. Consequently for
the final rule, we will rely on the latest
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data and best analysis available at the
time to estimate outlier payments and
update the FDL ratio if appropriate.
Because payment for NRS was
included in the base rate of the national
standardized 60-day episode payment
rate, under the refined system proposed
in this proposed rule, both the proposed
national standardized 60-day episode
payment rate and the proposed
computed NRS amount contribute
towards reaching the outlier threshold
in the outlier payment calculation.
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B. Rebasing and Revising of the Home
Health Market Basket
1. Background
Section 1895(b)(3)(B) of the Act, as
amended by section 701(b)(3) of the
MMA, requires the standard prospective
payment amounts to be adjusted by a
factor equal to the applicable home
health market basket increase for CY
2008.
Effective for cost reporting periods
beginning on or after July 1, 1980, we
developed and adopted an HHA input
price index (that is, the home health
‘‘market basket’’). Although ‘‘market
basket’’ technically describes the mix of
goods and services used to produce
home health care, this term is also
commonly used to denote the input
price index derived from that market
basket. Accordingly, the term ‘‘home
health market basket’’ used in this
document refers to the HHA input price
index.
The percentage change in the home
health market basket reflects the average
change in the price of goods and
services purchased by HHAs in
providing an efficient level of home
health care services. We first used the
home health market basket to adjust
HHA cost limits by an amount that
reflected the average increase in the
prices of the goods and services used to
furnish reasonable cost home health
care. This approach linked the increase
in the cost limits to the efficient
utilization of resources. For a greater
discussion on the home health market
basket, see the notice with comment
period published in the Federal
Register on February 15, 1980 (45 FR
10450, 10451), the notice with comment
period published in the Federal
Register on February 14, 1995 (60 FR
8389, 8392), and the notice with
comment period published in Federal
Register on July 1, 1996 (61 FR 34344,
34347). Beginning with the FY 2002 HH
PPS payments, we used the home health
market basket to update payments under
the HH PPS. We last rebased the home
health market basket effective with the
CY 2005 update. For more information
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on the HH PPS home health market
basket, see our proposed rule published
in the Federal Register on June 2, 2004
(69 FR 31251, 31255).
The home health market basket is a
fixed-weight Laspeyres-type price
index; its weights reflect the cost
distribution for the base year while
current period price changes are
measured. The home health market
basket is constructed in three steps.
First, a base period is selected and total
base period expenditures are estimated
for mutually exclusive and exhaustive
spending categories based upon the type
of expenditure. Then the proportion of
total costs that each spending category
represents is determined. These
proportions are called cost or
expenditure weights.
The second step essential for
developing an input price index is to
match each expenditure category to an
appropriate price/wage variable, called
a price proxy. These proxy variables are
drawn from publicly available statistical
series published on a consistent
schedule, preferably at least quarterly.
In the third and final step, the price
level for each spending category is
multiplied by the expenditure weight
for that category. The sum of these
products for all cost categories yields
the composite index level in the market
basket in a given year. Repeating the
third step for other years will produce
a time series of market basket index
levels. Dividing one index level by an
earlier index level will produce rates of
growth in the input price index.
We described the market basket as a
fixed-weight index because it answers
the question of how much more or less
it would cost, at a later time, to
purchase the same mix of goods and
services that was purchased in the base
period. As such, it measures ‘‘pure’’
price changes only. The effects on total
expenditures resulting from changes in
the quantity or mix of goods and
services purchased subsequent to the
base period are, by design, not
considered.
2. Rebasing and Revising the Home
Health Market Basket
We believe that it is desirable to
rebase the home health market basket
periodically so the cost category weights
reflect changes in the mix of goods and
services that HHAs purchase in
furnishing home health care. We based
the cost category weights in the current
home health market basket on FY 2000
data. We are proposing to rebase and
revise the home health market basket to
reflect FY 2003 Medicare cost report
data, the latest available and most
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complete data on the structure of HHA
costs.
The terms ‘‘rebasing’’ and ‘‘revising,’’
while often used interchangeably,
actually denote different activities. The
term ‘‘rebasing’’ means moving the base
year for the structure of costs of an input
price index (that is, in this exercise, we
are proposing to move the base year cost
structure from FY 2000 to FY 2003). The
term ‘‘revising’’ means changing data
sources, cost categories, and/or price
proxies used in the input price index.
For this proposed revising and
rebasing, we modified the wages and
salaries and benefits cost categories in
order to reflect a new data source on the
occupational mix of HHAs. We mainly
relied on this alternative proposed data
source to construct the cost weights for
the blended wage and benefit index. We
are not proposing any changes to the
price proxies used in the HH market
basket or the HH blended wage and
benefit proxies.
The weights for this proposed revised
and rebased home health market basket
are based off of the cost report data for
freestanding HHAs, whose cost
reporting period began on or after
October 1, 2002 and before October 1,
2003. Using this methodology allowed
our sample to include HHA facilities
with varying cost report years including,
but not limited to, the federal fiscal or
calendar year. We refer to the market
basket as a fiscal year market basket
because the base period for all price
proxies and weights are set to FY 2003.
For this proposed rebased and revised
market basket, we reviewed HHA
expenditure data for the market basket
cost categories.
We proposed to maintain our policy
of using data from freestanding HHAs
because they better reflect HHAs actual
cost structure. Expense data for a
hospital-based HHA are affected by the
allocation of overhead costs over the
entire institution (including but not
limited to hospital, hospital-based
skilled nursing facility, and hospitalbased HHA). Due to the method of
allocation, total expenses will be
correct, but the individual components’
expenses may be skewed. Therefore, if
data from hospital-based HHAs were
included, the resultant cost structure
could be unrepresentative of the average
HHA costs.
Data on HHA expenditures for nine
major expense categories (wages and
salaries, employee benefits,
transportation, operation and
maintenance, administrative and
general, insurance, fixed capital,
movable capital, and a residual ‘‘all
other’’) were tabulated from the FY 2003
Medicare HHA cost reports. As
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prescription drugs and DME are not
payable under the HH PPS, we excluded
those items from the home health
market basket and from the
expenditures. Expenditures for contract
services were also tabulated from these
FY 2003 Medicare HHA cost reports and
allocated to wages and salaries,
employee benefits, administrative and
general, and other expenses. After totals
for these cost categories were edited to
remove reports where the data were
deemed unreasonable (for example,
when total costs were not greater than
zero), we then determined the
proportion of total costs that each
category represents. The proportions
represent the major rebased home health
market basket weights.
We determined the weights for
subcategories (telephone, postage,
professional fees, other products, and
other services) within the combined
administrative and general and other
expenses using the latest available (1997
Benchmark) U.S. Department of
Commerce, Bureau of Economic
Analysis (BEA) Input-Output (I–O)
Table, from which we extracted data for
HHAs. The BEA I–O data, which are
updated at 5-year intervals, were most
recently described in the Survey of
Current Business article, ‘‘Benchmark
Input-Output Accounts of the U.S.,
1997’’ (December 2002). These data were
aged from 1997 to 2003 using relevant
price changes.
The methodology we used to age the
data applied the annual price changes
from the price proxies to the appropriate
cost categories. We repeated this
practice for each year.
This work resulted in the
identification of 12 separate cost
categories, the same number found in
the FY 2000-based home health market
basket. The differences between the
major categories for the proposed FY
2003-based index and those used for the
current FY 2000-based index are
summarized in Table 13. We have
allocated the contracted services weight
to the wages and salaries, employee
benefits, and administrative and general
and other expenses cost categories in
the proposed FY 2003-based index as
we did in the FY 2000-based index.
TABLE 13.—COMPARISON OF 2000-BASED AND PROPOSED 2003-BASED HOME HEALTH MARKET BASKETS MAJOR COST
CATEGORIES AND WEIGHTS
2000-Based
home health
market basket
Proposed
2003-based
home health
market basket
Wages and Salaries, including allocated contract services’ labor ..........................................................................
Employee Benefits, including allocated contract services’ labor ............................................................................
All Other Expenses including allocated contract services’ labor ............................................................................
65.766
11.009
23.225
64.484
12.598
22.918
Total ..................................................................................................................................................................
100.000
100.000
Cost categories
The complete proposed 2003-based
cost categories and weights are listed in
Table 14.
TABLE 14.—COST CATEGORIES, WEIGHTS, AND PRICE PROXIES IN PROPOSED 2003-BASED HOME HEALTH MARKET
BASKET
Cost categories
Weight
77.082
64.484
12.598
0.694
16.712
Total .......................................................................................
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Compensation, including allocated contract services’ labor .........
Wages and Salaries, including allocated contract services’ labor
Employee Benefits, including allocated contract services’ labor ..
Operations & Maintenance ...........................................................
Administrative & General & Other Expenses including allocated
contract services’ labor.
Telephone .....................................................................................
Postage .........................................................................................
Professional Fees .........................................................................
Other Products ..............................................................................
Other Services ..............................................................................
Transportation ...............................................................................
Capital-Related .............................................................................
Insurance ......................................................................................
Fixed Capital .................................................................................
Movable Capital ............................................................................
100.000
0.785
0.605
1.471
7.228
6.622
2.494
3.018
0.510
1.618
0.890
Price proxy
Proposed Home Health Occupational Wage Index.
Proposed Home Health Occupational Benefits Index.
CPI–U Fuel & Other Utilities.
CPI–U Telephone Services.
CPI–U Postage.
ECI for Compensation for Professional and Technical Workers.
CPI–U All Items Less Food and Energy.
ECI for Compensation for Service Workers.
CPI–U Private Transportation.
CPI–U Household Insurance.
CPI–U Owner’s Equivalent Rent.
PPI Machinery & Equipment.
**
** Figures may not sum to total due to rounding.
After we computed the FY 2003 cost
category weights for the proposed
rebased home health market basket, we
selected the most appropriate wage and
price indexes to proxy the rate of change
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for each expenditure category. These
price proxies are based on Bureau of
Labor Statistics (BLS) data and are
grouped into one of the following BLS
categories:
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• Employment Cost Indexes—
Employment Cost Indexes (ECIs)
measure the rate of change in employee
wage rates and employer costs for
employee benefits per hour worked.
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These indexes are fixed-weight indexes
and strictly measure the change in wage
rates and employee benefits per hour.
They are not affected by shifts in skill
mix. ECIs are superior to average hourly
earnings as price proxies for input price
indexes for two reasons: (a) They
measure pure price change; and (b) they
are available by occupational groups,
not just by industry.
• Consumer Price Indexes—
Consumer Price Indexes (CPIs) measure
change in the prices of final goods and
services bought by the typical
consumer. Consumer price indexes are
used when the expenditure is more
similar to that of a purchase at the retail
level rather than at the wholesale level,
or if no appropriate Producer Price
Indexes (PPIs) were available.
• Producer Price Indexes—PPIs are
used to measure price changes for goods
sold in other than retail markets. For
example, a PPI for movable equipment
is used rather than a CPI for equipment.
PPIs in some cases are preferable price
proxies for goods that HHAs purchase at
wholesale levels. These fixed-weight
indexes are a measure of price change
at the producer or at the intermediate
stage of production.
We evaluated the price proxies using
the criteria of reliability, timeliness,
availability, and relevance. Reliability
indicates that the index is based on
valid statistical methods and has low
sampling variability. Widely accepted
statistical methods ensure that the data
were collected and aggregated in way
that can be replicated. Low sampling
variability is desirable because it
indicates that sample reflects the typical
members of the population. (Sampling
variability is variation that occurs by
chance because a sample was surveyed
rather than the entire population.)
Timeliness implies that the proxy is
published regularly, preferably at least
once a quarter. The market baskets are
updated quarterly and therefore it is
important the underlying price proxies
be up-to-date, reflecting the most recent
data available. We believe that using
proxies that are published regularly (at
least quarterly, whenever possible)
helps ensure that we are using the most
recent data available to update the
market basket. We strive to use
publications that are disseminated
frequently because we believe that this
is an optimal way to stay abreast of the
most current data available. Availability
means that the proxy is publicly
available. We prefer that our proxies are
publicly available because this will help
ensure that our market basket updates
are as transparent to the public as
possible. In addition, this enables the
public to be able to obtain the price
proxy data on a regular basis. Finally,
relevance means that the proxy is
applicable and representative of the cost
category weight to which it is applied.
The CPIs, PPIs, and ECIs selected by us
to be proposed in this regulation meet
these criteria. Therefore, we believe that
they continue to be the best measure of
price changes for the cost categories to
which they would be applied.
As part of the revising and rebasing of
the home health market basket, we are
proposing to revise and rebase the home
health blended wage and salary index
and the home health blended benefits
index.
25437
We would use these blended indexes
as price proxies for the wages and
salaries and the employee benefits
portions of the proposed FY 2003-based
home health market basket, as we did in
the FY 2000-based home health market
basket. The price proxies for these two
cost categories are the same as those
used in the FY 2000-based home health
market basket but with occupational
weights reflecting the FY 2003
occupational mix in HHAs. These
proxies are a combination of health
industry specific and economy-wide
proxies.
3. Price Proxies Used To Measure Cost
Category Growth
• Wages and salaries, including an
allocation for contract services’ labor:
For measuring price growth in the FY
2003-based home health market basket,
as we did in the FY 2000-based index,
five price proxies would be applied to
the four occupational subcategories
within the wages and salaries
component, and would be weighted to
reflect the HHA occupational mix. This
approach was used because there is not
a wage proxy for home health care
workers that reflects only wage changes
and not both wage and skill mix
changes. The professional and technical
occupational subcategory is represented
by a 50–50 blend of hospital industry
and economy-wide price proxies.
Therefore, there are five price proxies
used for the four occupational
subcategories. The percentage change in
the blended wages and salaries price is
applied to the wages and salaries
component of the home health market
basket, which is described in Table 15.
TABLE 15.—PROPOSED HOME HEALTH OCCUPATIONAL WAGES AND SALARIES INDEX
[Wages and salaries component of the proposed FY 2003-based home health market basket]
2000
weight
Cost category
2003
weight
Skilled Nursing & Therapists & Other Professional/Technical, including an allocation for contract services’ labor.
53.816
50.812
Managerial/Supervisory, including an allocation for contract
services’ labor.
Clerical, including an allocation for contract services’ labor
7.431
9.007
6.822
7.596
Service, including an allocation for contract services’ labor
31.931
32.584
100.000
• 50 percent ECI for Wages & Salaries in Private Industry
for Professional, Specialty & Technical Workers.
• 50 percent ECI for Wages & Salaries for Civilian Hospital Workers.
ECI for Wages & Salaries in Private Industry for Executive,
Administrative & Managerial Workers.
ECI for Wages & Salaries in Private Industry for Administrative Support, Including Clerical Workers.
ECI for Wages & Salaries in Private Industry Service Occupations.
100.000
Total ...............................................................................
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Price proxy
Beginning with the FY 2001 Medicare
cost report, the occupational specific
wage and benefit expenditure data was
no longer collected in the cost report.
Previously, we used these data to
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estimate weights for the home health
blended wage and salary index and the
home health blended benefits index. We
believed the options to obtain these data
were:
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• To obtain the home health
occupational specific expenditure data
from an alternative source, or
• To propose a change to the home
health wages and salaries and the home
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health benefits proxy used in the market
basket.
However, there is no publicly
available data source that tracks wage
and salary price growth for the home
health industry while holding skill mix
constant. There is also no publicly
available data source that tracks benefit
price growth for the home health
industry while holding skill mix
constant. Therefore, option 2 was not an
viable solution. Next, we investigated if
there was home health occupational
specific expenditure data from an
alternative source other than the
Medicare cost reports. We believe an
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alternative source exists in the form of
data from the November 2003 National
industry-specific occupational
employment and wage estimates
published by the BLS Office of
Occupational Employment Statistics
(OES). Accordingly, we propose to use
that data to determine weights for the
home health specific blended wage and
benefits proxy. Detailed information on
the methodology for the national
industry-specific occupational
employment and wage estimates survey
can be found at https://www.bls.gov/oes/
current/oes_tec.htm.
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Therefore, the needed data on HHA
expenditures for the four occupational
subcategories (managerial, professional
and technical, service, and clerical) for
the wages and salaries component were
tabulated from the November 2003 OES
data for North American Industrial
Classification System (NAICS) 621600,
Home Health Care Services. We
assigned the occupations to the groups
in a manner consistent with the
occupational groupings used in the
Medicare cost report. Table 16 shows
the specific occupational assignments to
the four CMS designated subcategories.
BILLING CODE 4120–01–P
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Total expenditures by occupation
were calculated by taking the OES
number of employees multiplied by the
OES annual average salary. The wage
and salary expenditures were aggregated
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based on the groupings in table 14.
Next, contract labor expenditures were
obtained from the 1997 I-O for the home
health industry, NAICS 621600 and
aged forward to FY 2003 using the PPI
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25439
for employment services. We then
proportionally allocated the contract
labor to each of the four subcategories.
We determined the proportion of total
wage costs (contract wages plus
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industry wages) that each subcategory
represents. These proportions represent
the major rebased and revised home
health blended wage and salary index
weights.
We did not propose a change from our
current blended measure because we
believe it reflects the competition
between HHAs and hospitals for
registered nurses, while still capturing
the overall wage trends for professional
and technical workers.
• Employee benefits, including an
allocation for contract services’ labor:
For measuring employee benefits price
growth in the FY 2003-based home
health market basket, price proxies are
applied to the four occupational
subcategories within the employee
benefits component, weighted to reflect
the home health occupational mix. The
professional and technical occupational
subcategory is represented by a blend of
hospital industry and economy-wide
price proxies. Therefore, there are five
price proxies for four occupational
subcategories. The percentage change in
the blended price of home health
employee benefits is applied to this
component, which is described in Table
17.
TABLE 17.—PROPOSED HOME HEALTH OCCUPATIONAL BENEFITS INDEX
[Employee benefits component of the proposed 2003-based home health market basket]
2000
weight
Cost category
2003
weight
Skilled Nursing & Therapists & Other Professional/Technical, including an allocation for contract services’ labor.
53.492
50.506
Managerial/Supervisory, including an allocation for contract
services’ labor.
Clerical, including an allocation for contract services’ labor
7.232
8.766
6.941
7.698
Service, including an allocation for contract services’ labor
32.362
33.024
100.000
• 50 percent ECI for Benefits in Private Industry for Professional, Specialty &Technical Workers.
• 50 percent ECI for Benefits for Civilian Hospital Workers.
ECI for Benefits in Private Industry for Executive, Administrative & Managerial Workers.
ECI for Benefits in Private Industry for Administrative Support, Including Clerical Workers.
ECI for Benefits in Private Industry Service Occupations.
100.000
Total ...............................................................................
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Price proxy
After conducting research we could
find no data source that exists for
benefit expenditures by occupation for
the home health industry. Thus, to
construct weights for the home health
occupational benefits index we
calculated the ratio of benefits to wages
and salaries from the 2000 Home health
occupational wages and occupational
benefits indices for the four
occupational subcategories. We then
applied the benefit-to-wage ratios to
each of the four occupational
subcategories from the 2003 OES wage
and salary weights. For example, the
ratio of benefits to wages from the 2000
home health occupational wage and
benefit indexes for home health
managers is 0.973. We apply this ratio
to the 2003 OES weight for wages and
salaries for home health managers,
9.007, to obtain a benefit weight in the
home health occupational benefit index
for home health managers of 8.766
percent.
We are proposing to continue to use
the same 50-50 split for benefits for
professional and technical workers (50
percent hospital workers and 50 percent
professional and technical workers) as
we did in the FY 2000-based market
basket.
• Operations and Maintenance: The
percentage change in the price of fuel
and other utilities as measured by the
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Consumer Price Index is applied to this
component. The same proxy was used
for the FY 2000-based market basket.
• Telephone: The percentage change
in the price of telephone service as
measured by the Consumer Price Index
is applied to this component. The same
proxy was used for the FY 2000-based
market basket.
• Postage: The percentage change in
the price of postage as measured by the
Consumer Price Index is applied to this
component. The same proxy was used
for the FY 2000-based market basket.
• Professional Fees: The percentage
change in the price of professional fees
as measured by the ECI for
compensation for professional and
technical workers is applied to this
component. The same proxy was used
for the 2000-based market basket.
• Other Products: The percentage
change in the price for all items less
food and energy as measured by the
Consumer Price Index is applied to this
component. The same proxy was used
for the FY 2000-based market basket.
• Other Services: The percentage
change in the employment cost index
for compensation for service workers is
applied to this component. The same
proxy was used for the FY 2000-based
market basket.
• Transportation: The percentage
change in the price of private
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transportation as measured by the
Consumer Price Index is applied to this
component. The same proxy was used
for the FY 2000-based market basket.
• Insurance: The percentage change
in the price of household insurance as
measured by the Consumer Price Index
is applied to this component. The same
proxy was used for the FY 2000-based
market basket.
• Fixed capital: The percentage
change in the price of an owner’s
equivalent rent as measured by the
Consumer Price Index is applied to this
component. The same proxy was used
for the FY 2000-based market basket.
• Movable Capital: The percentage
change in the price of machinery and
equipment as measured by the Producer
Price Index is applied to this
component. The same proxy was used
for the FY 2000-based market basket.
As we did in the FY 2000-based home
health market basket, we allocated the
Contract Services’ share of home health
agency expenditures among wages and
salaries, employee benefits,
administrative and general and other
expenses.
Table 18 summarizes the proposed FY
2003-based proxies and compares them
to the FY 2000-based proxies.
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TABLE 18.—COMPARISON OF PRICE PROXIES USED IN THE 2000–BASED AND THE PROPOSED 2003–BASED HOME
HEALTH MARKET BASKETS
2000-Based
price proxy
Cost category
Compensation, including allocated contract services’ labor
Wages and Salaries, including allocated contract services’
labor
Employee Benefits, including allocated contract services’
labor
Operations and Maintenance ....................................................
Administrative & General & Other Expenses, including allocated contract services’ labor
Telephone ..................................................................................
Postage ......................................................................................
Professional Fees ......................................................................
Other Products ..........................................................................
Other Services ...........................................................................
Transportation ............................................................................
Capital-Related
Insurance ...................................................................................
Fixed Capital ..............................................................................
Movable Capital .........................................................................
Contract Services ......................................................................
4. Rebasing Results
2003-Based proposed price proxy
Same ................
Home Health Agency Occupational Wage Index.
Same ................
Home Health Agency Occupational Benefits Index.
Same ................
CPI–Fuel and Other Utilities.
Same ................
Same ................
Same ................
CPI–U Telephone.
CPI–U Postage.
ECI for Compensation for Professional and Technical Workers.
CPI–U for All Items Less Food and Energy.
ECI for Compensation for Service Workers.
CPI–U Private Transportation.
Same ................
Same ................
Same ................
Same
Same
Same
Same
................
................
................
................
CPI–U Household Insurance.
CPI–U Owner’s Equivalent Rent.
PPI Machinery and Equipment.
Contained within Wages & Salaries, Employee Benefits, Administrative & General & Other Expenses; see those price
proxies.
2000-based home health market basket
and the proposed FY 2003-based home
health market basket is shown in Table
19. The average annual increase in the
A comparison of the yearly changes
from CY 2005 to CY 2008 for the FY
two market baskets is similar, and in no
year is the difference greater than 0.1
percentage point.
TABLE 19.—COMPARISON OF THE 2000–BASED HOME HEALTH MARKET BASKET AND THE PROPOSED 2003–BASED
HOME HEALTH MARKET BASKET, PERCENT CHANGE, 2005–2008
Home health
market basket,
2000-based
Fiscal years beginning October 1
Proposed
home health
market basket,
2003-based
3.1
3.2
3.1
2.9
3.1
Difference
(proposed
2003-based
less 2000based)
3.1
3.1
3.1
2.9
3.1
Historical:
CY 2005 .......................................................................................................................................
CY 2006 .......................................................................................................................................
CY 2007 .......................................................................................................................................
CY 2008 .......................................................................................................................................
Average Change: 2005–2008 ......................................................................................................
0.0
¥0.1
0.0
0.0
0.0
Source: Global Insights, Inc, 4th Qtr, 2006.
Table 20 shows that the forecasted
rate of growth for CY 2008, beginning
January 1, 2008, for the proposed
rebased and revised home health market
basket is 2.9 percent, while the
forecasted rate of growth for the current
2000-based home health market basket
is also 2.9 percent. As previously
mentioned, we rebase the home health
market basket periodically so the cost
category weights continue to reflect
changes in the mix of goods and
services that HHAs purchase in
furnishing home health care.
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TABLE 20.—FORECASTED ANNUAL PERCENT CHANGE IN THE CURRENT AND PROPOSED REVISED AND REBASED HOME
HEALTH MARKET BASKETS
Calendar year beginning January 1
Home health
market basket,
2000-based
Proposed
home health
market basket,
2003-based
Difference
(proposed
2003-based
Less 2000based)
January 2008, CY 2008 ...............................................................................................................
2.9
2.9
0.0
Source: Global Insights, Inc, 4th Qtr, 2006.
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Table 21 shows the percent changes
for CY 2008 for each cost category in the
home health market basket.
TABLE 21.—CY 2008 FORECASTED ANNUAL PERCENT CHANGE FOR ALL COST CATEGORIES IN THE PROPOSED 2003–
BASED HOME HEALTH MARKET BASKET
Cost categories
Weight
Total ..............................................................................
Compensation ...............................................................
Wages and Salaries ......................................................
Employee Benefits ........................................................
Operations & Maintenance ...........................................
Administrative & General & Other Expenses ...............
Telephone .....................................................................
Postage .........................................................................
Professional Fees .........................................................
100.00
77.082
64.484
12.598
0.694
16.712
0.785
0.605
1.471
Other Products ..............................................................
Other Services ..............................................................
Transportation ...............................................................
Capital-Related .............................................................
Insurance ......................................................................
Fixed Capital .................................................................
Movable Capital ............................................................
6.622
7.228
2.494
3.018
0.510
1.618
0.890
Forecasted
annual percent
change for CY
2008
Price proxy
..................................................................................
..................................................................................
Proposed Home Health Occupational Wage Index .....
Proposed Home Health Occupational Benefits Index
CPI–U Fuel & Other Utilities ........................................
..................................................................................
CPI–U Telephone Services ..........................................
CPI–U Postage ............................................................
ECI for Compensation for Professional and Technical
Workers.
CPI–U All Items Less Food and Energy ......................
ECI for Compensation for Service Workers ................
CPI–U Private Transportation ......................................
..................................................................................
CPI–U Household Insurance .......................................
CPI–U Owner’s Equivalent Rent ..................................
PPI Machinery & Equipment ........................................
2.9
3.1
2.9
3.8
3.2
2.6
0.8
4.8
3.0
2.0
3.1
0.5
1.8
2.6
2.6
¥0.3
Source: Global Insights, Inc, 4th Qtr, 2006.
5. Labor-Related Share
In the 2000-based home health market
basket the labor-related share was
76.775 percent while the remaining
non-labor-related share was 23.225
percent. In the proposed revised and
rebased home health market basket, the
labor-related share would be 77.082
percent. The labor-related share
includes wages and salaries and
employee benefits. The proposed nonlabor-related share would be 22.918
percent. The increase in the laborrelated share using the FY 2003-based
HH market basket is primarily due to
the increase in the benefit cost weight.
Our preliminary analysis of Medicare
cost report data for skilled nursing
facilities and acute care hospitals also
shows a similar upward trend for the
SNF and hospital benefit cost weights
from FY 2000 to FY 2003.
Table 22 details the components of
the labor-related share for the FY 2000based and proposed FY 2003-based
home health market baskets.
TABLE 22.—LABOR-RELATED SHARE OF CURRENT AND PROPOSED HOME HEALTH MARKET BASKETS
2000-based
market basket
weight
Proposed
2003-based
market basket
weight
Wages and Salaries ................................................................................................................................................
Employee Benefits ...................................................................................................................................................
65.766
11.009
64.484
12.598
Total Labor Related ..........................................................................................................................................
76.775
77.082
Total Non-Labor Related ..................................................................................................................................
23.225
22.918
Cost category
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C. National Standardized 60-Day
Episode Payment Rate
The Medicare HH PPS has been
effective since October 1, 2000. As set
forth in the final rule published July 3,
2000 in the Federal Register (65 FR
41128), the unit of payment under the
Medicare HH PPS is a national
standardized 60-day episode payment
rate. As set forth in § 484.220, we adjust
the national standardized 60-day
episode payment rate by a case-mix
grouping and a wage index value based
on the site of service for the beneficiary.
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The proposed CY 2008 HH PPS rates
use the case-mix methodology proposed
in section II.A.2 of this proposed rule
and application of the wage index
adjustment to the labor portion of the
HH PPS rates as set forth in the July 3,
2000 final rule. As stated above, we are
proposing to rebase and revise the home
health market basket, resulting in a
revised and rebased labor related share
of 77.082 percent and a non-labor
portion of 22.918 percent. We multiply
the national standardized 60-day
episode payment rate by the patient’s
applicable case-mix weight. We divide
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the case-mix adjusted amount into a
labor and non-labor portion. We
multiply the labor portion by the
applicable wage index based on the site
of service of the beneficiary.
For CY 2008, we are proposing to base
the wage index adjustment to the labor
portion of the HH PPS rates on the most
recent pre-floor and pre-reclassified
hospital wage index as discussed in
section II.B of this proposed rule (not
including any reclassifications under
section 1886(d)(8)(B)) of the Act.
As discussed in the July 3, 2000 HH
PPS final rule, for episodes with four or
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fewer visits, Medicare pays the national
per-visit amount by discipline, referred
to as a LUPA. We update the national
per-visit amounts by discipline annually
by the applicable home health market
basket percentage. We adjust the
national per-visit amount by the
appropriate wage index based on the
site of service for the beneficiary as set
forth in § 484.230. We propose to adjust
the labor portion of the updated
national per-visit amounts by discipline
used to calculate the LUPA by the most
recent pre-floor and pre-reclassified
hospital wage index, as discussed in
section II.D of this proposed rule.
Medicare pays the 60-day case-mix
and wage-adjusted episode payment on
a split percentage payment approach.
The split percentage payment approach
includes an initial percentage payment
and a final percentage payment as set
forth in § 484.205(b)(1) and (b)(2). We
may base the initial percentage payment
on the submission of a request for
anticipated payment and the final
percentage payment on the submission
of the claim for the episode, as
discussed in § 409.43. The claim for the
episode that the HHA submits for the
final percentage payment determines
the total payment amount for the
episode and whether we make an
applicable adjustment to the 60-day
case-mix and wage-adjusted episode
payment. The end date of the 60-day
episode as reported on the claim
determines which CY rates Medicare
will use to pay the claim.
We may also adjust the 60-day casemix and wage-adjusted episode
payment based on the information
submitted on the claim to reflect the
following:
• A LUPA provided on a per-visit
basis as set forth in § 484.205(c) and
§ 484.230.
• A PEP adjustment as set forth in
§ 484.205(d) and § 484.235.
• An outlier payment as set forth in
§ 484.205(f) and § 484.240.
Currently, we may also adjust the
episode payment by a SCIC adjustment
as set forth in § 484.202, but as noted in
section II.A.6 of this proposed rule, we
are now proposing to remove the SCIC
adjustment from HH PPS.
This proposed rule reflects the
proposed updated CY 2008 rates that
would be effective January 1, 2008.
D. Proposed CY 2008 Rate Update by
the Home Health Market Basket Index
(With Examples of Standard 60-Day and
LUPA Episode Payment Calculations)
Section 1895(b)(3)(B) of the Act, as
amended by section 5201 of the DRA,
requires for CY 2008 that the standard
prospective payment amounts be
increased by a factor equal to the
applicable home health market basket
update for those HHAs that submit
quality data as required by the
Secretary. The applicable home health
market basket update will be reduced by
2 percentage points for those HHAs that
fail to submit the required quality data.
• Proposed CY 2008 Adjustments
In calculating the annual update for
the CY 2008 national standardized 60day episode payment rates, we are
proposing to first look at the CY 2007
rates as a starting point. The CY 2007
national standardized 60-day episode
payment rate is $2,339.00.
25443
In order to calculate the CY 2008
national standardized 60-day episode
payment rate, we are proposing to first
increase the CY 2007 national
standardized 60-day episode payment
rate ($2,339.00) by the proposed
estimated rebased and revised home
health market basket update of 2.9
percent for CY 2008.
Given this updated rate, we would
then take a reduction of 2.75 percent to
account for nominal change in case-mix.
We would multiply the resulting value
by 1.05 and 0.958614805 to account for
the estimated percentage of outlier
payments as a result of the current FDL
ratio of 0.67 (that is, $2,339.00 * 1.029
* .9725 * 1.05 * 0.958614805), to yield
an updated CY 2008 national
standardized 60-day episode payment
rate of $2,355.96 for episodes that begin
in CY 2007 and end in CY 2008 (see
Table 23a). For episodes that begin in
CY 2007 and end in CY 2008, the new
proposed 153 HHRG case-mix model
(and associated Grouper) would not yet
be in effect. For that reason, we propose
that episodes that begin in CY 2007 and
end in CY 2008 be paid at the rate of
$2,355.96, and be further adjusted for
wage differences and for case-mix,
based on the current 80 HHRG case-mix
model. We recognize that the annual
update for CY 2008 is for all episodes
that end on or after January 1, 2008 and
before January 1, 2009. By paying this
rate ($2,355.96) for episodes that begin
in CY 2007 and end in CY 2008, we will
have appropriately recognized that these
episodes are entitled to receive the CY
2008 home health market, even though
the new case-mix model will not yet be
in effect.
TABLE 23A.—PROPOSED NATIONAL 60-DAY EPISODE AMOUNTS UPDATED BY THE ESTIMATED HOME HEALTH MARKET
BASKET UPDATE FOR CY 2008, BEFORE CASE-MIX ADJUSTMENT, WAGE INDEX ADJUSTMENT BASED ON THE SITE OF
SERVICE FOR THE BENEFICIARY OR APPLICABLE PAYMENT ADJUSTMENT FOR EPISODES BEGINNING IN CY 2007 AND
ENDING IN CY 2008
Multiply by the
proposed estimated home
health market
basket update
(2.9 percent) 1
Total CY 2007 national standardized 60-day episode payment rate
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$2,339.00 .........................................................................................................
× 1.029
Reduce by
2.75 percent
for nominal
change in
case-mix
× 0.9725
Adjusted to
account for the
5 percent
outlier policy
Proposed national standardized 60-day
episode payment rate for
episodes beginning in CY
2007 and ending in CY 2008
× 1.05
× 0.958614805
$2,355.96
1 The estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006.
Next, in order to establish new rates
based on a proposed new case-mix
system, we again start with the CY 2007
national standardized 60-day episode
payment rate and increase that rate by
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the proposed estimated rebased and
revised home health market basket
update (2.9 percent) ($2,339.00 * 1.029
= $2,406.83). We next have to put
dollars associated with the outlier
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targeted estimates back into the base
rate. In the 2000 HH PPS final rule (65
FR 41184), we divided the base rate by
1.05 to account for the outlier target
policy. Therefore, we are proposing to
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multiply the $2,406.83 by 1.05, resulting
in $2,527.17. Next we need to reduce
this amount to pay for each of our
proposed policies. As noted previously,
based upon our proposed change to the
LUPA payment, the NRS redistribution,
the elimination of the SCIC policy, the
amounts needed to account for outlier
payments, and the reduction accounting
for nominal change in case-mix, we
would reduce the national standardized
60-day episode payment rate by $6.46,
$40.88, $15.71, $94.02, and $69.50,
respectively. This results in a proposed
CY 2008 updated national standardized
60-day episode payment rate, for
episodes beginning and ending in CY
2008, of $2,300.60 (see Table 23b).
These episodes would be further
adjusted for case-mix based on the
proposed 153 HHRG case-mix model for
episodes beginning and ending in CY
2008. As we noted in section II.A.2.d.,
we increased the case-mix weights by a
budget neutrality factor of 1.194227193.
TABLE 23b.—PROPOSED NATIONAL 60-DAY EPISODE AMOUNTS UPDATED BY THE ESTIMATED HOME HEALTH MARKET
BASKET UPDATE FOR CY 2008, BEFORE CASE-MIX ADJUSTMENT, WAGE INDEX ADJUSTMENT BASED ON THE SITE OF
SERVICE FOR THE BENEFICIARY OR APPLICABLE PAYMENT ADJUSTMENT FOR EPISODES BEGINNING AND ENDING IN
CY 2008
Total CY 2007 national standardized
60-day episode payment rate
Multiply by the
proposed estimated home
health market basket update
(2.9 percent) 1
Adjusted to return
the outlier funds to
the national standardized 60-day
episode payment
rate
Updated and
outlier adjusted
national standardized 60-day episode payment
Changes to account for LUPA
adjustment
($6.46), NRS payment ($40.88),
elimination of
SCIC policy
($15.71), maintaining a 0.67 FDL
ratio ($94.02), and
2.75 percent reduction for nominal change in
case-mix ($69.50)
for episodes beginning and ending in CY 2008
$2,339.00 ...............................................
× 1.029
× 1.05
$2,527.17
¥$226.57
Proposed CY
2008 national
standardized 60day episode payment rate for episodes beginning
and ending in CY
2008
$2,300.60
1 The
estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006.
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Under the HH PPS, NRS payment,
which was $49.62 at the onset of the HH
PPS, has been updated yearly as part of
the national standardized 60-day
episode payment rate. As discussed
previously in section II.A.7., we propose
to remove the current NRS payment
amount portion from the national
standardized 60-day episode payment
rate and add a severity adjusted NRS
payment amount subject to case-mix
and wage adjustment to the national
standardized 60-day episode payment
rate. Therefore, to calculate an episode’s
prospective payment amount, the NRS
adjusted payment amount must first be
calculated by multiplying the episode’s
NRS weight (taken from Table 11 of this
proposed rule) by the NRS conversion
factor. This NRS adjusted payment
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amount is then added to, and, becomes
a part of, the non-adjusted HH PPS
standardized prospective payment rate
for CY 2008. Then, for any HHRG group,
to compute a case-mix adjusted
payment, the sum of the non-adjusted
national standardized 60-day episode
payment rate and the NRS adjusted
payment amount are multiplied by the
appropriate case-mix weight taken from
Table 5. Finally, to compute a wage
adjusted national standardized 60-day
episode payment rate, that labor-related
portion of the national standardized 60day episode payment rate for CY 2008
is multiplied by the appropriate wage
index factor listed in Addendum A. The
product of that calculation is added to
the corresponding non-labor-related
amount. The resulting amount is the
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national case-mix and wage adjusted
national standardized 60-day episode
payment rate for that particular episode.
The following example illustrates the
computation described above:
Example 1. An HHA is providing services
to a Medicare beneficiary in Grand Forks,
ND. The national standardized payment rate
is $2,300.60 (see Table 23). The HHA
determines that the beneficiary is in his or
her 3rd episode and thus falls under the
C1F3S3 HHRG for 3rd+ episodes with 0 to 13
therapy visits (Case Mix Weight = 1.4815). It
is also determined that the beneficiary falls
under NRS severity level #4. The NRS
Severity Level #4 weight = 6.9612 and the
NRS Conversion Factor = $52.77 (see Table
11).
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• National Per-visit Amounts Used to
Pay LUPAs and Compute Imputed Costs
Used in Outlier Calculations
As discussed previously in this
proposed rule, the policies governing
LUPAs and the outlier calculations set
forth in the July 3, 2000 HH PPS final
rule will continue (65 FR 41128) with
an increase of $92.63 for initial and only
episode LUPAs during CY 2008. In
calculating the proposed CY 2008
national per-visit amounts used to
calculate payments for LUPA episodes
and to compute the imputed costs in
outlier calculations, we are proposing to
start with the CY 2007 per-visit
amounts. We propose to increase the CY
2007 per-visit amounts for each home
health discipline for CY 2008 by the
proposed estimated rebased and revised
home health market basket update (2.9
percent), then multiply by 1.05 and
0.958614805 to account for the
estimated percentage of outlier
payments as a result of the current FDL
ratio of 0.67 (see Table 24).
TABLE 24.—PROPOSED NATIONAL PER-VISIT AMOUNTS FOR LUPAS (NOT INCLUDING THE INCREASE IN PAYMENT FOR A
BENEFICIARY’S ONLY EPISODE OR THE INITIAL EPISODE IN A SEQUENCE OF ADJACENT EPISODES) AND OUTLIER CALCULATIONS UPDATED BY THE ESTIMATED HOME HEALTH MARKET BASKET UPDATE FOR CY 2008, BEFORE WAGE
INDEX ADJUSTMENT BASED ON THE SITE OF SERVICE FOR THE BENEFICIARY
Final CY 2007
per-visit
amounts per
60-day episode
for LUPAs
Multiply by the
proposed estimated home
health market
basket
(2.9 percent) 1
Home Health Aide ...................................................................................
$46.24
× 1.029
Medical Social Services ...........................................................................
163.68
× 1.029
Occupational Therapy ..............................................................................
112.40
× 1.029
Physical Therapy .....................................................................................
111.65
× 1.029
Skilled Nursing .........................................................................................
102.11
× 1.029
Speech-Language Pathology ..................................................................
121.22
× 1.029
Home health discipline type
Adjusted to account for the 5
percent outlier
policy
× 1.05
× 0.958614805
× 1.05
× 0.958614805
× 1.05
× 0.958614805
× 1.05
× 0.958614805
× 1.05
× 0.958614805
× 1.05
× 0.958614805
Proposed CY
2008 per-visit
payment
amount per
discipline
$47.91.
169.53.
116.42.
115.63.
105.76.
125.55.
1 The estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006.
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Payment for LUPA episodes is
changed in that for LUPAs that occur as
initial episodes in a sequence of
adjacent episodes or as the only
episode, we are proposing an increased
payment amount (see section II.A.5. of
this proposed regulation) to the LUPA
payment. Table 24 rates are before that
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adjustment and are the rates paid to all
other LUPA episodes. LUPA episodes
that occur as the only episode or initial
episode in a sequence of adjacent
episodes are adjusted by including the
proposed amount of $92.63 to the LUPA
payment before adjusting for wage
index.
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Example 2. An HHA is providing services
to a Medicare beneficiary in rural New
Hampshire. During the 60-day episode the
beneficiary receives only 3 visits. It is the
initial episode during a sequence of adjacent
episodes for this beneficiary.
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Outlier payments are determined and
calculated using the same methodology
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that has been used since the
implementation of the HH PPS.
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E. Hospital Wage Index
Sections 1895(b)(4)(A)(ii) and (b)(4)(C)
of the Act require the Secretary to
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establish area wage adjustment factors
that reflect the relative level of wages
and wage-related costs applicable to the
furnishing of home health services and
to provide appropriate adjustments to
the episode payment amounts under the
HH PPS to account for area wage
differences. We apply the appropriate
wage index value to the proposed labor
portion (77.082 percent; see Table 22) of
the HH PPS rates based on the
geographic area where the beneficiary
received the home health services. As
implemented under the HH PPS in the
July 3, 2000 HH PPS final rule, each
HHA’s labor market area is based on
definitions of Metropolitan Statistical
Areas (MSAs) issued by the OMB.
In the August 11, 2004 IPPS final rule
[69 FR 49206], revised labor market area
definitions were adopted at § 412.64(b),
which were effective October 1, 2004 for
acute care hospitals. The new standards,
Core Based Statistical Areas (CBSAs),
were announced by OMB in late 2000
and were also discussed in greater detail
in the July 14, 2005 HH PPS proposed
rule. For the purposes of the HH PPS,
the term ‘‘MSA-based’’ refers to wage
index values and designations based on
the previous MSA designations.
Conversely, the term ‘‘CBSA-based’’
refers to wage index values and
designations based on the new OMB
revised MSA designations which now
include CBSAs. In the November 9,
2005 HH PPS final rule (70 FR 68132),
we implemented a 1-year transition
policy using a 50/50 blend of the CBSAbased wage index values and the MSAbased wage index values for CY 2006.
The one-year transition policy ended in
CY 2006. For CY 2008, we propose to
use a wage index based solely on the
CBSA designations.
1. Background
As implemented under the HH PPS in
the July 3, 2000 HH PPS final rule, each
HHA’s labor market is determined based
on definitions of MSAs issued by OMB.
In general, an urban area is defined as
an MSA or New England County
Metropolitan Area (NECMA) as defined
by OMB. Under § 412.64(b)(1)(ii)(C), a
rural area is defined as any area outside
of the urban area. The urban and rural
area geographic classifications are
defined in § 412.64(b)(1)(ii)(A) and
§ 412.64.(b)(1)(II)(C) respectively, and
have been used under the HH PPS since
implementation.
Under the HH PPS, the wage index
value used is based upon the location of
the beneficiary’s home. As has been our
longstanding practice, any area not
included in an MSA (urban area) is
considered to be non-urban
§ 412.64(b)(1)(ii)(C) and receives the
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statewide rural wage index value (see,
for example, 65 FR 41173).
As discussed previously and set forth
in the July 3, 2000 final rule, the statute
provides that the wage adjustment
factors may be the factors used by the
Secretary for purposes of section
1886(d)(3)(E) of the Act for hospital
wage adjustment factors. As discussed
in the July 3, 2000 final rule, we are
proposing again to use the pre-floor and
pre-reclassified hospital wage index
data to adjust the labor portion of the
HH PPS rates based on the geographic
area where the beneficiary receives
home health services. We believe the
use of the pre-floor and pre-reclassified
hospital wage index data results in the
appropriate adjustment to the labor
portion of the costs as required by
statute. For the CY 2008 update to home
health payment rates, we would
continue to use the most recent pre-floor
and pre-reclassified hospital wage index
available at the time of publication.
In adopting the CBSA designations,
we identified some geographic areas
where there are no hospitals, and thus
no hospital wage data on which to base
the calculation of the home health wage
index. Beginning in CY 2006, we
adopted a policy that, for urban labor
markets without an urban hospital from
which a hospital wage index can be
derived, all of the urban CBSA wage
index values within the State would be
used to calculate a statewide urban
average wage index to use as a
reasonable proxy for these areas.
Currently, the only CBSA that would be
affected by this policy is CBSA 25980,
Hinesville, Georgia. We propose to
continue this policy for CY 2008.
2. Update
Currently, the only rural areas where
there are no hospitals from which to
calculate a hospital wage index are
Massachusetts and Puerto Rico. For CY
2006, we adopted a policy in the HH
PPS November 9, 2005 final rule (70 FR
68138) of using the CY 2005 pre-floor,
pre-reclassified hospital wage index
value. In the August 3, 2006 proposed
rule, we again proposed to apply the CY
2005 pre-floor/pre-reclassified hospital
wage index to rural areas where no
hospital wage data is available. In
response to commenters’ concerns and
in recognition that, in the future, there
may be additional rural areas impacted
by a lack of hospital wage data from
which to derive a wage index, we
adopted, in the November 9, 2006 final
rule (71 FR 65905), the following
methodology for imputing a rural wage
index for areas where no hospital wage
data are available as an acceptable
proxy. The methodology that we
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implemented for CY 2007 imputed an
average wage index value by averaging
the wage index values from contiguous
CBSAs as a reasonable proxy for rural
areas with no hospital wage data from
which to calculate a wage index. We
believe this methodology best meets our
criteria for imputing a rural wage index
as well as representing an appropriate
wage index proxy for rural areas
without hospital wage data.
Specifically, such a methodology uses
pre-floor, pre-reclassified hospital wage
data, is easy to evaluate, is updateable
from year to year, and uses the most
local data available. In determining an
imputed rural wage index, we define
‘‘contiguous’’ as sharing a border. For
Massachusetts, rural Massachusetts
currently consists of Dukes and
Nantucket Counties. We determined
that the borders of Dukes and Nantucket
counties are ‘‘contiguous’’ with
Barnstable and Bristol counties. We are
again proposing to apply this
methodology for imputing a rural wage
index for those rural areas without rural
hospital wage data. While we continue
to believe that this policy could be
readily applied to other rural areas that
lack hospital wage data (possibly due to
hospitals converting to a different
provider type (such as a CAH) that does
not submit the appropriate wage data),
we specifically solicit comments on this
issue.
However, as we noted in the HH PPS
final rule for CY 2007, we did not
believe that this policy was appropriate
for Puerto Rico. As noted in the August
3, 2006 proposed rule, there are
sufficient economic differences between
the hospitals in the United States and
those in Puerto Rico, including the fact
that hospitals in Puerto Rico are paid on
blended Federal/Commonwealthspecific rates, that a separate distinct
policy for Puerto Rico is necessary.
Consequently, any alternative
methodology for imputing a wage index
for rural Puerto Rico would need to take
into account those differences. Our
policy of imputing a rural wage index
by using an averaged wage index of
CBSAs contiguous to that rural area
does not recognize the unique
circumstances of Puerto Rico. For CY
2008, we again propose to continue to
use the most recent wage index
previously available for Puerto Rico
which is 0.4047.
The rural and urban hospital wage
indexes can be found in Addenda A and
B of this proposed rule. For HH PPS
rates addressed in this proposed rule,
we are using the 2007 pre-floor and prereclassified hospital wage index data, as
2008 pre-floor and pre-reclassified
hospital wage index data are not yet
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available. We propose to use the 2008
pre-floor and pre-reclassified hospital
wage index (not including any
reclassification under section
1886(d)(8)(B) of the Act) to adjust rates
for CY 2008 and will publish those wage
index values in the final rule.
F. Home Health Care Quality
Improvement
Section 5201(c)(2) of the DRA added
section 1895(b)(3)(B)(v)(II) to the Act,
requiring that ‘‘each home health agency
shall submit to the Secretary such data
that the Secretary determines are
appropriate for the measurement of
health care quality. Such data shall be
submitted in a form and manner, and at
a time, specified by the Secretary for
purposes of this clause.’’ In addition,
section 1895(b)(3)(B)(v)(I) of the Act, as
also added by section 5201(c)(2) of the
DRA, dictates that ‘‘for 2007 and each
subsequent year, in the case of a home
health agency that does not submit data
to the Secretary in accordance with
subclause (II) with respect to such a
year, the home health market basket
percentage increase applicable under
such clause for such year shall be
reduced by 2 percentage points.’’
The OASIS data currently provide
consumers and HHAs with 10 publiclyreported home health quality measures
which have been endorsed by the
National Quality Forum (NQF).
Reporting these quality data have also
required the development of several
supporting mechanisms such as the
HAVEN software used to encode and
transmit data using a CMS standard
electronic record layout, edit
specifications, and data dictionary. The
HAVEN software includes the required
OASIS data set that has become a
standard part of HHA operations. These
early investments in data infrastructure
and supporting software that CMS and
HHAs have made over the past several
years in order to create this quality
reporting structure have been successful
in making quality reporting and
measurement an integral component of
the HHA industry. The 10 measures
are—
• Improvement in ambulation/
locomotion;
• Improvement in bathing;
• Improvement in transferring;
• Improvement in management of
oral medications;
• Improvement in pain interfering
with activity;
• Acute care hospitalization;
• Emergent care;
• Improvement in dyspnea;
• Improvement in urinary
incontinence; and
• Discharge to community.
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We are proposing to continue to use
OASIS data and the current 10 quality
measures, and to add two additional
quality measures based on those data for
the CY 2008 HH PPS quality data
reporting requirement. Continuing to
use the OASIS instrument ensures that
providers will not have an additional
burden of reporting through a separate
mechanism and that the costs associated
with the development and testing of a
new reporting mechanism can be
avoided. Accordingly, for CY 2008, we
propose to continue to use submission
of OASIS data to meet the requirement
that the HHA submit data appropriate
for the measurement of health care
quality.
We specifically propose to add the
following two additional quality
measures as data appropriate for
measuring health care quality. Adding
new measures to the currently available
outcome measures could broaden the
patient population we can assess,
expand the types of quality care we can
measure, and capture an aspect of care
directly under providers’ control. These
two wound measures focus on a
prevalent condition among home health
beneficiaries. We believe that by adding
these two measures, we can address
agencies’ ability to maintain patients in
their homes. These additional NQF
endorsed measures that will provide a
more complete picture of the level of
quality care delivered by HHAs are the
following:
• Emergent Care for Wound
Infections, Deteriorating Wound Status;
and
• Improvement in Status of Surgical
Wound.
The data elements used to calculate
these measures are already captured by
the OASIS instrument and do not
require additional reporting or burden
to HHAs.
Additionally, section
1895(b)(3)(B)(v)(II) of the Act provides
the Secretary with the discretion to
submit the required data in a form,
manner, and time specified by him. We
are proposing for CY 2008 to consider
OASIS data submitted by HHAs to CMS
for episodes beginning on or after July
1, 2006 and before July 1, 2007 as
meeting the reporting requirement for
CY 2008. This reporting time period
would allow 12 full months of data and
would provide us the time necessary to
analyze and make any necessary
payment adjustments to the CY 2008
payment rates. HHAs that meet the
reporting requirement would be eligible
for the full home health market basket
percentage increase.
We recognize, however, that the home
health conditions of participations
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25449
(CoPs) in (42 CFR part 484) that require
OASIS submission also provide for
exclusions from the CoP submission
requirement. Generally, agencies
excluded from the CoP OASIS
submission requirement do not receive
Medicare payments as they either do not
provide services to Medicare
beneficiaries or the patients are not
receiving Medicare-covered home
health services. Under the CoP, agencies
are excluded from the OASIS reporting
requirement on individual patients if—
• Those patients are receiving only
non-skilled services;
• Neither Medicare nor Medicaid is
paying for home health care (patients
receiving care under a Medicare or
Medicaid Managed Care Plan are not
excluded from the OASIS reporting
requirement);
• Those patients are receiving pre- or
post-partum services; and
• Those patients are under the age of
18 years.
We believe that the rationale behind
the exclusion of these agencies from
submission of OASIS on patients which
are excluded from OASIS CoP
submission is equally applicable to
HHAs for quality purposes. If an agency
is not submitting OASIS for patients
excluded from OASIS submission for
purposes of a CoP, we believe that the
submission of OASIS for quality
measures for Medicare purposes is
likewise not necessary. Therefore, we
propose that those agencies do not need
to submit quality measures for reporting
purposes for those patients who are
excluded from the OASIS CoP
submission.
Additionally, we propose that
agencies newly certified (on or after
May 31, 2007 for payments to be made
in CY 2008) be excluded from the
quality reporting requirement as data
submission and analysis would not be
possible for an agency certified this late
in the reporting time period. We again
propose that in future years, agencies
that certify on or after May 31 of the
preceding year involved be excluded
from any payment penalty for quality
reporting purposes for the following CY.
We note these exclusions only affect
quality reporting requirements and do
not affect the agency’s OASIS reporting
responsibilities under the CoP.
We propose to require that all HHAs,
unless covered by these specific
exclusions, meet the reporting
requirement, or be subject to a 2 percent
reduction in the home health market
basket percentage increase in
accordance with section
895(b)(3)(B)(v)(I) of the Act. The 2
percent reduction would apply to all
episode payments beginning on or after
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Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
January 1, 2008. We provide the
proposed reduced payment rates in
tables 25 and 26. We would reconcile
the OASIS submissions with claims data
in order to verify full compliance with
the quality reporting requirements.
For episodes that begin in CY 2007
and end in CY 2008, the new proposed
153 HHRG case-mix model (and
associated Grouper) would not yet be in
effect. For that reason, we propose, for
HHAs that do not submit required
quality data (for episodes that begin in
CY 2007 and end in CY 2008), the
following: First, we update the CY 2007
rate of $2,339.00 by the home health
market basket percentage update (2.9
percent) minus 2 percent, reduced by
2.75 percent to account for nominal
change in case-mix, and multiplied by
1.05 and 0.958614805 to account for the
estimated percentage of outlier
payments as a result of the current FDL
ratio of 0.67 ($2,339.00 * 1.009 * .9725
* 1.05 * 0.958614805), to yield an
updated CY 2008 national standardized
60-day episode payment rate of
$2,310.17 for episodes that begin in CY
2007 and end in CY 2008 for HHAs that
do not submit required quality data (see
Table 25a).
These episodes would be further
adjusted for case-mix based on the 80
HHRG case-mix model for episodes
beginning in CY 2007 and ending in CY
2008.
TABLE 25A.—FOR HHAS THAT DO NOT SUBMIT THE REQUIRED QUALITY DATA-PROPOSED NATIONAL 60-DAY EPISODE
AMOUNTS UPDATED BY THE ESTIMATED HOME HEALTH MARKET BASKET UPDATE FOR CY 2008, MINUS 2 PERCENTAGE POINTS, FOR EPISODES THAT BEGIN IN CY 2007 AND END IN CY 2008 BEFORE CASE-MIX ADJUSTMENT, WAGE
INDEX ADJUSTMENT BASED ON THE SITE OF SERVICE FOR THE BENEFICIARY OR APPLICABLE PAYMENT ADJUSTMENT
Multiply by the
proposed estimated home
health market
basket update
(2.9 percent)1
Minus 2 percent
Total CY 2007 national standardized 60-Day episode payment rate
$2,339.00 .........................................................................................................
× 1.009
Reduce by
2.75 percent
for nominal
change in
case-mix
× 0.9725
Adjusted to
account for the
5 percent
outlier policy
Proposed national standardized 60-day
episode payment rate for
episodes beginning in CY
2007 and ending in CY 2008
for HHAs that
do not submit
required quality data
× 1.05
× 0.958614805
$2,310.17
1 The estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006.
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Next, in order to establish new rates
based on a proposed new case-mix
system, we again start with the CY 2007
national standardized 60-day episode
payment rate and increase that rate by
the proposed estimated rebased and
revised home health market basket
update (2.9 percent) minus 2 percent
($2,339.00 * 1.009 = $2,360.05). We next
have to put dollars associated with the
outlier target estimate back into the base
rate. In the 2000 HH PPS final rule (65
FR 41184), we divided the base rate by
1.05 to account for outlier payments.
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Therefore, we are proposing to multiply
the $2,360.05 by 1.05, resulting in
$2,478.05. Next we need to reduce this
amount to pay for each of our proposed
policies. To do this, we take the
payment adjustment amount to pay for
our proposed policies of this rule,
determined in Table 23a of $226.57,
multiply it by (1/1.029) to take away the
2.9 percent increase, and multiply that
number by 1.009 to impose the 0.9
percent update for episodes where
HHAs have not submitted the required
quality data. This results in a payment
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adjustment amount of $222.17. Finally,
subtract the payment adjustment
amount of $222.17 from $2,478.05, for a
final rate of $2,255.88 for HHAs that do
not submit quality data, for episodes
that begin and end in CY 2008.
These episodes would be further
adjusted for case-mix based on the 153
HHRG case-mix model for episodes
beginning and ending in CY 2008. As
we noted in section II.A.2.d., we
increased the case-mix weights by a
budget neutrality factor of 1.194227193.
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25451
TABLE 25B.—FOR HHAS THAT DO NOT SUBMIT THE REQURIED QUALITY DATA-PROPOSED NATIONAL 60-DAY EPISODE
AMOUNTS UPDATED BY THE ESTIMATED HOME HEALTH MARKET BASKET UPDATE FOR CY 2008, MINUS 2 PERCENTAGE POINTS, FOR EPISODES THAT BEGIN AND END IN CY 2008, BEFORE CASE-MIX ADJUSTMENT, WAGE INDEX ADJUSTMENT BASED ON THE SITE OF SERVICE FOR THE BENEFICIARY OR APPLICABLE PAYMENT ADJUSTMENT
Total CY 2007 national standardized 60-day episode payment rate
Multiply by the
proposed estimated home
health market
basket update
(2.9 percent) 1
Adjusted to return the outlier
funds to the
national standardized 60-day
episode payment rate
Updated and
outlier adjusted national
standardized
60-day episode payment
Changes to
account for
LUPA adjustment ($6.46),
NRS payment
($40.88), elimination of SCIC
policy
($15.71),
outlier target
($94.02), and
2.75 percent
reduction for
nominal
change in
case-mix
($69.50) =
$226.57;
minus 2 percentage points
off of the
home health
market basket
update (2.9
Percent) 1 for
episodes beginning and
ending in CY
2008
$2,339.00 .............................................................................
× 1.009
× 1.05
$2,478.05
¥$222.17
Proposed CY
2008 national
standardized
60-day episode payment
rate for episodes beginning and ending in CY 2008
$2,255.88
1 The
estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006.
In calculating the proposed CY 2008
national per-visit amounts used to
calculate payments for LUPA episodes
for HHAs that do not submit required
quality data and to compute the
imputed costs in outlier calculations for
those episodes, we are proposing to start
with the CY 2007 per-visit rates. We
propose to multiply those amounts by
the proposed estimated home health
market basket update (2.9 percent)
minus 2 percentage points, then
multiply by 1.05 and 0.958614805 to
account for the estimated percentage of
outlier payments as a result of the
current FDL ratio of 0.67, to yield the
updated per-visit amounts for each
home health discipline for CY 2008 for
HHAs that do not submit required
quality data.
TABLE 26.—FOR HHAS THAT DO NOT SUBMIT THE REQUIRED QUALITY DATA-PROPOSED NATIONAL PER-VISIT AMOUNTS
FOR LUPAS (NOT INCLUDING THE INCREASE IN PAYMENT FOR A BENEFICIARY’S ONLY EPISODE OR THE INITIAL EPISODE IN A SEQUENCE OF ADJACENT EPISODES) AND OUTLIER CALCULATIONS UPDATED BY THE ESTIMATED HOME
HEALTH MARKET BASKET UPDATE FOR CY 2008, MINUS 2 PERCENTAGE POINTS, BEFORE WAGE INDEX ADJUSTMENT
BASED ON THE SITE OF SERVICE FOR THE BENEFICIARY
Final CY 2007
per-visit
amounts per
60-day episode
for LUPAs
Home health discipline type
Multiply by the
proposed estimated home
health market
basket
(2.9 percent) 1
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Home Health Aide ...................................................................................
$46.24
×1.009
Medical Social Services ...........................................................................
163.68
×1.009
Occupational Therapy ..............................................................................
112.40
×1.009
Physical Therapy .....................................................................................
111.65
×1.009
Skilled Nursing .........................................................................................
102.11
..........................
×1.009
..........................
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Adjusted to
account for the
5 percent outlier
policy
×1.05
×0.958614805
×1.05
× 0.958614805
×10.5
×0.958614805
× 1.05
×0.958614805
×1.05
×0.958614805
04MYP2
Proposed CY
2008 per-visit
payment
amount per
discipline for a
beneficiary who
resides in a
non-MSA for
HHAs that do
not submit
required quality
data
$46.96
.
166.23
114.15
113.39
103.70
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TABLE 26.—FOR HHAS THAT DO NOT SUBMIT THE REQUIRED QUALITY DATA-PROPOSED NATIONAL PER-VISIT AMOUNTS
FOR LUPAS (NOT INCLUDING THE INCREASE IN PAYMENT FOR A BENEFICIARY’S ONLY EPISODE OR THE INITIAL EPISODE IN A SEQUENCE OF ADJACENT EPISODES) AND OUTLIER CALCULATIONS UPDATED BY THE ESTIMATED HOME
HEALTH MARKET BASKET UPDATE FOR CY 2008, MINUS 2 PERCENTAGE POINTS, BEFORE WAGE INDEX ADJUSTMENT
BASED ON THE SITE OF SERVICE FOR THE BENEFICIARY—Continued
Final CY 2007
per-visit
amounts per
60-day episode
for LUPAs
Home health discipline type
Speech-Language Pathology ..................................................................
Multiply by the
proposed estimated home
health market
basket
(2.9 percent) 1
121.22
×1.009
Adjusted to
account for the
5 percent outlier
policy
Proposed CY
2008 per-visit
payment
amount per
discipline for a
beneficiary who
resides in a
non-MSA for
HHAs that do
not submit
required quality
data
×1.05
×0.958614805
123.11
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The estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006.
Section 1895(b)(3)(B)(v)(III) of the Act
further requires that the ‘‘Secretary shall
establish procedures for making data
submitted under subclause (II) available
to the public.’’ Additionally, the statute
requires that ‘‘such procedures shall
ensure that a home health agency has
the opportunity to review the data that
is to be made public with respect to the
agency before such data being made
public.’’ To meet the requirement for
making such data public, we are
proposing to continue to use the Home
Health Compare Web site whereby
HHAs are listed geographically.
Currently, the 10 existing quality
measures are posted on the Home
Health Compare Web site. The Home
Health Compare Web site will also
include the two proposed additional
measures discussed earlier. Consumers
can search for all Medicare-approved
home health providers that serve their
city or zip code and then find the
agencies offering the types of services
they need as well as the proposed
quality measures. See https://
www.medicare.gov/HHCompare/
Home.asp. HHAs currently have access
(through the Home Health Compare
contractor) to their own agency’s quality
data (updated periodically) and we
propose to continue this process thus
enabling each agency to know how it is
performing before public posting of data
on the Home Health Compare Web site.
Over the next year, we will be testing
patient level process measures for
HHAs, as well as continuing to refine
the current OASIS tool in response to
recommendations from a TEP
conducted to review the data elements
that make up the OASIS tool. We expect
to introduce these complementary
additional measures during CY 2008 to
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determine if they should be
incorporated into the statutory quality
measure reporting requirements. We
hope to apply these measures to the CY
2010 reporting period. Before usage in
the HH PPS, we will test and refine
these measures to determine if they can
more accurately reflect the level of
quality care being provided at HHAs
without being overly burdensome with
the data collection instrument. To the
extent that evidence-based data are
available on which to determine the
appropriate measure specifications, and
adequate risk-adjustments are made, we
anticipate collecting and reporting these
measures as part of each agency’s home
health quality plan. We believe that
future modifications to the current
OASIS tool, refinements to the possible
responses as well as adding new process
measures will be made. In all cases, we
anticipate that any future quality
measures should be evidence-based,
clearly linked to improved outcomes,
and able to be reliably captured with the
least burden to the provider. We are also
working on developing measures of
patient experience in the home health
setting through the development of the
Home Health Consumer Assessment of
Healthcare Providers and Systems
(CAHPS) Survey. We will be working
with the Agency for Healthcare
Research and Quality (AHRQ) to field
test this instrument in summer/fall
2007. We anticipate implementing the
Home Health CAHPS Survey in late
2008 for potential application to the CY
2010 pay for reporting requirements.
III. Collection of Information
Requirements
Under the Paperwork Reduction Act
(PRA) of 1995, we are required to
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provide 60-day notice in the Federal
Register and solicit public comment
before a collection of information
requirement is submitted to the Office of
Management and Budget (OMB) for
review and approval. In order to fairly
evaluate whether an information
collection should be approved by OMB,
section 3506(c)(2)(A) of the PRA of 1995
requires that we solicit comment on the
following issues:
• The need for the information
collection and its usefulness in carrying
out the proper functions of our agency.
• The accuracy of our estimate of the
information collection burden.
• The quality, utility, and clarity of
the information to be collected.
• Recommendations to minimize the
information collection burden on the
affected public, including automated
collection techniques.
Therefore, we are soliciting public
comments on each of these issues for
the information collection requirements
discussed below.
To implement the OASIS changes
discussed in sections II.A.(2)(a),
II.A.(2)(b), and II.A.(2)(c) of this
proposed rule, which are currently
approved in § 484.55, § 484.205, and
§ 484.250, a few items in the OASIS will
need to be modified, deleted, or added.
The requirements and burden associated
with the OASIS are currently approved
under OMB control number 0938–0760
with an expiration date of August 31,
2007. We are soliciting public comment
on each of the proposed changes for the
information collection requirements
(ICRs) as summarized and discussed
below. For the purposes of soliciting
public review and comment, we have
placed a current draft of the proposed
changes to the OASIS on the CMS Web
site at: https://www.cms.hhs.gov/
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PaperworkReductionActof1995/PRAL/
list.asp#TopOfPage.
As discussed in section II.A.(2)(a) of
this proposed rule, in order for the
OASIS to have the information
necessary to allow the grouper to priceout the claim, we propose to make the
following changes to the OASIS to
capture whether an episode is an early
or later episode:
The creation of a new OASIS item to
capture whether a particular
assessment, is for an episode considered
to be an early episode or a later episode
in the patient’s current sequence of
adjacent Medicare home health payment
episodes. As defined in section II.A.1. of
this proposed rule, we defined a
sequence of adjacent episodes for a
beneficiary as a series of claims with no
more than 60-days without home care
between the end of one episode, which
is the 60th day (except for episode that
have been PEP-adjusted), and the
beginning of the next episode. This
definition holds true regardless of
whether or not the same HHA provided
care for the entire sequence of adjacent
episodes. The HHA will chose from the
options: ‘‘Early’’ for single episodes or
the first or second episode in a sequence
of adjacent episodes, ‘‘Later’’ for third or
later episodes, ‘‘UK’’ for unknown if the
HHA is uncertain as to whether the
episode is an early or later episode (the
payment grouper software will default
to the definition of an ‘‘early’’ episode),
and ‘‘NA’’ for not applicable (no
Medicare case-mix group to be defined
by this assessment).
As discussed in section II.A.(2)(b) of
this proposed rule, we propose to make
changes to the OASIS in order to enable
agencies to report secondary case-mix
diagnosis codes. The proposed changes
clarify how to appropriately fill out
OASIS items M0230 and M0240, using
ICD–9–CM sequencing requirements if
multiple coding is indicated for any
diagnosis. Additionally, if a V-code is
reported in place of a case-mix
diagnosis for OASIS item M0230 or
M0240, then the new optional OASIS
item (which is replacing existing OASIS
item M0245) may then be completed. A
case-mix diagnosis is a diagnosis that
determines the HH PPS case-mix group.
As discussed in section II.A.(2)(c) of
this proposed rule, we propose to make
changes to the OASIS to capture the
projected total number of therapy visits
for a given episode. With the projected
total number of therapy visits, the
payment grouper would be able to group
that episode into the appropriate casemix group for payment. The existing
OASIS item M0825 asks an HHA if the
projected number of therapy visits
would meet the therapy threshold or
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not. As noted previously, we propose to
delete OASIS item M0825 and replace it
with a new OASIS item. The OASIS
item would ask the following: ‘‘In the
plan of care for the Medicare payment
episode for which this assessment will
define a case-mix group, what is the
indicated need for therapy visits (total
of reasonable and necessary physical,
occupational, and speech-pathology
visits combined)?’’ The HHA would
provide the total number of projected
therapy visits for that Medicare payment
episode, unless not applicable (that is,
no case-mix group defined by this
assessment). The HHA would enter
‘‘000’’ if no therapy visits were projected
for that particular episode.
The burden associated with the
proposed changes discussed in sections
II.A.(2)(a), II.A.(2)(b), and II.A.(2)(c) of
this rule includes possible training of
staff, the time and effort associated with
downloading a new form and replacing
previously pre-printed versions of the
OASIS, and utilizing updated vendor
software. However, as stated above,
CMS would be removing or modifying
existing questions in the OASIS data set
to accommodate the proposed
requirements referenced above. In
addition, as a result of the proposed
changes of this rule, we expect that the
claims processing system is expected to
automatically adjust the therapy visits,
upward and downward on the final
claim, according to the information on
the final claim.
Consequently, the HHA would no
longer have to withdraw and resubmit a
revised claim when the number of
therapy visits delivered to the patient is
higher than the level report on the RAP.
Therefore, CMS believes the burden
increase associated with these changes
is negated by the removal or
modification of several current data
items.
We have submitted a copy of this
proposed rule to OMB for its review of
the information collection requirements
described above. These requirements are
not effective until OMB has approved
them.
If you comment on any of these
information collection and record
keeping requirements, please mail
copies directly to the following:
Centers for Medicare & Medicaid
Services, Office of Strategic
Operations and Regulatory Affairs,
Regulations Development Group,
Attn.: Melissa Musotto, CMS–1541–P,
Room C4–26–05, 7500 Security
Boulevard, Baltimore, MD 21244–
1850; and Office of Information and
Regulatory Affairs, Office of
Management and Budget, Room
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25453
10235, New Executive Office
Building, Washington, DC 20503,
Attn: Carolyn Lovett, CMS Desk
Officer, (CMS–1541–P),
carolyn_lovett@omb.eop.gov. Fax
(202) 395–6974.
IV. Response to Comments
Because of the large number of public
comments normally receive on Federal
Register documents, we are not able to
acknowledge or respond to them
individually. We will consider all
comments we receive by the date and
time specified in the DATES section of
this proposed rule, and, when we
proceed with subsequent document, we
will respond to the comments in the
preamble to that document.
V. Regulatory Impact Analysis
[If you choose to comment on issues
in this section, please include the
caption ‘‘REGULATORY IMPACT
ANALYSIS’’ at the beginning of your
comments.]
A. Overall Impact
We have examined the impacts of this
rule as required by Executive Order
12866 (September 1993, Regulatory
Planning and Review), the Regulatory
Flexibility Act (RFA) (September 19,
1980, Pub. L. 96–354), section 1102(b) of
the Social Security Act, the Unfunded
Mandates Reform Act of 1995 (Pub. L.
104–4), and Executive Order 13132.
Executive Order 12866 (as amended
by Executive Order 13258, which
merely reassigns responsibility of
duties) directs agencies to assess all
costs and benefits of available regulatory
alternatives and, if regulation is
necessary, to select regulatory
approaches that maximize net benefits
(including potential economic,
environmental, public health and safety
effects, distributive impacts, and
equity). A regulatory impact analysis
(RIA) must be prepared for major rules
with economically significant effects
($100 million or more in any 1 year).
This proposed rule would be a major
rule, as defined in Title 5, United States
Code, section 804(2), because we
estimate the impact to the Medicare
program, and the annual effects to the
overall economy, would be more than
$100 million. The update set forth in
this proposed rule would apply to
Medicare payments under the HH PPS
in CY 2008.
Accordingly, the following analysis
describes the impact in CY 2008 only.
We estimate that the net impact of the
proposals in this rule, including a 2.75
percent reduction to the case-mix
weights to account for nominal increase
in case-mix, is estimated to be
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approximately $140 million in CY 2008
expenditures. That estimate
incorporates the 2.9 percent home
health market basket increase (an
estimated additional $410 million in CY
2008 expenditures attributable only to
the CY 2008 proposed estimated home
health market basket update), an
estimated additional $130 million due
to the increase in the HH PPS rates as
a result of maintaining a FDL ratio of
0.67, and the 2.75 percent decrease
(¥$400 million for the first year of a 3year phase-in) to the HH PPS national
standardized 60-day episode rate to
account for the nominal increase in
case-mix under the HH PPS. Given that
we allowed for a FDL ratio of 0.67, all
HH PPS rates were adjusted slightly
upward by a factor of 0.008614805.
Column 6 of Table 27 displays a 0.95
percent increase in expenditures when
comparing the CY 2007 current system
to the proposed revised CY 2008 system.
This equates to approximately $140
million and is driven primarily by the
adjustment made to maintain the FDL
ratio at 0.67 and partially by the
difference between the 2.9 percent
update and the 2.75 percent reduction
to the HH PPS rates.
The RFA requires agencies to analyze
options for regulatory relief of small
businesses. For purposes of the RFA,
small entities include small businesses,
nonprofit organizations, and small
governmental jurisdictions. Most
hospitals and most other providers and
suppliers are small entities, either by
nonprofit status or by having revenues
of $6 million to $29 million in any 1
year. For purposes of the RFA,
approximately 75 percent of HHAs are
considered small businesses according
to the Small Business Administration’s
size standards with total revenues of
$11.5 million or less in any 1 year.
Individuals and States are not included
in the definition of a small entity. As
stated above, this proposed rule would
have an estimated positive effect upon
small entities that are HHAs.
In addition, section 1102(b) of the Act
requires us to prepare a regulatory
impact analysis if a rule may have a
significant impact on the operations of
a substantial number of small rural
hospitals. This analysis must conform to
the provisions of section 603 of the
RFA. For purposes of section 1102(b) of
the Act, we define a small rural hospital
as a hospital that is located outside of
a Metropolitan Statistical Area and has
fewer than 100 beds. We have
determined that this proposed rule
would not have a significant economic
impact on the operations of a substantial
number of small rural hospitals.
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Section 202 of the Unfunded
Mandates Reform Act of 1995 also
requires that agencies assess anticipated
costs and benefits before issuing any
rule that may result in expenditure in
any 1 year by State, local, or tribal
governments, in the aggregate, or by the
private sector, of $110 million. We
believe this proposed rule would not
mandate expenditures in that amount.
Executive Order 13132 establishes
certain requirements that an agency
must meet when it promulgates a
proposed rule (and subsequent final
rule) that imposes substantial direct
requirement costs on State and local
governments, preempts State law, or
otherwise has Federalism implications.
We have determined that this proposed
rule would not have substantial direct
effects on the rights, roles, and
responsibilities of States.
B. Anticipated Effects
This proposed rule would update the
HH PPS rates contained in the CY 2007
final rule (71 FR 65884, November 9,
2006). The impact analysis of this
proposed rule presents the refinement
related policy changes proposed in this
rule. We use the best data available, but
we do not attempt to predict behavioral
responses to these changes, and we do
not make adjustments for future changes
in such variables as days or case-mix.
This analysis incorporates the latest
estimates of growth in service use and
payments under the Medicare home
health benefit, based on the latest
available Medicare claims from 2003.
We note that certain events may
combine to limit the scope or accuracy
of our impact analysis, because such an
analysis is future-oriented and, thus,
susceptible to forecasting errors due to
other changes in the forecasted impact
time period. Some examples of such
possible events are newly-legislated
general Medicare program funding
changes made by the Congress, or
changes specifically related to HHAs. In
addition, changes to the Medicare
program may continue to be made as a
result of the BBA, the BBRA, the
Medicare, Medicaid, and SCHIP
Benefits Improvement and Protection
Act of 2000, the MMA, the DRA, or new
statutory provisions. Although these
changes may not be specific to the HH
PPS, the nature of the Medicare program
is such that the changes may interact,
and the complexity of the interaction of
these changes could make it difficult to
predict accurately the full scope of the
impact upon HHAs.
Table 27 represents how home health
agencies are likely to be affected by the
policy changes described in this rule.
For each agency type listed below, Table
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27 displays the average case-mix index,
both under the current HH PPS case-mix
system and the proposed CY 2008 HH
PPS case-mix system. For this analysis,
we used the most recent data available
that linked home health claims and
OASIS assessments, a 10 percent sample
of episodes occurring in FY 2003. In
Table 27, the average case-mix is the
same, in the aggregate, between the
current HH PPS system and the
proposed revised HH PPS system, due
to our application of a budget neutrality
factor for the case-mix weights. Column
one of this table classifies HHAs
according to a number of characteristics
including provider type, geographic
region, and urban versus rural location.
Column two displays the average casemix weight for each type of agency
under the current payment system.
Column three displays the average casemix weight for each type of agency
incorporating all of the changes/
refinements discussed above. The
average case-mix weight for proprietary
(for profit) agencies is estimated to
decrease from 1.2601 to 1.2227.
Comparatively, the average case-mix
weight for voluntary non-profit agencies
is estimated to increase from 1.1404 to
1.1716. Rural agencies are estimated to
experience a decrease in their average
case-mix from 1.1583 to 1.1417. It is
estimated that urban agencies would see
a slight increase in their average casemix weight from 1.2032 to 1.2074. In
particular, the New England, MidAtlantic, East North Central, Mountain,
and West North Central areas of the
country are estimated to see their
average case-mix increase under the
proposed refinements of this rule.
Conversely, the West South Central,
East South Central, Pacific, and South
Atlantic areas of the country are
estimated to see their average case-mix
decrease as a result of proposed
refinements of this rule. Both small and
large agencies are estimated to see
decreases in their average case-mix
under the new proposed case-mix
system, the only exception being much
larger agencies (200+ first episodes),
which are estimated to see an increase
of their average case-mix from 1.1769 to
1.1920.
For the purposes of analyzing impacts
on payments, we performed three
simulations and compared them to each
other. The first simulation estimated
2007 payments under the current
system. The second simulation
estimated 2008 payments as though
there would be no changes to the
payment system other than the rebased
and revised home health market basket
increase of 2.9 percent. The second
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simulation produces an estimate of what
total payments using the sample data
would be in 2008 without making any
of the proposed changes described in
this proposed rule.
The third simulation estimates what
total payments would be in 2008, using
the proposed case-mix model, the
proposed additional payment for initial
and only episode LUPA episodes, the
proposed removal of SCIC adjustments,
and the proposed revised approach to
making NRS payments. The third
simulation also assumed payments
would incorporate the rebased and
revised home health market basket
increase of 2.9 percent, the current
outlier threshold determined by a FDL
ratio of 0.67, and the 2.75 percent
reduction in the national standardized
60-day episode payment rate to account
for the proposed nominal change in
case-mix. All three simulations used the
same CBSA wage index (we used a
crosswalk from the MSA reported on the
2003 claims to the CBSA to determine
the appropriate wage index). The results
of comparing these simulations are
displayed in columns four, five, and six
of Table 27.
Column four shows the percentage
change in estimated total payments in
moving from CY 2007 to a CY 2008
system incorporating none of the
proposed refinements to the HH PPS
except for the rebased and revised home
health market basket increase of 2.9
percent. Column five shows the
percentage change in estimated total
payments in moving from a CY 2008
system that incorporates none of the
proposed changes to the HH PPS except
for the rebased and revised home health
market basket increase of 2.9 percent to
the proposed revised CY 2008 system of
this rule. Finally, column six shows the
percentage change in estimated total
payments in moving from CY 2007 to
the proposed revised CY 2008 system of
this rule.
In general terms, the percentage
change in estimated total payments from
CY 2007 to a CY 2008 system that
incorporates none of the proposed
refinements to the HH PPS except for
the rebased and revised home health
market basket update of 2.9 percent is
approximately the home health market
basket increase of 2.9 percent. Some of
the classifications of HHAs show a
slightly less than 2.9 percent increase in
this comparison, which is due to the CY
2007 system incorporating the current
labor share, which is slightly less than
the labor share being proposed for the
CY 2008 system.
When comparing a CY 2008 system
that incorporates none of the
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refinements to the HH PPS except for
the rebased and revised home health
market basket increase of 2.9 percent
with the proposed revised CY 2008
system of this rule, it is estimated that
under the proposed revised CY 2008
system of this rule, total estimated
payments would decrease by
approximately 1.88 percent.
Comparatively, the percentage change in
estimated total payments from CY 2007
to the proposed revised CY 2008 system
of this rule is an increase of just under
1 percent (0.95 percent). All three
simulations incorporate a FDL ratio of
0.67. By maintaining the FDL ratio of
0.67, we believe we will continue to
meet the statutory requirement of
having an outlier payment outlay that
does not exceed 5 percent of total HH
PPS payments. In maintaining a 0.67
FDL ratio for CY 2008, in order to
maintain budget neutrality (other than
the 2.75 percent reduction to the HH
PPS rates to account for nominal casemix change), HH PPS rates are increased
slightly, as stated earlier in this section.
In general, voluntary non-profit HHAs
(3.56 percent), facility-based HHAs (3.50
percent), government owned HHAs
(3.04 percent) and free-standing HHAs
(0.10 percent) are estimated to see an
increase in the percentage change in
estimated total payments from CY 2007
to the proposed revised CY 2008 system.
Proprietary HHAs, on the other hand are
estimated to see a decrease of 1.90
percent in estimated total payments
from CY 2007 to the proposed revised
CY 2008 system. The major contributor
to this decrease of 1.90 percent is the
free-standing proprietary HHAs, which
are estimated to see a decrease of
slightly more than 2 percent in the
percentage change in estimated total
payment from CY 2007 to the proposed
revised CY 2008 system.
We note that some of these impacts
are partly explained by practice patterns
associated with certain types of
agencies. For example, LUPA episodes
are relatively common among nonprofit
agencies and freestanding governmentowned agencies. Our proposal for an
additional payment for certain LUPA
episodes would tend to increase
payments for such classes of agencies
with higher-than-average LUPA rates,
while tending to decrease payments for
agencies with comparatively low LUPA
rates. Similarly, the proposed
elimination of the SCIC policy would
tend to favorably affect total payments
for agencies with relatively high rates of
SCIC episodes, such as facility-based
proprietary agencies and facility-based
government agencies. The percentage
change in estimated total payments from
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25455
CY 2007 to a CY 2008 system that
incorporates all of the refinements to the
HH PPS for rural HHAs is a slight
decrease of 0.50 percent, while for
urban HHAs an increase of 1.26 percent
is expected. Urban agencies have
somewhat higher LUPA rates than rural
agencies, so urban agencies would be
expected to benefit, relative to rural
agencies, from the proposal to make an
additional payment for certain LUPA
episodes. Urban agencies are also more
likely to benefit from elimination of the
SCIC policy. Urban agencies are less
likely to bill a SCIC episode than rural
agencies. However, when urban
agencies do bill a SCIC episode the
payment is reduced more, on average,
than when rural agencies bill a SCIC.
The net effect of these two components
(relative frequency and payment impact
per SCIC episode) is a larger expected
reduction for urban agencies under the
SCIC adjustment policy. Therefore,
while both urban and rural agencies
benefit from eliminating the SCIC
policy, urban agencies benefit more.
HHAs in the North are expected to
experience a percentage change increase
of 4.33 percent in estimated total
payments from CY 2007 to the proposed
revised CY 2008 system. The only
region estimated to experience a
decrease in the percentage change in
estimated total payments from CY 2007
to the proposed revised CY 2008 system
is the South. That percentage change is
an estimated decrease of 1.84 percent. It
is estimated that New England and Mid
Atlantic area HHAs will experience
percentage change increases of slightly
more than 4 percent (New England, 4.10
percent and the Mid-Atlantic, 4.45
percent) in estimated total payments
from CY 2007 to the proposed revised
CY 2008 system. Conversely, West
South Central HHAs are expected to
experience a decrease (¥3.80 percent)
in the percentage change in estimated
total payments from CY 2007 to the
proposed CY 2008 system. In general,
smaller HHAs are expected to
experience a decrease (ranging from
¥0.63 percent to ¥2.76 percent) for
their percentage change in estimated
total payments from CY 2007 to the
proposed revised CY 2008 system.
Conversely, larger HHAs are estimated
to experience an increase (ranging from
0.59 percent to 2.16 percent) in the
percent change in estimated total
payments from CY 2007 to the proposed
CY 2008 system.
BILLING CODE 4120–01–P
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Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
C. Accounting Statement
have prepared an accounting statement
showing the classification of the
expenditures associated with the
provisions of this proposed rule. This
table provides our best estimate of the
increase in Medicare payments under
As Required by OMB Circular A–4
(available at https://
www.whitehouse.gov/omb/circulars/
a004/a-4.pdf), in Table 28 below, we
25457
the HH PPS as a result of the changes
presented in this proposed rule based
on the data for 8,164 HHAs in our
database. All expenditures are classified
as transfers to Medicare providers (that
is, HHAs).
TABLE 28.—ACCOUNTING STATEMENT: CLASSIFICATION OF ESTIMATED EXPENDITURES, FROM CY 2007 TO CY 2008
[In millions]
Category
Transfers
In accordance with the provisions of
Executive Order 12866, this regulation
was reviewed by the Office of
Management and Budget.
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List of Subjects in 42 CFR Part 484
Health facilities, Health professions,
Medicare, and Reporting and
recordkeeping requirements.
For the reasons set forth in the
preamble, the Centers for Medicare &
Medicaid Services would amend 42 CFR
chapter IV as set forth below:
PART 484—HOME HEALTH SERVICES
1. The authority citation for part 484
continues to read as follows:
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Authority: Secs. 1102 and 1871 of the
Social Security Act (42 U.S.C.1302 and
1395(hh)).
Subpart E—Prospective Payment
System for Home Health Agencies
§ 484.205
[Amended]
2. Amend § 484.205 by—
A. Removing paragraph (a)(3).
B. Redesignating paragraph (a)(4) as
paragraph (a)(3).
C. Revising paragraph (b) introductory
text.
D. Removing paragraph (e).
E. Redesignating paragraph (f) as
paragraph (e).
The revisions read as follows:
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$140.
Federal Government to HHAs.
§ 484.205
Basis of payment.
*
*
*
*
*
(b) Episode payment. The national
prospective 60-day episode payment
represents payment in full for all costs
associated with furnishing home health
services previously paid on a reasonable
cost basis (except the osteoporosis drug
listed in section 1861(m) of the Act as
defined in section 1861(kk) of the Act)
as of August 5, 1997 unless the national
60-day episode payment is subject to a
low-utilization payment adjustment set
forth in § 484.230, a partial episode
payment adjustment set forth at
§ 484.235, or an additional outlier
payment set forth in § 484.240. All
payments under this system may be
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Annualized Monetized Transfers ....................................................................................................
From Whom to Whom? ...................................................................................................................
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subject to a medical review adjustment
reflecting beneficiary eligibility, medical
necessity determinations, and HHRG
assignment. DME provided as a home
health service as defined in section
1861(m) of the Act continues to be paid
the fee schedule amount.
*
*
*
*
*
3. Revise § 484.220 to read as follows:
§ 484.220 Calculation of the adjusted
national prospective 60-day episode
payment rate for case-mix and area wage
levels.
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CMS adjusts the national prospective
60-day episode payment rate to account
for the following:
(a) HHA case-mix using a case-mix
index to explain the relative resource
utilization of different patients. To
address changes to the case-mix that are
a result of changes in the coding or
classification of different units of
service that do not reflect real changes
in case-mix, the national prospective 60-
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Jkt 220001
day episode payment rate will be
adjusted downward as follows:
(1) For CY 2008 the adjustment is 2.75
percent.
(2) For CY 2009 and CY 2010, the
adjustment is 2.75 percent in each year.
(b) Geographic differences in wage
levels using an appropriate wage index
based on the site of service of the
beneficiary.
4. Amend § 484.230 by adding a third,
fourth, and fifth sentence after the
second sentence to read as follows:
§ 484.230 Methodology used for the
calculation of the low-utilization payment
adjustment.
* * * For 2008 and subsequent
calendar years, an amount will be added
to low-utilization payment adjustments
for low-utilization episodes that occur
as the beneficiary’s only episode or
initial episode in a sequence of adjacent
episodes. For purposes of the home
health PPS, a sequence of adjacent
episodes for a beneficiary is a series of
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claims with no more than 60 days
without home care between the end of
one episode, which is the 60th day
(except for episodes that have been PEPadjusted), and the beginning of the next
episode. This additional amount will be
updated annually after 2008 by a factor
equal to the applicable home health
market basket percentage.
§ 484.237
[Removed]
5. Remove § 484.237.
(Catalog of Federal Domestic Assistance
Program No. 93.773, Medicare—Hospital
Insurance; and Program No. 93.774,
Medicare—Supplementary Medical
Insurance Program)
Dated: February 15, 2007.
Leslie V. Norwalk,
Acting Administrator, Centers for Medicare
& Medicaid Services.
Approved: April 2, 2007.
Michael O. Leavitt,
Secretary.
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25459
Note: The following addenda will not be
published in the Code of Federal Regulations.
ADDENDUM A.—CY 2007 WAGE INDEX ADDENDUM A.—CY 2007 WAGE INDEX
FOR RURAL AREAS BY CBSA; APFOR RURAL AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-REPLICABLE PRE-FLOOR AND PRE-READDENDUM A.—CY 2007 WAGE INDEX
CLASSIFIED
HOSPITAL
WAGE
CLASSIFIED
HOSPITAL
WAGE
FOR RURAL AREAS BY CBSA; APINDEX—Continued
INDEX—Continued
PLICABLE PRE-FLOOR AND PRE-RECBSA
Wage
CBSA
Wage
CLASSIFIED HOSPITAL WAGE INDEX
Nonurban area
Nonurban area
code
CBSA
code
01
02
03
04
05
06
07
08
10
11
12
13
14
15
16
17
18
19
20
21
22
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
Wage
index
Nonurban area
Alabama ..................
Alaska .....................
Arizona ....................
Arkansas .................
California .................
Colorado .................
Connecticut .............
Delaware .................
Florida .....................
Georgia ...................
Hawaii .....................
Idaho .......................
Illinois ......................
Indiana ....................
Iowa ........................
Kansas ....................
Kentucky .................
Louisiana ................
Maine ......................
Maryland .................
Massachusetts 1 ......
0.7592
1.0661
0.8909
0.7307
1.1454
0.9325
1.1709
0.9706
0.8594
0.7593
1.0449
0.8120
0.8320
0.8539
0.8682
0.7999
0.7769
0.7438
0.8443
0.8927
1.0661
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
.............
index
Michigan .................
Minnesota ...............
Mississippi ..............
Missouri ..................
Montana ..................
Nebraska ................
Nevada ...................
New Hampshire ......
New Jersey 1,2 ........
New Mexico ............
New York ................
North Carolina ........
North Dakota ..........
Ohio ........................
Oklahoma ...............
Oregon ....................
Pennsylvania ..........
Puerto Rico 3 ...........
Rhode Island 2 ........
South Carolina ........
South Dakota ..........
Tennessee ..............
Texas ......................
0.9063
0.9153
0.7738
0.7927
0.8590
0.8678
0.8944
1.0853
................
0.8333
0.8232
0.8589
0.7216
0.8659
0.7629
0.9753
0.8321
0.4047
................
0.8566
0.8480
0.7827
0.7965
code
46
47
48
49
50
51
52
53
65
.............
.............
.............
.............
.............
.............
.............
.............
.............
index
Utah ........................
Vermont ..................
Virgin Islands ..........
Virginia ....................
Washington .............
West Virginia ..........
Wisconsin ...............
Wyoming .................
Guam ......................
0.8141
0.9744
0.8467
0.7941
1.0263
0.7607
0.9553
0.9295
0.9611
1 All counties within the State are classified
as rural. No short-term, acute care hospitals
are located in the area(s). The rural wage
index for Massachusetts is imputed using the
methodology discussed in section II.E.2 of this
rule.
2 All counties within the State are classified
as urban.
3 All counties within the State are classified
as rural. No short-term, acute care hospitals
are located in the area(s). We will continue to
use the wage index from CY 2005, which was
the last year in which we had ‘‘rural’’ hospital
wage data for Puerto Rico.
ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX
CBSA
code
Urban area (constituent counties)
10180 .......
Abilene, TX ............................................................................................................................................................................
Callahan County, TX.
Jones County, TX.
Taylor County, TX.
´
Aguadilla-Isabela-San Sebastian, PR ...................................................................................................................................
Aguada Municipio, PR.
Aguadilla Municipio, PR.
˜
Anasco Municipio, PR.
Isabela Municipio, PR.
Lares Municipio, PR.
Moca Municipio, PR.
´
Rincon Municipio, PR.
´
San Sebastian Municipio, PR.
Akron, OH ..............................................................................................................................................................................
Portage County, OH.
Summit County, OH.
Albany, GA .............................................................................................................................................................................
Baker County, GA.
Dougherty County, GA.
Lee County, GA.
Terrell County, GA.
Worth County, GA.
Albany-Schenectady-Troy, NY ..............................................................................................................................................
Albany County, NY.
Rensselaer County, NY.
Saratoga County, NY.
Schenectady County, NY.
Schoharie County, NY.
Albuquerque, NM ...................................................................................................................................................................
Bernalillo County, NM.
Sandoval County, NM.
Torrance County, NM.
Valencia County, NM.
Alexandria, LA .......................................................................................................................................................................
Grant Parish, LA.
Rapides Parish, LA.
10380 .......
10420 .......
10500 .......
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10580 .......
10740 .......
10780 .......
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index
04MYP2
0.8001
0.3915
0.8654
0.8991
0.8720
0.9458
0.8006
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
Urban area (constituent counties)
10900 .......
Allentown-Bethlehem-Easton, PA-NJ ....................................................................................................................................
Warren County, NJ.
Carbon County, PA.
Lehigh County, PA.
Northampton County, PA.
Altoona, PA ............................................................................................................................................................................
Blair County, PA.
Amarillo, TX ...........................................................................................................................................................................
Armstrong County, TX.
Carson County, TX.
Potter County, TX.
Randall County, TX.
Ames, IA ................................................................................................................................................................................
Story County, IA.
Anchorage, AK .......................................................................................................................................................................
Anchorage Municipality, AK.
Matanuska-Susitna Borough, AK.
Anderson, IN ..........................................................................................................................................................................
Madison County, IN.
Anderson, SC ........................................................................................................................................................................
Anderson County, SC.
Ann Arbor, MI ........................................................................................................................................................................
Washtenaw County, MI.
Anniston-Oxford, AL ..............................................................................................................................................................
Calhoun County, AL.
Appleton, WI ..........................................................................................................................................................................
Calumet County, WI.
Outagamie County, WI.
Asheville, NC .........................................................................................................................................................................
Buncombe County, NC.
Haywood County, NC.
Henderson County, NC.
Madison County, NC.
Athens-Clarke County, GA ....................................................................................................................................................
Clarke County, GA.
Madison County, GA.
Oconee County, GA.
Oglethorpe County, GA.
Atlanta-Sandy Springs-Marietta, GA .....................................................................................................................................
Barrow County, GA.
Bartow County, GA.
Butts County, GA.
Carroll County, GA.
Cherokee County, GA.
Clayton County, GA.
Cobb County, GA.
Coweta County, GA.
Dawson County, GA.
DeKalb County, GA.
Douglas County, GA.
Fayette County, GA.
Forsyth County, GA.
Fulton County, GA.
Gwinnett County, GA.
Haralson County, GA.
Heard County, GA.
Henry County, GA.
Jasper County, GA.
Lamar County, GA.
Meriwether County, GA.
Newton County, GA.
Paulding County, GA.
Pickens County, GA.
Pike County, GA.
Rockdale County, GA.
Spalding County, GA.
Walton County, GA.
Atlantic City, NJ .....................................................................................................................................................................
Atlantic County, NJ.
Auburn-Opelika, AL ...............................................................................................................................................................
11020 .......
11100 .......
11180 .......
11260 .......
11300 .......
11340 .......
11460 .......
11500 .......
11540 .......
11700 .......
12020 .......
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12220 .......
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0.9947
0.8812
0.9161
0.9760
1.2024
0.8681
0.9017
1.0826
0.7770
0.9455
0.9077
0.9856
0.9762
1.1831
0.8096
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
12260 .......
12420 .......
12540 .......
12580 .......
12620 .......
12700 .......
12940 .......
12980 .......
13020 .......
13140 .......
13380 .......
13460 .......
13644 .......
13740 .......
13780 .......
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13900 .......
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Urban area (constituent counties)
Lee County, AL.
Augusta-Richmond County, GA-SC ......................................................................................................................................
Burke County, GA.
Columbia County, GA.
McDuffie County, GA.
Richmond County, GA.
Aiken County, SC.
Edgefield County, SC.
Austin-Round Rock, TX .........................................................................................................................................................
Bastrop County, TX.
Caldwell County, TX.
Hays County, TX.
Travis County, TX.
Williamson County, TX.
Bakersfield, CA ......................................................................................................................................................................
Kern County, CA.
Baltimore-Towson, MD ..........................................................................................................................................................
Anne Arundel County, MD.
Baltimore County, MD.
Carroll County, MD.
Harford County, MD.
Howard County, MD.
Queen Anne’s County, MD.
Baltimore City, MD.
Bangor, ME ............................................................................................................................................................................
Penobscot County, ME.
Barnstable Town, MA ............................................................................................................................................................
Barnstable County, MA.
Baton Rouge, LA ...................................................................................................................................................................
Ascension Parish, LA.
East Baton Rouge Parish, LA.
East Feliciana Parish, LA.
Iberville Parish, LA.
Livingston Parish, LA.
Pointe Coupee Parish, LA.
St. Helena Parish, LA.
West Baton Rouge Parish, LA.
West Feliciana Parish, LA.
Battle Creek, MI .....................................................................................................................................................................
Calhoun County, MI.
Bay City, MI ...........................................................................................................................................................................
Bay County, MI.
Beaumont-Port Arthur, TX .....................................................................................................................................................
Hardin County, TX.
Jefferson County, TX.
Orange County, TX.
Bellingham, WA .....................................................................................................................................................................
Whatcom County, WA.
Bend, OR ...............................................................................................................................................................................
Deschutes County, OR.
Bethesda-Frederick-Gaithersburg, MD ..................................................................................................................................
Frederick County, MD.
Montgomery County, MD.
Billings, MT ............................................................................................................................................................................
Carbon County, MT.
Yellowstone County, MT.
Binghamton, NY .....................................................................................................................................................................
Broome County, NY.
Tioga County, NY.
Birmingham-Hoover, AL ........................................................................................................................................................
Bibb County, AL.
Blount County, AL.
Chilton County, AL.
Jefferson County, AL.
St. Clair County, AL.
Shelby County, AL.
Walker County, AL.
Bismarck, ND .........................................................................................................................................................................
Burleigh County, ND.
Morton County, ND.
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0.8085
0.9763
0.9252
0.8595
1.1105
1.0743
1.0904
0.8713
0.8786
0.8994
0.7240
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
Urban area (constituent counties)
13980 .......
Blacksburg-Christiansburg-Radford, VA ................................................................................................................................
Giles County, VA.
Montgomery County, VA.
Pulaski County, VA.
Radford City, VA.
Bloomington, IN .....................................................................................................................................................................
Greene County, IN.
Monroe County, IN.
Owen County, IN.
Bloomington-Normal, IL .........................................................................................................................................................
McLean County, IL.
Boise City-Nampa, ID ............................................................................................................................................................
Ada County, ID.
Boise County, ID.
Canyon County, ID.
Gem County, ID.
Owyhee County, ID.
Boston-Quincy, MA ................................................................................................................................................................
Norfolk County, MA.
Plymouth County, MA.
Suffolk County, MA.
Boulder, CO ...........................................................................................................................................................................
Boulder County, CO.
Bowling Green, KY ................................................................................................................................................................
Edmonson County, KY.
Warren County, KY.
Bremerton-Silverdale, WA .....................................................................................................................................................
Kitsap County, WA.
Bridgeport-Stamford-Norwalk, CT .........................................................................................................................................
Fairfield County, CT.
Brownsville-Harlingen, TX .....................................................................................................................................................
Cameron County, TX.
Brunswick, GA .......................................................................................................................................................................
Brantley County, GA.
Glynn County, GA.
McIntosh County, GA.
Buffalo-Niagara Falls, NY ......................................................................................................................................................
Erie County, NY.
Niagara County, NY.
Burlington, NC .......................................................................................................................................................................
Alamance County, NC.
Burlington-South Burlington, VT ............................................................................................................................................
Chittenden County, VT.
Franklin County, VT.
Grand Isle County, VT.
Cambridge-Newton-Framingham, MA ...................................................................................................................................
Middlesex County, MA.
Camden, NJ ...........................................................................................................................................................................
Burlington County, NJ.
Camden County, NJ.
Gloucester County, NJ.
Canton-Massillon, OH ............................................................................................................................................................
Carroll County, OH.
Stark County, OH.
Cape Coral-Fort Myers, FL ....................................................................................................................................................
Lee County, FL.
Carson City, NV .....................................................................................................................................................................
Carson City, NV.
Casper, WY ...........................................................................................................................................................................
Natrona County, WY.
Cedar Rapids, IA ...................................................................................................................................................................
Benton County, IA.
Jones County, IA.
Linn County, IA.
Champaign-Urbana, IL ..........................................................................................................................................................
Champaign County, IL.
Ford County, IL.
Piatt County, IL.
Charleston, WV ......................................................................................................................................................................
Boone County, WV.
14020 .......
14060 .......
14260 .......
14484 .......
14500 .......
14540 .......
14740 .......
14860 .......
15180 .......
15260 .......
15380 .......
15500 .......
15540 .......
15764 .......
15804 .......
15940 .......
15980 .......
16180 .......
16220 .......
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16580 .......
16620 .......
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0.8213
0.8533
0.8945
0.9401
1.1679
1.0350
0.8148
1.0914
1.2659
0.9430
1.0165
0.9424
0.8674
0.9475
1.0970
1.0393
0.9032
0.9343
1.0026
0.9145
0.8888
0.9645
0.8543
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
16700 .......
16740 .......
16820 .......
16860 .......
16940 .......
16974 .......
17020 .......
17140 .......
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17420 .......
17460 .......
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Urban area (constituent counties)
Clay County, WV.
Kanawha County, WV.
Lincoln County, WV.
Putnam County, WV.
Charleston-North Charleston, SC ..........................................................................................................................................
Berkeley County, SC.
Charleston County, SC.
Dorchester County, SC.
Charlotte-Gastonia-Concord, NC-SC ....................................................................................................................................
Anson County, NC.
Cabarrus County, NC.
Gaston County, NC.
Mecklenburg County, NC.
Union County, NC.
York County, SC.
Charlottesville, VA .................................................................................................................................................................
Albemarle County, VA.
Fluvanna County, VA.
Greene County, VA.
Nelson County, VA.
Charlottesville City, VA.
Chattanooga, TN-GA .............................................................................................................................................................
Catoosa County, GA.
Dade County, GA.
Walker County, GA.
Hamilton County, TN.
Marion County, TN.
Sequatchie County, TN.
Cheyenne, WY .......................................................................................................................................................................
Laramie County, WY.
Chicago-Naperville-Joliet, IL ..................................................................................................................................................
Cook County, IL.
DeKalb County, IL.
DuPage County, IL.
Grundy County, IL.
Kane County, IL.
Kendall County, IL.
McHenry County, IL.
Will County, IL.
Chico, CA ...............................................................................................................................................................................
Butte County, CA.
Cincinnati-Middletown, OH-KY-IN .........................................................................................................................................
Dearborn County, IN.
Franklin County, IN.
Ohio County, IN.
Boone County, KY.
Bracken County, KY.
Campbell County, KY.
Gallatin County, KY.
Grant County, KY.
Kenton County, KY.
Pendleton County, KY.
Brown County, OH.
Butler County, OH.
Clermont County, OH.
Hamilton County, OH.
Warren County, OH.
Clarksville, TN-KY ..................................................................................................................................................................
Christian County, KY.
Trigg County, KY.
Montgomery County, TN.
Stewart County, TN.
Cleveland, TN ........................................................................................................................................................................
Bradley County, TN.
Polk County, TN.
Cleveland-Elyria-Mentor, OH .................................................................................................................................................
Cuyahoga County, OH.
Geauga County, OH.
Lake County, OH.
Lorain County, OH.
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0.9601
0.8436
0.8110
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
17660 .......
17780 .......
17820 .......
17860 .......
17900 .......
17980 .......
18020 .......
18140 .......
18580 .......
18700 .......
19060 .......
19124 .......
19140 .......
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19380 .......
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Urban area (constituent counties)
Medina County, OH.
Coeur d’Alene, ID ..................................................................................................................................................................
Kootenai County, ID.
College Station-Bryan, TX .....................................................................................................................................................
Brazos County, TX.
Burleson County, TX.
Robertson County, TX.
Colorado Springs, CO ...........................................................................................................................................................
El Paso County, CO.
Teller County, CO.
Columbia, MO ........................................................................................................................................................................
Boone County, MO.
Howard County, MO.
Columbia, SC .........................................................................................................................................................................
Calhoun County, SC.
Fairfield County, SC.
Kershaw County, SC.
Lexington County, SC.
Richland County, SC.
Saluda County, SC.
Columbus, GA-AL ..................................................................................................................................................................
Russell County, AL.
Chattahoochee County, GA.
Harris County, GA.
Marion County, GA.
Muscogee County, GA.
Columbus, IN .........................................................................................................................................................................
Bartholomew County, IN.
Columbus, OH .......................................................................................................................................................................
Delaware County, OH.
Fairfield County, OH.
Franklin County, OH.
Licking County, OH.
Madison County, OH.
Morrow County, OH.
Pickaway County, OH.
Union County, OH.
Corpus Christi, TX .................................................................................................................................................................
Aransas County, TX.
Nueces County, TX.
San Patricio County, TX.
Corvallis, OR ..........................................................................................................................................................................
Benton County, OR.
Cumberland, MD-WV .............................................................................................................................................................
Allegany County, MD.
Mineral County, WV.
Dallas-Plano-Irving, TX ..........................................................................................................................................................
Collin County, TX.
Dallas County, TX.
Delta County, TX.
Denton County, TX.
Ellis County, TX.
Hunt County, TX.
Kaufman County, TX.
Rockwall County, TX.
Dalton, GA .............................................................................................................................................................................
Murray County, GA.
Whitfield County, GA.
Danville, IL .............................................................................................................................................................................
Vermilion County, IL.
Danville, VA ...........................................................................................................................................................................
Pittsylvania County, VA.
Danville City, VA.
Davenport-Moline-Rock Island, IA-IL .....................................................................................................................................
Henry County, IL.
Mercer County, IL.
Rock Island County, IL.
Scott County, IA.
Dayton, OH ............................................................................................................................................................................
Greene County, OH.
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0.8934
0.8239
0.9318
1.0107
0.8564
1.1546
0.8447
1.0076
0.9093
0.9267
0.8451
0.8847
0.9037
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
19460 .......
19500 .......
19660 .......
19740 .......
19780 .......
19804 .......
20020 .......
20100 .......
20220 .......
20260 .......
20500 .......
20740 .......
20764 .......
20940 .......
21060 .......
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21300 .......
21340 .......
21500 .......
21604 .......
21660 .......
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Urban area (constituent counties)
Miami County, OH.
Montgomery County, OH.
Preble County, OH.
Decatur, AL ............................................................................................................................................................................
Lawrence County, AL.
Morgan County, AL.
Decatur, IL .............................................................................................................................................................................
Macon County, IL.
Deltona-Daytona Beach-Ormond Beach, FL .........................................................................................................................
Volusia County, FL.
Denver-Aurora, CO ................................................................................................................................................................
Adams County, CO.
Arapahoe County, CO.
Broomfield County, CO.
Clear Creek County, CO.
Denver County, CO.
Douglas County, CO.
Elbert County, CO.
Gilpin County, CO.
Jefferson County, CO.
Park County, CO.
Des Moines, IA ......................................................................................................................................................................
Dallas County, IA.
Guthrie County, IA.
Madison County, IA.
Polk County, IA.
Warren County, IA.
Detroit-Livonia-Dearborn, MI .................................................................................................................................................
Wayne County, MI.
Dothan, AL .............................................................................................................................................................................
Geneva County, AL.
Henry County, AL.
Houston County, AL.
Dover, DE ..............................................................................................................................................................................
Kent County, DE.
Dubuque, IA ...........................................................................................................................................................................
Dubuque County, IA.
Duluth, MN-WI .......................................................................................................................................................................
Carlton County, MN.
St. Louis County, MN.
Douglas County, WI.
Durham, NC ...........................................................................................................................................................................
Chatham County, NC.
Durham County, NC.
Orange County, NC.
Person County, NC.
Eau Claire, WI .......................................................................................................................................................................
Chippewa County, WI.
Eau Claire County, WI.
Edison, NJ .............................................................................................................................................................................
Middlesex County, NJ.
Monmouth County, NJ.
Ocean County, NJ.
Somerset County, NJ.
El Centro, CA .........................................................................................................................................................................
Imperial County, CA.
Elizabethtown, KY ..................................................................................................................................................................
Hardin County, KY.
Larue County, KY.
Elkhart-Goshen, IN ................................................................................................................................................................
Elkhart County, IN.
Elmira, NY ..............................................................................................................................................................................
Chemung County, NY.
El Paso, TX ............................................................................................................................................................................
El Paso County, TX.
Erie, PA ..................................................................................................................................................................................
Erie County, PA.
Essex County, MA .................................................................................................................................................................
Essex County, MA.
Eugene-Springfield, OR .........................................................................................................................................................
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1.0282
0.7381
0.9848
0.9134
1.0042
0.9826
0.9630
1.1190
0.9076
0.8698
0.9426
0.8240
0.9053
0.8828
1.0419
1.0877
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
21780 .......
21820 .......
21940 .......
22020 .......
22140 .......
22180 .......
22220 .......
22380 .......
22420 .......
22500 .......
22520 .......
22540 .......
22660 .......
22744 .......
22900 .......
23020 .......
23060 .......
23104 .......
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23460 .......
23540 .......
23580 .......
23844 .......
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Urban area (constituent counties)
Lane County, OR.
Evansville, IN-KY ...................................................................................................................................................................
Gibson County, IN.
Posey County, IN.
Vanderburgh County, IN.
Warrick County, IN.
Henderson County, KY.
Webster County, KY.
Fairbanks, AK ........................................................................................................................................................................
Fairbanks North Star Borough, AK.
Fajardo, PR ............................................................................................................................................................................
Ceiba Municipio, PR.
Fajardo Municipio, PR.
Luquillo Municipio, PR.
Fargo, ND-MN .......................................................................................................................................................................
Cass County, ND.
Clay County, MN.
Farmington, NM .....................................................................................................................................................................
San Juan County, NM.
Fayetteville, NC .....................................................................................................................................................................
Cumberland County, NC.
Hoke County, NC.
Fayetteville-Springdale-Rogers, AR-MO ...............................................................................................................................
Benton County, AR.
Madison County, AR.
Washington County, AR.
McDonald County, MO.
Flagstaff, AZ ..........................................................................................................................................................................
Coconino County, AZ.
Flint, MI ..................................................................................................................................................................................
Genesee County, MI.
Florence, SC ..........................................................................................................................................................................
Darlington County, SC.
Florence County, SC.
Florence-Muscle Shoals, AL ..................................................................................................................................................
Colbert County, AL.
Lauderdale County, AL.
Fond du Lac, WI ....................................................................................................................................................................
Fond du Lac County, WI.
Fort Collins-Loveland, CO .....................................................................................................................................................
Larimer County, CO.
Fort Lauderdale-Pompano Beach-Deerfield Beach, FL ........................................................................................................
Broward County, FL.
Fort Smith, AR-OK .................................................................................................................................................................
Crawford County, AR.
Franklin County, AR.
Sebastian County, AR.
Le Flore County, OK.
Sequoyah County, OK.
Fort Walton Beach-Crestview-Destin, FL ..............................................................................................................................
Okaloosa County, FL.
Fort Wayne, IN ......................................................................................................................................................................
Allen County, IN.
Wells County, IN.
Whitley County, IN.
Fort Worth-Arlington, TX ........................................................................................................................................................
Johnson County, TX.
Parker County, TX.
Tarrant County, TX.
Wise County, TX.
Fresno, CA .............................................................................................................................................................................
Fresno County, CA.
Gadsden, AL ..........................................................................................................................................................................
Etowah County, AL.
Gainesville, FL .......................................................................................................................................................................
Alachua County, FL.
Gilchrist County, FL.
Gainesville, GA ......................................................................................................................................................................
Hall County, GA.
Gary, IN .................................................................................................................................................................................
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0.8946
0.8865
1.1601
1.0969
0.8388
0.7844
1.0064
0.9545
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
24020 .......
24140 .......
24220 .......
24300 .......
24340 .......
24500 .......
24540 .......
24580 .......
24660 .......
24780 .......
24860 .......
25020 .......
25060 .......
25180 .......
25260 .......
25420 .......
25500 .......
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25620 .......
25860 .......
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Urban area (constituent counties)
Jasper County, IN.
Lake County, IN.
Newton County, IN.
Porter County, IN.
Glens Falls, NY ......................................................................................................................................................................
Warren County, NY.
Washington County, NY.
Goldsboro, NC .......................................................................................................................................................................
Wayne County, NC.
Grand Forks, ND-MN .............................................................................................................................................................
Polk County, MN.
Grand Forks County, ND.
Grand Junction, CO ...............................................................................................................................................................
Mesa County, CO.
Grand Rapids-Wyoming, MI ..................................................................................................................................................
Barry County, MI.
Ionia County, MI.
Kent County, MI.
Newaygo County, MI.
Great Falls, MT ......................................................................................................................................................................
Cascade County, MT.
Greeley, CO ...........................................................................................................................................................................
Weld County, CO.
Green Bay, WI .......................................................................................................................................................................
Brown County, WI.
Kewaunee County, WI.
Oconto County, WI.
Greensboro-High Point, NC ...................................................................................................................................................
Guilford County, NC.
Randolph County, NC.
Rockingham County, NC.
Greenville, NC .......................................................................................................................................................................
Greene County, NC.
Pitt County, NC.
Greenville, SC ........................................................................................................................................................................
Greenville County, SC.
Laurens County, SC.
Pickens County, SC.
Guayama, PR ........................................................................................................................................................................
Arroyo Municipio, PR.
Guayama Municipio, PR.
Patillas Municipio, PR.
Gulfport-Biloxi, MS .................................................................................................................................................................
Hancock County, MS.
Harrison County, MS.
Stone County, MS.
Hagerstown-Martinsburg, MD-WV .........................................................................................................................................
Washington County, MD.
Berkeley County, WV.
Morgan County, WV.
Hanford-Corcoran, CA ...........................................................................................................................................................
Kings County, CA.
Harrisburg-Carlisle, PA ..........................................................................................................................................................
Cumberland County, PA.
Dauphin County, PA.
Perry County, PA.
Harrisonburg, VA ...................................................................................................................................................................
Rockingham County, VA.
Harrisonburg City, VA.
Hartford-West Hartford-East Hartford, CT .............................................................................................................................
Hartford County, CT.
Litchfield County, CT.
Middlesex County, CT.
Tolland County, CT.
Hattiesburg, MS .....................................................................................................................................................................
Forrest County, MS.
Lamar County, MS.
Perry County, MS.
Hickory-Lenoir-Morganton, NC ..............................................................................................................................................
Alexander County, NC.
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
259801 .....
26100 .......
26180 .......
26300 .......
26380 .......
26420 .......
26580 .......
26620 .......
26820 .......
26900 .......
26980 .......
27060 .......
27100 .......
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27180 .......
27260 .......
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Urban area (constituent counties)
Burke County, NC.
Caldwell County, NC.
Catawba County, NC.
Hinesville-Fort Stewart, GA ...................................................................................................................................................
Liberty County, GA.
Long County, GA.
Holland-Grand Haven, MI ......................................................................................................................................................
Ottawa County, MI.
Honolulu, HI ...........................................................................................................................................................................
Honolulu County, HI.
Hot Springs, AR .....................................................................................................................................................................
Garland County, AR.
Houma-Bayou Cane-Thibodaux, LA ......................................................................................................................................
Lafourche Parish, LA.
Terrebonne Parish, LA.
Houston-Baytown-Sugar Land, TX ........................................................................................................................................
Austin County, TX.
Brazoria County, TX.
Chambers County, TX.
Fort Bend County, TX.
Galveston County, TX.
Harris County, TX.
Liberty County, TX.
Montgomery County, TX.
San Jacinto County, TX.
Waller County, TX.
Huntington-Ashland, WV-KY-OH ...........................................................................................................................................
Boyd County, KY.
Greenup County, KY.
Lawrence County, OH.
Cabell County, WV.
Wayne County, WV.
Huntsville, AL .........................................................................................................................................................................
Limestone County, AL.
Madison County, AL.
Idaho Falls, ID .......................................................................................................................................................................
Bonneville County, ID.
Jefferson County, ID.
Indianapolis, IN ......................................................................................................................................................................
Boone County, IN.
Brown County, IN.
Hamilton County, IN.
Hancock County, IN.
Hendricks County, IN.
Johnson County, IN.
Marion County, IN.
Morgan County, IN.
Putnam County, IN.
Shelby County, IN.
Iowa City, IA ..........................................................................................................................................................................
Johnson County, IA.
Washington County, IA.
Ithaca, NY ..............................................................................................................................................................................
Tompkins County, NY.
Jackson, MI ............................................................................................................................................................................
Jackson County, MI.
Jackson, MS ..........................................................................................................................................................................
Copiah County, MS.
Hinds County, MS.
Madison County, MS.
Rankin County, MS.
Simpson County, MS.
Jackson, TN ...........................................................................................................................................................................
Chester County, TN.
Madison County, TN.
Jacksonville, FL .....................................................................................................................................................................
Baker County, FL.
Clay County, FL.
Duval County, FL.
Nassau County, FL.
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0.8998
0.9007
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Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
27340 .......
27500 .......
27620 .......
27740 .......
27780 .......
27860 .......
27900 .......
28020 .......
28100 .......
28140 .......
28420 .......
28660 .......
28700 .......
28740 .......
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29020 .......
29100 .......
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Urban area (constituent counties)
St. Johns County, FL.
Jacksonville, NC ....................................................................................................................................................................
Onslow County, NC.
Janesville, WI .........................................................................................................................................................................
Rock County, WI.
Jefferson City, MO .................................................................................................................................................................
Callaway County, MO.
Cole County, MO.
Moniteau County, MO.
Osage County, MO.
Johnson City, TN ...................................................................................................................................................................
Carter County, TN.
Unicoi County, TN.
Washington County, TN.
Johnstown, PA .......................................................................................................................................................................
Cambria County, PA.
Jonesboro, AR .......................................................................................................................................................................
Craighead County, AR.
Poinsett County, AR.
Joplin, MO ..............................................................................................................................................................................
Jasper County, MO.
Newton County, MO.
Kalamazoo-Portage, MI .........................................................................................................................................................
Kalamazoo County, MI.
Van Buren County, MI.
Kankakee-Bradley, IL ............................................................................................................................................................
Kankakee County, IL.
Kansas City, MO-KS ..............................................................................................................................................................
Franklin County, KS.
Johnson County, KS.
Leavenworth County, KS.
Linn County, KS.
Miami County, KS.
Wyandotte County, KS.
Bates County, MO.
Caldwell County, MO.
Cass County, MO.
Clay County, MO.
Clinton County, MO.
Jackson County, MO.
Lafayette County, MO.
Platte County, MO.
Ray County, MO.
Kennewick-Richland-Pasco, WA ...........................................................................................................................................
Benton County, WA.
Franklin County, WA.
Killeen-Temple-Fort Hood, TX ...............................................................................................................................................
Bell County, TX.
Coryell County, TX.
Lampasas County, TX.
Kingsport-Bristol-Bristol, TN-VA ............................................................................................................................................
Hawkins County, TN.
Sullivan County, TN.
Bristol City, VA.
Scott County, VA.
Washington County, VA.
Kingston, NY ..........................................................................................................................................................................
Ulster County, NY.
Knoxville, TN ..........................................................................................................................................................................
Anderson County, TN.
Blount County, TN.
Knox County, TN.
Loudon County, TN.
Union County, TN.
Kokomo, IN ............................................................................................................................................................................
Howard County, IN.
Tipton County, IN.
La Crosse, WI-MN .................................................................................................................................................................
Houston County, MN.
La Crosse County, WI.
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0.7662
0.8606
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1.0083
0.9495
1.0343
0.8902
0.7985
0.9367
0.8249
0.9669
0.9426
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Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
Urban area (constituent counties)
29140 .......
Lafayette, IN ..........................................................................................................................................................................
Benton County, IN.
Carroll County, IN.
Tippecanoe County, IN.
Lafayette, LA ..........................................................................................................................................................................
Lafayette Parish, LA.
St. Martin Parish, LA.
Lake Charles, LA ...................................................................................................................................................................
Calcasieu Parish, LA.
Cameron Parish, LA.
Lake County-Kenosha County, IL-WI ....................................................................................................................................
Lake County, IL.
Kenosha County, WI.
Lakeland, FL ..........................................................................................................................................................................
Polk County, FL.
Lancaster, PA ........................................................................................................................................................................
Lancaster County, PA.
Lansing-East Lansing, MI ......................................................................................................................................................
Clinton County, MI.
Eaton County, MI.
Ingham County, MI.
Laredo, TX .............................................................................................................................................................................
Webb County, TX.
Las Cruces, NM .....................................................................................................................................................................
Dona Ana County, NM.
Las Vegas-Paradise, NV .......................................................................................................................................................
Clark County, NV.
Lawrence, KS ........................................................................................................................................................................
Douglas County, KS.
Lawton, OK ............................................................................................................................................................................
Comanche County, OK.
Lebanon, PA ..........................................................................................................................................................................
Lebanon County, PA.
Lewiston, ID-WA ....................................................................................................................................................................
Nez Perce County, ID.
Asotin County, WA.
Lewiston-Auburn, ME ............................................................................................................................................................
Androscoggin County, ME.
Lexington-Fayette, KY ...........................................................................................................................................................
Bourbon County, KY.
Clark County, KY.
Fayette County, KY.
Jessamine County, KY.
Scott County, KY.
Woodford County, KY.
Lima, OH ................................................................................................................................................................................
Allen County, OH.
Lincoln, NE ............................................................................................................................................................................
Lancaster County, NE.
Seward County, NE.
Little Rock-North Little Rock, AR ...........................................................................................................................................
Faulkner County, AR.
Grant County, AR.
Lonoke County, AR.
Perry County, AR.
Pulaski County, AR.
Saline County, AR.
Logan, UT-ID .........................................................................................................................................................................
Franklin County, ID.
Cache County, UT.
Longview, TX .........................................................................................................................................................................
Gregg County, TX.
Rusk County, TX.
Upshur County, TX.
Longview, WA ........................................................................................................................................................................
Cowlitz County, WA.
Los Angeles-Long Beach-Glendale, CA ................................................................................................................................
Los Angeles County, CA.
Louisville, KY-IN ....................................................................................................................................................................
Clark County, IN.
29180 .......
29340 .......
29404 .......
29460 .......
29540 .......
29620 .......
29700 .......
29740 .......
29820 .......
29940 .......
30020 .......
30140 .......
30300 .......
30340 .......
30460 .......
30620 .......
30700 .......
30780 .......
30860 .......
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31084 .......
31140 .......
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0.8932
0.8289
0.7914
1.0571
0.8879
0.9589
1.0088
0.7812
0.9273
1.1430
0.8366
0.8066
0.8680
0.9854
0.9126
0.9181
0.9042
1.0092
0.8890
0.9022
0.8788
1.0011
1.1760
0.9119
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
31180 .......
31340 .......
31420 .......
31460 .......
31540 .......
31700 .......
31900 .......
32420 .......
32580 .......
32780 .......
32820 .......
32900 .......
33124 .......
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33260 .......
33340 .......
33460 .......
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Urban area (constituent counties)
Floyd County, IN.
Harrison County, IN.
Washington County, IN.
Bullitt County, KY.
Henry County, KY.
Jefferson County, KY.
Meade County, KY.
Nelson County, KY.
Oldham County, KY.
Shelby County, KY.
Spencer County, KY.
Trimble County, KY.
Lubbock, TX ...........................................................................................................................................................................
Crosby County, TX.
Lubbock County, TX.
Lynchburg, VA .......................................................................................................................................................................
Amherst County, VA.
Appomattox County, VA.
Bedford County, VA.
Campbell County, VA.
Bedford City, VA.
Lynchburg City, VA.
Macon, GA .............................................................................................................................................................................
Bibb County, GA.
Crawford County, GA.
Jones County, GA.
Monroe County, GA.
Twiggs County, GA.
Madera, CA ............................................................................................................................................................................
Madera County, CA.
Madison, WI ...........................................................................................................................................................................
Columbia County, WI.
Dane County, WI.
Iowa County, WI.
Manchester-Nashua, NH .......................................................................................................................................................
Hillsborough County, NH.
Merrimack County, NH.
Mansfield, OH ........................................................................................................................................................................
Richland County, OH.
¨
Mayaguez, PR .......................................................................................................................................................................
Hormigueros Municipio, PR.
¨
Mayaguez Municipio, PR.
McAllen-Edinburg-Pharr, TX ..................................................................................................................................................
Hidalgo County, TX.
Medford, OR ..........................................................................................................................................................................
Jackson County, OR.
Memphis, TN-MS-AR .............................................................................................................................................................
Crittenden County, AR.
DeSoto County, MS.
Marshall County, MS.
Tate County, MS.
Tunica County, MS.
Fayette County, TN.
Shelby County, TN.
Tipton County, TN.
Merced, CA ............................................................................................................................................................................
Merced County, CA.
Miami-Miami Beach-Kendall, FL ............................................................................................................................................
Miami-Dade County, FL.
Michigan City-La Porte, IN ....................................................................................................................................................
LaPorte County, IN.
Midland, TX ............................................................................................................................................................................
Midland County, TX.
Milwaukee-Waukesha-West Allis, WI ....................................................................................................................................
Milwaukee County, WI.
Ozaukee County, WI.
Washington County, WI.
Waukesha County, WI.
Minneapolis-St. Paul-Bloomington, MN-WI ...........................................................................................................................
Anoka County, MN.
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0.9813
0.9118
0.9786
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Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
33540 .......
33660 .......
33700 .......
33740 .......
33780 .......
33860 .......
34060 .......
34100 .......
34580 .......
34620 .......
34740 .......
34820 .......
34900 .......
34940 .......
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35004 .......
35084 .......
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Urban area (constituent counties)
Carver County, MN.
Chisago County, MN.
Dakota County, MN.
Hennepin County, MN.
Isanti County, MN.
Ramsey County, MN.
Scott County, MN.
Sherburne County, MN.
Washington County, MN.
Wright County, MN.
Pierce County, WI.
St. Croix County, WI.
Missoula, MT .........................................................................................................................................................................
Missoula County, MT.
Mobile, AL ..............................................................................................................................................................................
Mobile County, AL.
Modesto, CA ..........................................................................................................................................................................
Stanislaus County, CA.
Monroe, LA ............................................................................................................................................................................
Ouachita Parish, LA.
Union Parish, LA.
Monroe, MI .............................................................................................................................................................................
Monroe County, MI.
Montgomery, AL ....................................................................................................................................................................
Autauga County, AL.
Elmore County, AL.
Lowndes County, AL.
Montgomery County, AL.
Morgantown, WV ...................................................................................................................................................................
Monongalia County, WV.
Preston County, WV.
Morristown, TN ......................................................................................................................................................................
Grainger County, TN.
Hamblen County, TN.
Jefferson County, TN.
Mount Vernon-Anacortes, WA ...............................................................................................................................................
Skagit County, WA.
Muncie, IN ..............................................................................................................................................................................
Delaware County, IN.
Muskegon-Norton Shores, MI ................................................................................................................................................
Muskegon County, MI.
Myrtle Beach-Conway-North Myrtle Beach, SC ....................................................................................................................
Horry County, SC.
Napa, CA ...............................................................................................................................................................................
Napa County, CA.
Naples-Marco Island, FL .......................................................................................................................................................
Collier County, FL.
Nashville-Davidson-Murfreesboro, TN ...................................................................................................................................
Cannon County, TN.
Cheatham County, TN.
Davidson County, TN.
Dickson County, TN.
Hickman County, TN.
Macon County, TN.
Robertson County, TN.
Rutherford County, TN.
Smith County, TN.
Sumner County, TN.
Trousdale County, TN.
Williamson County, TN.
Wilson County, TN.
Nassau-Suffolk, NY ...............................................................................................................................................................
Nassau County, NY.
Suffolk County, NY.
Newark-Union, NJ-PA ............................................................................................................................................................
Essex County, NJ.
Hunterdon County, NJ.
Morris County, NJ.
Sussex County, NJ.
Union County, NJ.
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
35300 .......
35380 .......
35644 .......
35660 .......
35980 .......
36084 .......
36100 .......
36140 .......
36220 .......
36260 .......
36420 .......
36500 .......
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36980 .......
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Urban area (constituent counties)
Pike County, PA.
New Haven-Milford, CT .........................................................................................................................................................
New Haven County, CT.
New Orleans-Metairie-Kenner, LA .........................................................................................................................................
Jefferson Parish, LA.
Orleans Parish, LA.
Plaquemines Parish, LA.
St. Bernard Parish, LA.
St. Charles Parish, LA.
St. John the Baptist Parish, LA.
St. Tammany Parish, LA.
New York-Wayne-White Plains, NY-NJ .................................................................................................................................
Bergen County, NJ.
Hudson County, NJ.
Passaic County, NJ.
Bronx County, NY.
Kings County, NY.
New York County, NY.
Putnam County, NY.
Queens County, NY.
Richmond County, NY.
Rockland County, NY.
Westchester County, NY.
Niles-Benton Harbor, MI ........................................................................................................................................................
Berrien County, MI.
Norwich-New London, CT .....................................................................................................................................................
New London County, CT.
Oakland-Fremont-Hayward, CA ............................................................................................................................................
Alameda County, CA.
Contra Costa County, CA.
Ocala, FL ...............................................................................................................................................................................
Marion County, FL.
Ocean City, NJ ......................................................................................................................................................................
Cape May County, NJ.
Odessa, TX ............................................................................................................................................................................
Ector County, TX.
Ogden-Clearfield, UT .............................................................................................................................................................
Davis County, UT.
Morgan County, UT.
Weber County, UT.
Oklahoma City, OK ................................................................................................................................................................
Canadian County, OK.
Cleveland County, OK.
Grady County, OK.
Lincoln County, OK.
Logan County, OK.
McClain County, OK.
Oklahoma County, OK.
Olympia, WA ..........................................................................................................................................................................
Thurston County, WA.
Omaha-Council Bluffs, NE-IA ................................................................................................................................................
Harrison County, IA.
Mills County, IA.
Pottawattamie County, IA.
Cass County, NE.
Douglas County, NE.
Sarpy County, NE.
Saunders County, NE.
Washington County, NE.
Orlando, FL ............................................................................................................................................................................
Lake County, FL.
Orange County, FL.
Osceola County, FL.
Seminole County, FL.
Oshkosh-Neenah, WI ............................................................................................................................................................
Winnebago County, WI.
Owensboro, KY ......................................................................................................................................................................
Daviess County, KY.
Hancock County, KY.
McLean County, KY.
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1.5819
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1.1081
0.9450
0.9452
0.9315
0.8748
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Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
Urban area (constituent counties)
37100 .......
Oxnard-Thousand Oaks-Ventura, CA ...................................................................................................................................
Ventura County, CA.
Palm Bay-Melbourne-Titusville, FL ........................................................................................................................................
Brevard County, FL.
Panama City-Lynn Haven, FL ...............................................................................................................................................
Bay County, FL.
Parkersburg-Marietta, WV-OH ...............................................................................................................................................
Washington County, OH.
Pleasants County, WV.
Wirt County, WV.
Wood County, WV.
Pascagoula, MS .....................................................................................................................................................................
George County, MS.
Jackson County, MS.
Pensacola-Ferry Pass-Brent, FL ...........................................................................................................................................
Escambia County, FL.
Santa Rosa County, FL.
Peoria, IL ...............................................................................................................................................................................
Marshall County, IL.
Peoria County, IL.
Stark County, IL.
Tazewell County, IL.
Woodford County, IL.
Philadelphia, PA ....................................................................................................................................................................
Bucks County, PA.
Chester County, PA.
Delaware County, PA.
Montgomery County, PA.
Philadelphia County, PA.
Phoenix-Mesa-Scottsdale, AZ ...............................................................................................................................................
Maricopa County, AZ.
Pinal County, AZ.
Pine Bluff, AR ........................................................................................................................................................................
Cleveland County, AR.
Jefferson County, AR.
Lincoln County, AR.
Pittsburgh, PA ........................................................................................................................................................................
Allegheny County, PA.
Armstrong County, PA.
Beaver County, PA.
Butler County, PA.
Fayette County, PA.
Washington County, PA.
Westmoreland County, PA.
Pittsfield, MA ..........................................................................................................................................................................
Berkshire County, MA.
Pocatello, ID ..........................................................................................................................................................................
Bannock County, ID.
Power County, ID.
Ponce, PR ..............................................................................................................................................................................
´
Juana Dıaz Municipio, PR.
Ponce Municipio, PR.
Villalba Municipio, PR.
Portland-South Portland-Biddeford, ME ................................................................................................................................
Cumberland County, ME.
Sagadahoc County, ME.
York County, ME.
Portland-Vancouver-Beaverton, OR-WA ...............................................................................................................................
Clackamas County, OR.
Columbia County, OR.
Multnomah County, OR.
Washington County, OR.
Yamhill County, OR.
Clark County, WA.
Skamania County, WA.
Port St. Lucie-Fort Pierce, FL ................................................................................................................................................
Martin County, FL.
St. Lucie County, FL.
Poughkeepsie-Newburgh-Middletown, NY ............................................................................................................................
Dutchess County, NY.
37340 .......
37460 .......
37620 .......
37700 .......
37860 .......
37900 .......
37964 .......
38060 .......
38220 .......
38300 .......
38340 .......
38540 .......
38660 .......
38860 .......
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38940 .......
39100 .......
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index
04MYP2
1.1546
0.9443
0.8027
0.7978
0.8215
0.8000
0.8982
1.0997
1.0288
0.8383
0.8674
1.0266
0.9401
0.4843
0.9909
1.1416
0.9834
1.0911
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
25475
ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
39140 .......
39300 .......
39340 .......
39380 .......
39460 .......
39540 .......
39580 .......
39660 .......
39740 .......
39820 .......
39900 .......
40060 .......
40140 .......
cprice-sewell on DSK89S0YB1PROD with RULES
40220 .......
40340 .......
40380 .......
VerDate Nov<24>2008
Wage
index
Urban area (constituent counties)
Orange County, NY.
Prescott, AZ ...........................................................................................................................................................................
Yavapai County, AZ.
Providence-New Bedford-Fall River, RI-MA ..........................................................................................................................
Bristol County, MA.
Bristol County, RI.
Kent County, RI.
Newport County, RI.
Providence County, RI.
Washington County, RI.
Provo-Orem, UT ....................................................................................................................................................................
Juab County, UT.
Utah County, UT.
Pueblo, CO ............................................................................................................................................................................
Pueblo County, CO.
Punta Gorda, FL ....................................................................................................................................................................
Charlotte County, FL.
Racine, WI .............................................................................................................................................................................
Racine County, WI.
Raleigh-Cary, NC ...................................................................................................................................................................
Franklin County, NC.
Johnston County, NC.
Wake County, NC.
Rapid City, SD .......................................................................................................................................................................
Meade County, SD.
Pennington County, SD.
Reading, PA ...........................................................................................................................................................................
Berks County, PA.
Redding, CA ..........................................................................................................................................................................
Shasta County, CA.
Reno-Sparks, NV ...................................................................................................................................................................
Storey County, NV.
Washoe County, NV.
Richmond, VA ........................................................................................................................................................................
Amelia County, VA.
Caroline County, VA.
Charles City County, VA.
Chesterfield County, VA.
Cumberland County, VA.
Dinwiddie County, VA.
Goochland County, VA.
Hanover County, VA.
Henrico County, VA.
King and Queen County, VA.
King William County, VA.
Louisa County, VA.
New Kent County, VA.
Powhatan County, VA.
Prince George County, VA.
Sussex County, VA.
Colonial Heights City, VA.
Hopewell City, VA.
Petersburg City, VA.
Richmond City, VA.
Riverside-San Bernardino-Ontario, CA .................................................................................................................................
Riverside County, CA.
San Bernardino County, CA.
Roanoke, VA ..........................................................................................................................................................................
Botetourt County, VA.
Craig County, VA.
Franklin County, VA.
Roanoke County, VA.
Roanoke City, VA.
Salem City, VA.
Rochester, MN .......................................................................................................................................................................
Dodge County, MN.
Olmsted County, MN.
Wabasha County, MN.
Rochester, NY .......................................................................................................................................................................
Livingston County, NY.
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0.9538
0.8754
0.9405
0.9356
0.9864
0.8833
0.9623
1.3198
1.1964
0.9177
1.0904
0.8647
1.1408
0.8994
25476
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
40420 .......
40484 .......
40580 .......
40660 .......
40900 .......
40980 .......
41060 .......
41100 .......
41140 .......
41180 .......
41420 .......
41500 .......
41540 .......
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41620 .......
41660 .......
41700 .......
VerDate Nov<24>2008
Wage
index
Urban area (constituent counties)
Monroe County, NY.
Ontario County, NY.
Orleans County, NY.
Wayne County, NY.
Rockford, IL ...........................................................................................................................................................................
Boone County, IL.
Winnebago County, IL.
Rockingham County-Strafford County, NH ...........................................................................................................................
Rockingham County, NH.
Strafford County, NH.
Rocky Mount, NC ..................................................................................................................................................................
Edgecombe County, NC.
Nash County, NC.
Rome, GA ..............................................................................................................................................................................
Floyd County, GA.
SacramentoArden-ArcadeRoseville, CA ................................................................................................................................
El Dorado County, CA.
Placer County, CA.
Sacramento County, CA.
Yolo County, CA.
Saginaw-Saginaw Township North, MI .................................................................................................................................
Saginaw County, MI.
St. Cloud, MN ........................................................................................................................................................................
Benton County, MN.
Stearns County, MN.
St. George, UT ......................................................................................................................................................................
Washington County, UT.
St. Joseph, MO-KS ................................................................................................................................................................
Doniphan County, KS.
Andrew County, MO.
Buchanan County, MO.
DeKalb County, MO.
St. Louis, MO-IL .....................................................................................................................................................................
Bond County, IL.
Calhoun County, IL.
Clinton County, IL.
Jersey County, IL.
Macoupin County, IL.
Madison County, IL.
Monroe County, IL.
St. Clair County, IL.
Crawford County, MO.
Franklin County, MO.
Jefferson County, MO.
Lincoln County, MO.
St. Charles County, MO.
St. Louis County, MO.
Warren County, MO.
Washington County, MO.
St. Louis City, MO.
Salem, OR .............................................................................................................................................................................
Marion County, OR.
Polk County, OR.
Salinas, CA ............................................................................................................................................................................
Monterey County, CA.
Salisbury, MD ........................................................................................................................................................................
Somerset County, MD.
Wicomico County, MD.
Salt Lake City, UT .................................................................................................................................................................
Salt Lake County, UT.
Summit County, UT.
Tooele County, UT.
San Angelo, TX .....................................................................................................................................................................
Irion County, TX.
Tom Green County, TX.
San Antonio, TX ....................................................................................................................................................................
Atascosa County, TX.
Bandera County, TX.
Bexar County, TX.
Comal County, TX.
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0.8854
0.9194
1.3373
0.8874
1.0362
0.9265
1.0118
0.9006
1.0439
1.4338
0.8953
0.9402
0.8362
0.8845
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
41740 .......
41780 .......
41884 .......
41900 .......
41940 .......
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41980 .......
42020 .......
42044 .......
42060 .......
42100 .......
VerDate Nov<24>2008
Wage
index
Urban area (constituent counties)
Guadalupe County, TX.
Kendall County, TX.
Medina County, TX.
Wilson County, TX.
San Diego-Carlsbad-San Marcos, CA ...................................................................................................................................
San Diego County, CA.
Sandusky, OH ........................................................................................................................................................................
Erie County, OH.
San Francisco-San Mateo-Redwood City, CA ......................................................................................................................
Marin County, CA.
San Francisco County, CA.
San Mateo County, CA.
´
San German-Cabo Rojo, PR .................................................................................................................................................
Cabo Rojo Municipio, PR.
Lajas Municipio, PR.
Sabana Grande Municipio, PR.
´
San German Municipio, PR.
San Jose-Sunnyvale-Santa Clara, CA ..................................................................................................................................
San Benito County, CA.
Santa Clara County, CA.
San Juan-Caguas-Guaynabo, PR .........................................................................................................................................
Aguas Buenas Municipio, PR.
Aibonito Municipio, PR.
Arecibo Municipio, PR.
Barceloneta Municipio, PR.
Barranquitas Municipio, PR.
´
Bayamon Municipio, PR.
Caguas Municipio, PR.
Camuy Municipio, PR.
´
Canovanas Municipio, PR.
Carolina Municipio, PR.
˜
Catano Municipio, PR.
Cayey Municipio, PR.
Ciales Municipio, PR.
Cidra Municipio, PR.
´
Comerıo Municipio, PR.
Corozal Municipio, PR.
Dorado Municipio, PR.
Florida Municipio, PR.
Guaynabo Municipio, PR.
Gurabo Municipio, PR.
Hatillo Municipio, PR.
Humacao Municipio, PR.
Juncos Municipio, PR.
Las Piedras Municipio, PR.
´
Loıza Municipio, PR.
´
Manatı Municipio, PR.
Maunabo Municipio, PR.
Morovis Municipio, PR.
Naguabo Municipio, PR.
Naranjito Municipio, PR.
Orocovis Municipio, PR.
Quebradillas Municipio, PR.
´
Rıo Grande Municipio, PR.
San Juan Municipio, PR.
San Lorenzo Municipio, PR.
Toa Alta Municipio, PR.
Toa Baja Municipio, PR.
Trujillo Alto Municipio, PR.
Vega Alta Municipio, PR.
Vega Baja Municipio, PR.
Yabucoa Municipio, PR.
.
San Luis Obispo-Paso Robles, CA .......................................................................................................................................
San Luis Obispo County, CA.
Santa Ana-Anaheim-Irvine, CA .............................................................................................................................................
Orange County, CA.
Santa Barbara-Santa Maria-Goleta, CA ................................................................................................................................
Santa Barbara County, CA.
Santa Cruz-Watsonville, CA ..................................................................................................................................................
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1.5166
0.4885
1.5543
0.4452
1.1599
1.1473
1.1092
1.5458
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
42140 .......
42220 .......
42260 .......
42340 .......
42540 .......
42644 .......
42680 .......
43100 .......
43300 .......
43340 .......
43580 .......
43620 .......
43780 .......
43900 .......
44060 .......
44100 .......
44140 .......
44180 .......
44220 .......
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44300 .......
44700 .......
44940 .......
45060 .......
VerDate Nov<24>2008
Wage
index
Urban area (constituent counties)
Santa Cruz County, CA.
Santa Fe, NM ........................................................................................................................................................................
Santa Fe County, NM.
Santa Rosa-Petaluma, CA ....................................................................................................................................................
Sonoma County, CA.
Sarasota-Bradenton-Venice, FL ............................................................................................................................................
Manatee County, FL.
Sarasota County, FL.
Savannah, GA .......................................................................................................................................................................
Bryan County, GA.
Chatham County, GA.
Effingham County, GA.
ScrantonWilkes-Barre, PA .....................................................................................................................................................
Lackawanna County, PA.
Luzerne County, PA.
Wyoming County, PA.
Seattle-Bellevue-Everett, WA ................................................................................................................................................
King County, WA.
Snohomish County, WA.
Sebastian-Vero Beach, FL ....................................................................................................................................................
Sheboygan, WI ......................................................................................................................................................................
Sheboygan County, WI.
Sherman-Denison, TX ...........................................................................................................................................................
Grayson County, TX.
Shreveport-Bossier City, LA ..................................................................................................................................................
Bossier Parish, LA.
Caddo Parish, LA.
De Soto Parish, LA.
Sioux City, IA-NE-SD .............................................................................................................................................................
Woodbury County, IA.
Dakota County, NE.
Dixon County, NE.
Union County, SD.
Sioux Falls, SD ......................................................................................................................................................................
Lincoln County, SD.
McCook County, SD.
Minnehaha County, SD.
Turner County, SD.
South Bend-Mishawaka, IN-MI ..............................................................................................................................................
St. Joseph County, IN.
Cass County, MI.
Spartanburg, SC ....................................................................................................................................................................
Spartanburg County, SC.
Spokane, WA .........................................................................................................................................................................
Spokane County, WA.
Springfield, IL .........................................................................................................................................................................
Menard County, IL.
Sangamon County, IL.
Springfield, MA ......................................................................................................................................................................
Franklin County, MA.
Hampden County, MA.
Hampshire County, MA.
Springfield, MO ......................................................................................................................................................................
Christian County, MO.
Dallas County, MO.
Greene County, MO.
Polk County, MO.
Webster County, MO.
Springfield, OH ......................................................................................................................................................................
Clark County, OH.
State College, PA ..................................................................................................................................................................
Centre County, PA.
Stockton, CA ..........................................................................................................................................................................
San Joaquin County, CA.
Sumter, SC ............................................................................................................................................................................
Sumter County, SC.
Syracuse, NY .........................................................................................................................................................................
Madison County, NY.
Onondaga County, NY.
Oswego County, NY.
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1.4464
0.9868
0.9351
0.8348
1.1434
0.9573
0.9027
0.8503
0.8865
0.9201
0.9559
0.9842
0.9174
1.0447
0.8890
1.0079
0.8469
0.8593
0.8784
1.1443
0.8084
0.9692
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
Urban area (constituent counties)
45104 .......
Tacoma, WA ..........................................................................................................................................................................
Pierce County, WA.
Tallahassee, FL .....................................................................................................................................................................
Gadsden County, FL.
Jefferson County, FL.
Leon County, FL.
Wakulla County, FL.
Tampa-St. Petersburg-Clearwater, FL ..................................................................................................................................
Hernando County, FL.
Hillsborough County, FL.
Pasco County, FL.
Pinellas County, FL.
Terre Haute, IN ......................................................................................................................................................................
Clay County, IN.
Sullivan County, IN.
Vermillion County, IN.
Vigo County, IN.
Texarkana, TX-Texarkana, AR ..............................................................................................................................................
Miller County, AR.
Bowie County, TX.
Toledo, OH ............................................................................................................................................................................
Fulton County, OH.
Lucas County, OH.
Ottawa County, OH.
Wood County, OH.
Topeka, KS ............................................................................................................................................................................
Jackson County, KS.
Jefferson County, KS.
Osage County, KS.
Shawnee County, KS.
Wabaunsee County, KS.
Trenton-Ewing, NJ .................................................................................................................................................................
Mercer County, NJ.
Tucson, AZ ............................................................................................................................................................................
Pima County, AZ.
Tulsa, OK ...............................................................................................................................................................................
Creek County, OK.
Okmulgee County, OK.
Osage County, OK.
Pawnee County, OK.
Rogers County, OK.
Tulsa County, OK.
Wagoner County, OK.
Tuscaloosa, AL ......................................................................................................................................................................
Greene County, AL.
Hale County, AL.
Tuscaloosa County, AL.
Tyler, TX ................................................................................................................................................................................
Smith County, TX.
Utica-Rome, NY .....................................................................................................................................................................
Herkimer County, NY.
Oneida County, NY.
Valdosta, GA ..........................................................................................................................................................................
Brooks County, GA.
Echols County, GA.
Lanier County, GA.
Lowndes County, GA.
Vallejo-Fairfield, CA ...............................................................................................................................................................
Solano County, CA.
Victoria, TX ............................................................................................................................................................................
Calhoun County, TX.
Goliad County, TX.
Victoria County, TX.
Vineland-Millville-Bridgeton, NJ .............................................................................................................................................
Cumberland County, NJ.
Virginia Beach-Norfolk-Newport News, VA-NC .....................................................................................................................
Currituck County, NC.
Gloucester County, VA.
Isle of Wight County, VA.
James City County, VA.
45220 .......
45300 .......
45460 .......
45500 .......
45780 .......
45820 .......
45940 .......
46060 .......
46140 .......
46220 .......
46340 .......
46540 .......
46660 .......
46700 .......
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47020 .......
47220 .......
47260 .......
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04MYP2
1.0789
0.8942
0.9144
0.8765
0.8104
0.9586
0.8730
1.0836
0.9203
0.8103
0.8542
0.8812
0.8397
0.8369
1.5138
0.8560
0.9832
0.8790
25480
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules
ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED
HOSPITAL WAGE INDEX—Continued
CBSA
code
47300 .......
47380 .......
47580 .......
47644 .......
47894 .......
47940 .......
48140 .......
48260 .......
48300 .......
cprice-sewell on DSK89S0YB1PROD with RULES
48424 .......
48540 .......
48620 .......
VerDate Nov<24>2008
Wage
index
Urban area (constituent counties)
Mathews County, VA.
Surry County, VA.
York County, VA.
Chesapeake City, VA.
Hampton City, VA.
Newport News City, VA.
Norfolk City, VA.
Poquoson City, VA.
Portsmouth City, VA.
Suffolk City, VA.
Virginia Beach City, VA.
Williamsburg City, VA.
Visalia-Porterville, CA ............................................................................................................................................................
Tulare County, CA.
Waco, TX ...............................................................................................................................................................................
McLennan County, TX.
Warner Robins, GA ...............................................................................................................................................................
Houston County, GA.
Warren-Farmington Hills-Troy, MI .........................................................................................................................................
Lapeer County, MI.
Livingston County, MI.
Macomb County, MI.
Oakland County, MI.
St. Clair County, MI.
Washington-Arlington-Alexandria, DC-VA-MD-WV ...............................................................................................................
District of Columbia, DC.
Calvert County, MD.
Charles County, MD.
Prince George’s County, MD.
Arlington County, VA.
Clarke County, VA.
Fairfax County, VA.
Fauquier County, VA.
Loudoun County, VA.
Prince William County, VA.
Spotsylvania County, VA.
Stafford County, VA.
Warren County, VA.
Alexandria City, VA.
Fairfax City, VA.
Falls Church City, VA.
Fredericksburg City, VA.
Manassas City, VA.
Manassas Park City, VA.
Jefferson County, WV.
.
Waterloo-Cedar Falls, IA .......................................................................................................................................................
Black Hawk County, IA.
Bremer County, IA.
Grundy County, IA.
Wausau, WI ...........................................................................................................................................................................
Marathon County, WI.
Weirton-Steubenville, WV-OH ...............................................................................................................................................
Jefferson County, OH.
Brooke County, WV.
Hancock County, WV.
Wenatchee, WA .....................................................................................................................................................................
Chelan County, WA.
Douglas County, WA.
West Palm Beach-Boca Raton-Boynton Beach, FL ..............................................................................................................
Palm Beach County, FL.
Wheeling, WV-OH .................................................................................................................................................................
Belmont County, OH.
Marshall County, WV.
Ohio County, WV.
Wichita, KS ............................................................................................................................................................................
Butler County, KS.
Harvey County, KS.
Sedgwick County, KS.
Sumner County, KS.
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0.9063
Agencies
[Federal Register Volume 72, Number 86 (Friday, May 4, 2007)]
[Proposed Rules]
[Pages 25356-25481]
From the Federal Register Online via the Government Printing Office [www.gpo.gov]
[FR Doc No: 07-2167]
[[Page 25355]]
-----------------------------------------------------------------------
Part II
Department of Health and Human Services
-----------------------------------------------------------------------
Centers for Medicare and Medicaid Services
-----------------------------------------------------------------------
42 CFR Part 484
Medicare Program; Home Health Prospective Payment System Refinement and
Rate Update for Calendar Year 2008; Proposed Rule
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed
Rules
[[Page 25356]]
-----------------------------------------------------------------------
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Medicare & Medicaid Services
42 CFR Part 484
[CMS-1541-P]
RIN 0938-AO32
Medicare Program; Home Health Prospective Payment System
Refinement and Rate Update for Calendar Year 2008
AGENCY: Centers for Medicare & Medicaid Services (CMS), HHS.
ACTION: Proposed rule.
-----------------------------------------------------------------------
SUMMARY: This proposed rule would set forth an update to the 60-day
national episode rates and the national per-visit amounts under the
Medicare prospective payment system for home health services, effective
on January 1, 2008. As part of this proposed rule, we are also
proposing to rebase and revise the home health market basket to ensure
it continues to adequately reflect the price changes of efficiently
providing home health services. This proposed rule also would set forth
the refinements to the payment system. In addition, this proposed rule
would establish new quality of care data collection requirements.
DATES: To be assured consideration, comments must be received at one of
the addresses provided below, no later than 5 p.m. on July 3, 2007.
ADDRESSES: In commenting, please refer to file code CMS-1541-P. Because
of staff and resource limitations, we cannot accept comments by
facsimile (FAX) transmission.
You may submit comments in one of four ways (no duplicates,
please):
1. Electronically. You may submit electronic comments on specific
issues in this regulation to https://www.cms.hhs.gov/eRulemaking. Click
on the link ``Submit electronic comments on CMS regulations with an
open comment period.'' (Attachments should be in Microsoft Word,
WordPerfect, or Excel; however, we prefer Microsoft Word.)
2. By regular mail. You may mail written comments (one original and
two copies) to the following address ONLY: Centers for Medicare &
Medicaid Services, Department of Health and Human Services, Attention:
CMS-1541-P, P.O. Box 8012, Baltimore, MD 21244-8012.
Please allow sufficient time for mailed comments to be received
before the close of the comment period.
3. By express or overnight mail. You may send written comments (one
original and two copies) to the following address ONLY: Centers for
Medicare & Medicaid Services, Department of Health and Human Services,
Attention: CMS-1541-P, Mail Stop C4-26-05, 7500 Security Boulevard,
Baltimore, MD 21244-1850.
4. By hand or courier. If you prefer, you may deliver (by hand or
courier) your written comments (one original and two copies) before the
close of the comment period to one of the following addresses. If you
intend to deliver your comments to the Baltimore address, please call
telephone number (410) 786-7195 in advance to schedule your arrival
with one of our staff members. Room 445-G, Hubert H. Humphrey Building,
200 Independence Avenue, SW., Washington, DC 20201; or 7500 Security
Boulevard, Baltimore, MD 21244-1850.
(Because access to the interior of the HHH Building is not readily
available to persons without Federal Government identification,
commenters are encouraged to leave their comments in the CMS drop slots
located in the main lobby of the building. A stamp-in clock is
available for persons wishing to retain a proof of filing by stamping
in and retaining an extra copy of the comments being filed.)
Comments mailed to the addresses indicated as appropriate for hand
or courier delivery may be delayed and received after the comment
period.
Submission of comments on paperwork requirements. You may submit
comments on this document's paperwork requirements by mailing your
comments to the addresses provided at the end of the ``Collection of
Information Requirements'' section in this document.
For information on viewing public comments, see the beginning of
the SUPPLEMENTARY INFORMATION section.
FOR FURTHER INFORMATION CONTACT: Randy Throndset, (410) 786-0131.
General Issues: Sharon Ventura, (410) 786-1985.
Clinical (OASIS) Issues: Kathy Walch, (410) 786-7970.
Quality Issues: Doug Brown, (410) 786-0028.
Market Basket Update Issues: Mollie Knight, (410) 786-7948; and
Heidi Oumarou, (410) 786-7942.
SUPPLEMENTARY INFORMATION:
Submitting Comments: We welcome comments from the public on all
issues set forth in this rule to assist us in fully considering issues
and developing policies. You can assist us by referencing the file code
CMS-1541-P and the specific ``issue identifier'' that precedes the
section on which you choose to comment.
Inspection of Public Comments: All comments received before the
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Table of Contents
I. Background
A. Requirements of the Balanced Budget Act of 1997 for Updating
the Prospective Payment System for Home Health Services
B. Deficit Reduction Act of 2005
C. Updates to the HH PPS
D. System for Payment of Home Health Services
E. Summary of Home Health Payment Research
II. Provisions of the Proposed Regulation
A. Refinements to the Home Health Prospective Payment System
1. Current Payment Model
2. Refinements to the Case-Mix Model
a. Analysis of Later Episodes
b. Addition of Variables
c. Addition of Therapy Thresholds
d. Determining the Case-Mix Weights
3. Description & Analysis of Case-Mix Coding Change Under the HH
PPS
a. Change in Case-Mix Group Frequencies
b. Health Characteristics Reported on the OASIS
c. Impact of the Context of OASIS Reporting
4. Partial Episode Payment Adjustment (PEP Adjustment) Review
5. Low-Utilization Payment Adjustment (LUPA) Review
6. Significant Change in Condition (SCIC) Adjustment Review
7. Non-Routine Medical Supply (NRS) Amounts Review
8. Outlier Payment Review
B. Rebasing and Revising the Home Health Market Basket
1. Background
2. Rebasing and Revising the Home Health Market Basket
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3. Price Proxies Used To Measure Cost Category Growth
4. Rebasing Results
5. Labor-Related Share
C. National Standardized 60-Day Episode Payment Rate
D. Proposed CY 2008 Rate Update by the Home Health Market Basket
Index (With Examples of Standard 60-Day and LUPA Episode Payment
Calculations)
E. Hospital Wage Index
1. Background
2. Update
F. Home Health Care Quality Improvement
III. Collection of Information Requirements
IV. Response to Comments
V. Regulatory Impact Analysis
A. Overall Impact
B. Anticipated Effects
C. Accounting Statement
I. Background
[If you choose to comment on issues in this section, please include
the caption ``BACKGROUND'' at the beginning of your comments.]
A. Requirements of the Balanced Budget Act of 1997 for Updating the
Prospective Payment System for Home Health Services
The Balanced Budget Act of 1997 (BBA) (Pub. L. 105-33) enacted on
August 5, 1997, significantly changed the way Medicare pays for
Medicare home health services. Until the implementation of a home
health prospective payment system (HH PPS) on October 1, 2000, home
health agencies (HHAs) received payment under a cost-based
reimbursement system. Section 4603 of the BBA governed the development
of the HH PPS.
Section 4603(a) of the BBA provides the authority for the
development of a PPS for all Medicare-covered home health services
provided under a plan of care that were paid on a reasonable cost basis
by adding section 1895, entitled ``Prospective Payment For Home Health
Services,'' to the Social Security Act (the Act).
Section 1895(b)(1) of the Act requires the Secretary to establish a
PPS for all costs of home health services paid under Medicare.
Section 1895(b)(3)(A) of the Act requires that (1) The computation
of a standard prospective payment amount include all costs for home
health services covered and paid for on a reasonable cost basis and be
initially based on the most recent audited cost report data available
to the Secretary, and (2) the prospective payment amounts be
standardized to eliminate the effects of case-mix and wage levels among
HHAs.
Section 1895(b)(3)(B) of the Act addresses the annual update to the
standard prospective payment amounts by the home health applicable
increase percentage as specified in the statute.
Section 1895(b)(4) of the Act governs the payment computation.
Sections 1895(b)(4)(A)(i) and (b)(4)(A)(ii) of the Act require the
standard prospective payment amount to be adjusted for case-mix and
geographic differences in wage levels. Section 1895(b)(4)(B) of the Act
requires the establishment of an appropriate case-mix adjustment factor
that explains significant variation in costs among different units of
services. Similarly, section 1895(b)(4)(C) of the Act requires the
establishment of wage adjustment factors that reflect the relative
level of wages, and wage-related costs applicable to home health
services furnished in a geographic area compared to the applicable
national average level. These wage-adjustment factors may be used by
the Secretary for the different geographic wage levels for purposes of
section 1886(d)(3)(E) of the Act.
Section 1895(b)(5) of the Act gives the Secretary the option to
make additions or adjustments to the payment amount otherwise made in
the case of outliers because of unusual variations in the type or
amount of medically necessary care. Total outlier payments in a given
fiscal year (FY) may not exceed 5 percent of total payments projected
or estimated.
In accordance with the statute, we published a final rule (65 FR
41128) in the Federal Register on July 3, 2000 to implement the HH PPS
legislation. This final rule established requirements for the new PPS
for home health services as required by section 4603 of the BBA, and as
subsequently amended by section 5101 of the Omnibus Consolidated and
Emergency Supplemental Appropriations Act (OCESAA) for Fiscal Year
1999, (Pub. L. 105-277), enacted on October 21, 1998; and by sections
302, 305, and 306 of the Medicare, Medicaid, and SCHIP Balanced Budget
Refinement Act (BBRA) of 1999, (Pub. L. 106-113), enacted on November
29, 1999. The requirements include the implementation of a PPS for home
health services, consolidated billing requirements, and a number of
other related changes. The HH PPS described in that rule replaced the
retrospective reasonable-cost-based system that was used by Medicare
for the payment of home health services under Part A and Part B.
For a complete and full description of the HH PPS as required by
the BBA, see the July 2000 HH PPS final rule.
B. Deficit Reduction Act of 2005
On February 8, 2006, the Deficit Reduction Act (DRA) of 2005 (Pub.
L. 109-171) was enacted. This legislation affected updates to HH
payment rates for CY 2006. The DRA also introduces home health care
quality data and its effects on payments to HHAs beginning in CY 2007.
Specifically, section 5201 of the DRA changed the CY 2006 update
from the applicable home health market basket percentage increase minus
0.8 percentage point to a 0 percent update.
In addition, section 5201 of the DRA amends section 421(a) of the
Medicare Prescription Drug, Improvement, and Modernization Act of 2003
(MMA) (Pub. L. 108-173, enacted on December 8, 2003). The amended
section 421(a) of the MMA requires that for home health services
furnished in a rural area (as defined in section 1886(d)(2)(D) of the
Act) on or after January 1, 2006 and before January 1, 2007, that the
Secretary increase the payment amount otherwise made under section 1895
of the Act for home health services by 5 percent. The statute waives
budget neutrality for purposes of this increase since it specifically
states that the Secretary must not reduce the standard prospective
payment amount (or amounts) under section 1895 of the Act applicable to
home health services furnished during a period to offset the increase
in payments resulting in the application of this section of the
statute.
The 0 percent update to the payment rates and the rural add-on
provisions of the DRA were implemented through Pub. L. 100-20, One Time
Notification, Transmittal 211 issued on February 10, 2006.
In addition, section 5201 of the DRA requires HHAs to submit data
for purposes of measuring health care quality. This requirement is
applicable for CY 2007 and each subsequent year. If an HHA does not
submit quality data, the home health market basket percentage increase
will be reduced 2 percentage points.
C. Updates to the HH PPS
As required by section 1895(b)(3)(B) of the Act, we have
historically updated the HH PPS rates annually in a separate Federal
Register document. In those documents, we also incorporated the
legislative changes to the system required by the statute after the
BBA, specifically the MMA. On November 9, 2006, we published a final
rule titled ``Medicare Program; Home Health Prospective Payment System
Rate Update for Calendar Year 2007 and Deficit Reduction Act of 2005
Changes
[[Page 25358]]
to Medicare Payment for Oxygen Equipment and Capped Rental Durable
Medical Equipment; Final Rule'' (CMS-1304-F) (71 FR 65884) in the
Federal Register that updated the 60-day national episode rates and the
national per-visit amounts under the Medicare PPS for home health
services for CY 2007. In addition, this final rule ended the one-year
transition period that consisted of a blend of 50 percent of the new
area labor marker designations' wage index and 50 percent of the
previous area labor market designations' wage index. We also revised
the fixed dollar loss ratio, which is used in the calculation of
outlier payments. According to section 5201(c)(2) of the DRA, this
final rule also reduced, by 2 percentage points, the home health market
basket percentage increase to HHAs that did not submit required quality
data, as determined by the Secretary.
D. System for Payment of Home Health Services
Generally, Medicare makes payment under the HH PPS on the basis of
a national standardized 60-day episode payment rate that is adjusted
for case-mix and wage index. The national standardized 60-day episode
payment rate includes the six home health disciplines (skilled nursing,
home health aide, physical therapy, speech-language pathology,
occupational therapy, and medical social services) and medical
supplies. Durable medical equipment covered under home health is paid
for outside the HH PPS payment. To adjust for case mix, the HH PPS uses
an 80-category case-mix classification to assign patients to a home
health resource group (HHRG). Clinical, functional, and service
utilization are computed from responses to selected data elements in
the OASIS assessment instrument.
For episodes with four or fewer visits, Medicare pays on the basis
of a national per-visit amount by discipline, referred to as a LUPA.
Medicare also adjusts the national standardized 60-day episode payment
rate for certain intervening events that are subject to a partial
episode payment adjustment (PEP adjustment) or a significant change in
condition adjustment (SCIC adjustment). For certain cases that exceed a
specific cost threshold, an outlier adjustment may also be available.
E. Summary of Home Health Payment Research
The objective of a prospective payment system that is case-mix
adjusted is to predict resource costs of providing care to similar
types of patients and to align payments to those costs. As MEDPAC
points out in their December 2005 Report to Congress, if the case-mix
is not aligned appropriately to resource costs, then the PPS may
overpay for some services and underpay for others.
Since the July 3, 2000 final rule, we have stated our intention to
monitor the new PPS and make refinements to the system as needed. We
believe refinements are now needed to improve the performance and
appropriateness of the HH PPS, which has not undergone major
refinements since its implementation in October of 2000. The general
goal of any refinements would be to ensure that the payment system
continues to produce appropriate compensation for providers while
retaining opportunities to manage home health care efficiently. Also
important in any refinement is maintaining an appropriate degree of
operational simplicity. The analytic goals of our refinement research
included improving the accuracy of the case-mix model, understanding
the descriptive characteristics of the program and the use of payment
adjusters, understanding variations in HHA margins, and the simulation
of potential changes to payment methodology.
We contracted with Abt Associates, Inc., of Cambridge,
Massachusetts to conduct several analyses in order to achieve these
objectives. In particular, the Abt Associates analyses focused on the
resource needs of long stay patients; alternatives to the current
therapy threshold; the potential for a more extensive set of variables
to improve the accuracy of the Clinical on Top (COT) model used to
define the HHRG; alternative ways to account for non-routine medical
supplies (NRS); utilization and episode characteristics; and HHA
margins. In order to conduct these analyses, Abt Associates primarily
used data files created from a 20 percent sample of claims data
collected between 2001 and 2004, Outcome and Assessment Information Set
(OASIS) data linked to claims, and cost reports. For measures of
resource use, Abt Associates used weighted minutes for the case-mix
refinements research. For research on accounting for nonroutine
supplies costs, Abt Associates analyzed supplies charges reported on
claims after adjusting them using cost-to-charge ratios from selected
cost reports. These analyses are described in more detail in section
II.A.
In addition to these analyses, two Technical Expert Panel (TEP)
meetings were conducted, under contract with Abt Associates, on
December 15, 2005, and March 14, 2006. These TEP meetings provided an
opportunity for experts, industry representatives, and practitioners in
the field of home health care to provide feedback on Abt's research
examining the HH PPS and exploration of payment policy alternatives.
Abt considered this feedback when developing recommendations for
refinements to the HH PPS. The refinements to the HH PPS described in
the following sections are the culmination of substantial research
efforts focusing on several areas identified for possible improvements.
II. Provisions of the Proposed Regulation
[If you choose to comment on issues in this section, include the
caption ``PROVISIONS OF THE PROPOSED REGULATIONS'' at the beginning of
your comments.]
A. Refinements to the Home Health Prospective Payment System
The Medicare HH PPS has been in effect since October 1, 2000. As
set forth in the final rule published July 3, 2000 in the Federal
Register (65 FR 41128), the unit of payment under the Medicare HH PPS
is a national standardized 60-day episode payment rate. As set forth in
42 CFR 484.220, we adjust the national standardized 60-day episode
payment rate by a case-mix grouping and a wage index value based on the
site of service for the beneficiary. Since the July 3, 2000 final rule,
we have stated our intention to monitor the new PPS and make
refinements to the system as needed. We believe refinements are now
required to improve the performance and appropriateness of payment for
the HH PPS. After implementation of the HH PPS, we received a number of
public comments suggesting ways in which the payment system could be
improved. We took those comments into consideration as we proceeded to
explore the HH PPS for potential areas for refinement. This proposed
rule sets forth the first major refinements to the HH PPS since its
implementation in October of 2000. This proposed rule identifies seven
major areas of the HH PPS that were identified as possible areas for
refinement. Those areas are: (1) The case mix model; (2) changes in
case mix coding; (3) the PEP adjustment; (4) the LUPA; (5) the SCIC
adjustment; (6) method of accounting for NRS, and (7) the outlier
adjustment. While this proposed rule proposes to implement all of
refinements discussed in this rule effective January 1, 2008, we
recognize that there may be operational considerations, affecting CMS
or the
[[Page 25359]]
industry, which could necessitate an implementation schedule that
results in certain refinements becoming effective on different dates (a
split-implementation). We would like to solicit suggestions and
comments from the public on this matter.
1. Current Payment Model
On July 3, 2000, we published a final rule (65 FR 41128) in the
Federal Register. In that rule, we described a system for home health
case-mix adjustment developed under a research contract with Abt
Associates, Inc., of Cambridge, Massachusetts. Using selected data
elements from the OASIS and an additional data element measuring
receipt of at least 10 visits for therapy services, the case-mix system
projects patient resource use based on patient characteristics. These
data elements were selected because they were shown to influence home
health resource utilization upon statistical analysis of data from
approximately 30,000 episodes. This model used data from first episodes
only and a relatively small set of clinical, functional, and service
utilization variables. Clinical judgment, the relative predictive value
of potential case-mix variables, their susceptibility to gaming and
subjectivity, and administrative implications were considered in the
final resolution of the elements retained in the final model.
The data elements are organized into three dimensions to capture
clinical severity factors, functional severity factors, and services
utilization factors influencing case-mix. In the clinical and
functional dimensions, each data element is assigned a score value
derived from multiple regression analysis of the Abt research data. The
score value measures the impact of the data element on total resource
use. Scores are also assigned to data elements in the services
utilization dimension. To find a patient's case-mix group, the case-mix
grouper software sums the patient's scores within each of the three
dimensions. The resulting sum is used to assign the patient to a
severity level in each dimension. There are four clinical severity
levels, five functional severity levels, and four services utilization
severity levels. Thus, there are 80 possible combinations of severity
levels across the three dimensions. Each combination defines one of the
80 HHRGs in the case-mix system. For example, a patient with high
clinical severity, moderate functional severity, and low services
utilization severity is placed in the same group with all other
patients whose summed scores place them in the same set of severity
levels for the three dimensions.
We summarized the performance of the final PPS model for the PPS
using the R-squared statistic. An initial episode was defined as the
first home health episode of care for a given beneficiary in a sequence
of adjacent episodes. For the purposes of our analysis, we defined a
sequence of adjacent episodes for a beneficiary as a series of claims
with no more than 60 days without home care between the end of one
episode, which is the 60th day (except for episodes that have been PEP-
adjusted), and the beginning of the next episode. At the time, based on
data from the model development sample, this model's R-squared
statistic was 0.34. In other words, the model explained 34 percent of
the variation in resource use.
2. Refinements to the Case-Mix Model
Extensive research has been conducted to investigate ways to
improve the performance of the case-mix model. We found that the
addition of separate regression equations to account for later episodes
and multiple therapy thresholds (replacing the current threshold of 10
therapy visits) significantly improved the fit and performance of the
case-mix model. Further, we expanded the set of variables to include
new diagnosis groups, comorbidities, and interactions, yielding models
that performed better in simulations. We feel that these changes would
improve the HH PPS by allowing more accurate case-mix adjustment
without providing incentives for providers to distort appropriate
patterns of care.
As with the original case-mix model, the general approach to
developing a case-mix model was to use patient data and other
appropriate data to create a regression model for resource use over the
course of a 60-day episode. Case-mix refinement analysis focused on
investigating resource use in episodes that occur later in treatment as
well as the initial episode; testing additional clinical, functional,
and demographic variables; exploring the effect of comorbidities; and
testing new therapy thresholds.
The basis for selecting these areas of analysis will be described
in sections II.2.a., II.2.b., and II.2.c.
As with our case-mix studies that resulted in the case-mix
methodology discussed in the July 3, 2000 HH PPS final rule, the
dependent variable in these refinement studies is an estimate of cost
known as resource cost. To derive the resource cost estimate, the total
minutes reported on the claim for each discipline's visits are
converted to a resource cost. Resource cost results from weighting each
minute by the national average labor market hourly rate for the
individual discipline that provided the minutes of care. Bureau of
Labor Statistics data are used to derive the hourly rate. The sum of
the weighted minutes is the total resource cost estimate for the claim.
This method standardizes the resource cost for all episodes in the
analysis file.
Based on the findings of our analysis of the case-mix adjustment
under HH PPS, which we describe in section II.A.2, we propose that the
case-mix adjustment be refined to incorporate an expanded set of case-
mix variables to capture the additional clinical conditions and
comorbidities; four separate regression models that recognize four
different types of episodes; and a graduated, three-threshold approach
to accounting for therapy utilization. We refer to the four separate
regression models in this proposed case-adjustment system as the four-
equation model. The first regression equation is for low-therapy
episodes (less than 14 therapy visits) that occur as the first or
second episode in a series of adjacent episodes (Episodes are
considered to be ``adjacent'' if they are separated by no more than a
60-day period between claims). The second regression equation is for
high-therapy episodes (14 or more therapy visits) occurring as the
first or second episode in a series of adjacent episodes. The third
equation is for low-therapy episodes (under 14 therapy visits)
occurring after the second episode in a series of adjacent episodes.
And the fourth equation is for high-therapy episodes (14 or more
therapy visits) occurring after the second episode in a series of
adjacent episodes. As described in further detail below, these
equations incorporate a graduated, three-threshold approach to
accounting for therapy utilization. The 153 case mix groups created
from the results of the four-equation model are also described below,
as is the method we used to form the groups.
a. Analysis of Later Episodes
As a starting point for our analysis, we examined the performance
of our original model using data, derived from the National Claims
History, reflecting the period after the HH PPS was initiated. These
data from the period after the commencement of the HH PPS, a large
random sample of claims from CY 2003, indicate the performance of the
case-mix model differs from the original estimate, which reflected data
from the time of the Abt case-mix study.
[[Page 25360]]
The more recent data reflect both the inclusion of episodes beyond the
first episode as well as behavioral changes of health care providers
under the HH PPS. The R-squared statistic estimated from the more
recent data is approximately 0.21. An appropriate comparison with the
initial R-square statistic (0.34) is the R-squared value estimated from
the more recent data's initial episodes, which is 0.29. We therefore
believe the data reflect a more modest reduction in model performance
of 0.05. However, the value of the R-squared statistic calculated on
all the data, 0.21, is an indication that the case-mix model does not
fit non-initial episodes as well as it fits initial episodes.
Therefore, one focus of our refinement work was to investigate resource
use in episodes that occurred later in treatment as well as early
episodes.
Based on exploratory analysis, we defined ``early'' episodes to
include, not only the initial episode in a sequence of adjacent
episodes, but also the next adjacent episode, if any, that followed the
initial episode. ``Later'' episodes were defined as all adjacent
episodes beyond the second episode. When we analyzed the performance of
the case-mix model for later episodes, we determined there were two
important differences for episodes occurring later in the home health
treatment compared to earlier episodes: higher resource use per episode
and a different relationship between clinical conditions and resource
use.
Using a large, random sample of episodes, we found that the
estimated resource cost of early episodes is approximately 7 percent
lower than the estimated resource cost of later episodes. The current
case-mix model weights all episodes equally.
Furthermore, our exploratory regression models indicated that the
relationships between case-mix variables and resource use differed
between earlier and later episodes. This suggested that a scoring
system that differed for earlier and later episodes could potentially
perform better than a single scoring system. The system of four
separate regression equations allows the scores to differ according to
whether the episode is early or later. We recognize that this approach
introduces more complexity into the case-mix adjustment system.
However, less complex approaches that did not depend on separate
equations did not perform as well in terms of predictive accuracy; for
example, we explored using one equation in which we modeled additional
lump-sum costs due to the timing of an episode in a sequence of
adjacent episodes. This proved to be unsatisfactory because it
addressed only one of the two important differences presented by later
episodes, that is, their generally higher cost level.
For the purposes of payment, we propose to make changes to the
OASIS (see section III. Collection of Information Requirements), by
adding a new OASIS item to capture whether an episode is an early or
later episode. If an HHA is uncertain as to whether the episode is an
early or later episode, we propose to base payment as though the
episode were an early episode. Most patients do not have more than one
episode in a year. Consequently, we believe that selecting early as the
default is the best guess as to the eventual outcome of whether an
episode is early or later.
b. Addition of Variables
Since the system for case-mix adjustment was first implemented, we
have received comments suggesting ways in which case-mix adjustment may
be improved. Most of these comments requested that we add specific
variables or conditions to the case-mix model. We were also asked to
examine the appropriateness of including additional diagnosis groups,
comorbidities in general and specific comorbidities, for instance,
heart conditions, additional wound-related indicators, and other
patient characteristics. We considered these comments as we proceeded
to explore potential case-mix changes. We also considered comments
received during the initial rulemaking process, such as comments
pertaining to clinical issues and social characteristics such as
caregiver availability.
We evaluated variables for inclusion in a refined case-mix model in
much the same way that we did for the 2000 final rule, in that we
analyzed the relationship between resource use and patient
characteristics. Whereas the original case-mix study required us to
collect logs from a sample of episodes for the measure of resource use,
for this analysis, we were able to measure resource use directly from
the claims sample. The measures of patient characteristics come from
OASIS assessments. Under a contract with Fu Associates of Arlington,
Virginia, Standard Analytical Claims Files from the National Claims
History were cleaned, edited, and linked to the OASIS assessment
associated with the beginning of each claim period. Abt Associates
subsequently used these analytic files to draw large samples of claims
for analysis.
In the course of refining the current case-mix model, we continued
to monitor the performance of two special variables in explaining
resource use. These variables are dual-eligibility for Medicare and
Medicaid and caregiver support. The two variables are of interest to
some agencies because of their perceived impact on resource use and
overall profitability. Patients dually eligible for Medicare and
Medicaid may have health care needs that exceed the average needs due
to the health status and utilization differences associated with low-
income populations. Some agencies with caseloads containing large
numbers of dual eligibles have commented that they are penalized under
the HH PPS system because of their willingness to serve a disadvantaged
population without payments explicitly recognizing such agencies'
higher costs. We have also received comments that episodes involving
patients without a caregiver were underpaid by the HH PPS, and that
some agencies would be reluctant to admit such patients because of
financial implications. These commenters believe that the low admission
rate of patients without caregivers (about 2 percent of all episodes)
is evidence of this reluctance.
During our development of the original case-mix model implemented
in the July 2000 final rule, using the Abt Associates case-mix study
sample, we tested the Medicaid variable (which indicates whether
Medicaid was among the patient's payment sources). At that time, we
found that it did not contribute meaningfully in explaining variation
in resource use. Similarly, we tested the caregiver variable and it did
not contribute to explaining variation in resource cost, either.
Regarding the caregiver variable, we recognized in the July 3, 2000,
final rule that adjusting payment in response to the presence or
absence of a caregiver may be seen as inequitable. To the extent that
availability of caregiver services, particularly privately paid
services, reflects socioeconomic status differences, we indicated that
reducing payment for patients who have caregiver assistance may be
particularly sensitive in view of Medicare's role as an insurance
program rather than a social welfare program. Furthermore, we stated
that adjusting payment for caregiver factors would risk introducing new
and negative incentives into family and patient behavior. In the
discussion in the July 3, 2000 final rule (65 FR 41145), we also
indicated our belief that it is questionable whether Medicare should
adopt a payment policy that could weaken informal familial supports
currently benefiting patients at times when they are most vulnerable.
[[Page 25361]]
In our analysis for this proposed rule, we again tested variables
for dual eligibility and caregiver support. We operationalized the
Medicaid variable from the OASIS, using the presence of a Medicaid
number on the assessment as the indicator for Medicaid eligibility. We
found that Medicaid remains a marginal predictor at best, with a very
low score, after accounting for a broad range of clinical and
functional variables that predict resource use. We believe adding a
Medicaid variable is not justified in view of these results, especially
considering the added administrative burdens for both agencies and
Medicare that using such a variable would entail. These include costs
of ascertaining whether the reported Medicaid number is correct and
whether the eligibility status as reported on the assessment is
current.
We also operationalized a variable for support from a caregiver
from the OASIS assessment, item M0350, Assisting persons other than
home health agency staff. This variable identified patients without any
caregiver. While analyzing the payment adequacy of the four-equation
model (as explained further below) for patients without a caregiver we
found that, on average, episodes without caregivers would be
``underpaid''. However, the score to be gained by adding the variable
is not large (5 to 13 points, depending on the episode), and the
overall ability of the four-equation model to explain resource costs is
improved only minimally by adding this variable.
Therefore, we are not proposing that this variable be added to the
case-mix model. We continue to believe that including this kind of
variable in the case-mix system raises significant policy concerns. We
maintain that a case-mix adjustment should not discourage assistance
from family members of home care patients, nor should it make patients
feel there is some financial stake in how they report their familial
supports during their convalescence.
We continue to believe that adjusting payment in response to the
absence of a caregiver would introduce negative incentives with adverse
affects on home health Medicare beneficiaries. Furthermore, we are
doubtful that today's low rate of episodes without a caregiver (2 to 3
percent) reflects access barriers for these patients and nothing more.
We believe part of the reason for the low rate may be that under a
bundled payment system agencies are more careful about ascertaining
whether support is available and encourage use of caregivers within the
beneficiary's home.
For exploratory modeling of case-mix in our refinement work, in
addition to using existing case-mix variables from the OASIS, new
variables were created. Diagnosis codes reported on both the claims and
the OASIS were used extensively to form new or revised diagnosis groups
for inclusion in case-mix models. As a result, developmental models
included many new variables, including an expanded set of primary and
secondary diagnoses, as well as interaction terms that describe the
effect of combinations of patient conditions or characteristics on
resource cost. Using these new analytic files, it was possible to
explore some conditions that were too infrequent to study in the
original case-mix sample. For example, as suggested by commenters,
Abt's analysis tested the impact on resource use of having multiple
conditions from M0250, which reports on therapies received at home,
including intravenous infusion, and enteral and parenteral nutrition.
The results showed that a variable indicating the simultaneous presence
of multiple conditions from OASIS item M0250 did not improve the
accuracy of the case-mix model. However, we did find that having
separate scores for parenteral nutrition and IV therapy were not
necessary.
Abt's case-mix analysis focused on various issues, such as changes
to the list of conditions forming our diagnosis groups, additions of
comorbidities, prediction of therapy resources, and interactions. The
performance of each variable was scrutinized based on several criteria.
First, variables were assessed for statistical performance. Variables
that did not enhance the accuracy of the model were marked for
exclusion.
Variables were also assessed for policy appropriateness. Some
statistically significant variables were excluded if they offered
incentives for providers to distort patterns of good care or posed
excessive administrative burden on HHAs. In addition, some
statistically weak variables considered important for clinical or
policy reasons were added back to the model for further analysis.
We note we excluded a variable from this proposal, based in part on
concerns of excessive administrative burden. We propose to exclude
OASIS item M0175, which the case-mix system uses to identify the
patient's pre-admission location, from the case-mix models. Under this
proposal, there would be no case-mix score for M0175. Operational
experience with M0175 revealed that some agencies have encountered
difficulties in ascertaining precise information about the patient's
pre-admission location during the initial assessment. These
difficulties, suggestive of unforeseen administrative complexities,
contributed to our proposal to eliminate M0175 from the case-mix model.
In addition, the M0175 item did not perform well in the four-
equation model. We found that the results differed across the equations
in ways that were difficult to interpret. Moreover, the results showed
that the impact of including information from M0175 was small, both in
terms of case-mix scores and the overall payment accuracy of the case-
mix model.
In weighing the indications of administrative complexities due to
M0175 against the limited performance of M0175 in our analysis, we do
not find that the contribution of this item in explaining case-mix
justifies the operational challenge of achieving perfectly accurate
reporting for payment. Thus, as noted above, we are proposing to
eliminate it from the case-mix model. However, we continue to believe
that it is necessary for the conditions of participation and the OASIS
to require that agencies establish the patient's recent history of
health care before determining the plan of care. This determination
must be made with sufficient accuracy to allow appropriate planning,
even if precise dates and institutional certifications are not exactly
known. For example, it will be important to know the amount and types
of rehabilitation treatment the patient has received, the type of
institution that delivered the treatment, and how recently it was
delivered.
The final set of proposed clinical conditions resulting from our
exploratory series of analyses covers more types of conditions than
were used in the original case-mix model (Tables 2a and 2b). We
identified conditions from diagnosis codes on both claims and OASIS in
a linked sample of claims from FY 2003 (OASIS items M0230 and M0240,
Diagnoses and Severity Index). For example, heart and mental conditions
are now assigned case-mix scores. More wound conditions are assigned
scores, based on results from adding variables to indicate wound-
related diagnosis codes beyond those in the current HH PPS case-mix
model. (See Table 2b for diagnosis codes that define each condition in
the model.)
We also propose to assign scores to certain secondary diagnoses,
used to account for cost-increasing effects of comorbidities. An
example is secondary cancer diagnoses, whose cost-increasing effects
are not as large as those for primary cancer diagnoses. However, with
most diagnosis groups, we did not
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make a distinction in the final model between primary placement and
secondary placement of a condition in the reported list of diagnoses.
We made case-by-case decisions on this question based on differences in
the impact on resource cost between the primary diagnosis and secondary
diagnosis. If differences were small, we combined cases reporting the
conditions, regardless of whether the listed position of the diagnosis
was primary or secondary. We believe this is an important protection
against unintended and undesirable incentive effects that could arise
if agencies perceive opportunities to change the placement of the
diagnosis due to nonclinical reasons. In a few instances, the reason
for combining the primary or secondary diagnoses was to improve the
robustness of the scores.
Finally, we also propose that a small number of interactions--
combinations of conditions in the same episode--be assigned scores, to
capture the synergistic effect on resource use of certain conditions
that coexist in the episode. In some instances, a condition appears as
an interaction with a functional limitation or a treatment variable
such as parenteral therapy. In Table 2a, the interaction scores are
added to the case-mix score whenever the two conditions defining the
interaction occur together in the episode. Interaction scores,
therefore, do not substitute for scores of other variables in Table 2a
that involve either only one or the other of the two conditions.
As noted earlier, we also found that, compared to early episodes,
later episodes could exhibit a different relationship between resource
costs and a condition. This is reflected in Table 2a by the absence of
a condition-related score from one or more of the four equations, or a
score that differs from one equation to another.
During the later phases of testing alternative formulations of an
expanded list of clinical conditions, we followed two rules in our
formation of diagnosis groups. These rules would ultimately affect the
operation of the case-mix grouper which would be created pursuant to
the revisions being proposed in this proposed rule. First, if an
episode record in our sample file listed both primary and secondary
diagnoses from the same diagnosis group, the model estimation procedure
recognized the primary diagnosis variable for that case but not the
secondary diagnosis variable. This means that an episode would not be
eligible to earn more than one score for the same diagnosis group. The
primary reason for this rule is that we are aware of diagnosis coding
conventions that would produce repeated instances of the same or
similar codes in the diagnosis list, and these conventions would build
redundancy into the modeling process. A major goal of the exploratory
modeling process was to investigate the impact of comorbidities by
recognizing secondary diagnoses, but redundancy inhibits our
achievement of that goal. Consequently, we sought to reduce this type
of redundancy. A further reason for adhering to this rule is to inhibit
a future decline in model performance, which might come about through
changes in coding behavior. If agencies were to perceive that redundant
coding boosts the episode score, they might engage in it more in the
future. The result would be a degradation in the ability of the case-
mix model to provide for accurate payment.
The second rule we used affected how we define the interactions
between conditions. The second rule is that, for purposes of forming
diagnosis groups to test interactions between conditions, cases with
either a primary or secondary diagnosis from the same diagnosis group
are combined into a single group. This means that mention of a given
diagnosis anywhere in the diagnosis list puts episodes in a single
group for that diagnosis, for purposes of analyzing interactions
between conditions. We believe this rule is consistent with our goal of
isolating effects of comorbidities. Specifically, because the reason
for studying interactions is to identify the effects of combinations of
conditions, we believe it is appropriate to measure the combinations,
regardless of the placement (that is, primary or secondary) of a
diagnosis on the claim. Further, combining the primary and secondary
diagnoses within groups increases the ability of the modeling process
to uncover meaningful interaction effects. The second rule also works
to keep the model as simple as possible. Simplicity helps to limit the
risk that the model would not fit well for later data sets. Simplicity
also limits the amount of added administrative burden that could come
from using a more-complex model.
Changes to the OASIS are needed to enable agencies to report
secondary case-mix diagnosis codes. Specifically, the addition of
secondary diagnoses to the case-mix system (see Table 2a, case-mix
adjustment variables and scores) requires that the OASIS allow for
reporting of instances in which a V-code is coded in place of a case-
mix diagnosis other than the primary diagnosis. A case-mix diagnosis is
a diagnosis that determines the HH PPS case-mix group. Currently, the
OASIS allows for reporting of instances of displacement involving
primary diagnosis only (M0245). Consequently, because of the nature and
significance of the changes needed, we are proposing to delete the
OASIS item M0245 and replace it with a new OASIS item. (see section
III. Collection of Information Requirements).
c. Addition of Therapy Thresholds
As set forth in the July 3, 2000 final rule (65 FR 1128), patients
were grouped according to their therapy utilization status in order to
ensure that patients who required therapy would maintain access to
appropriate services. Specifically, we defined a therapy threshold of
at least 8 hours of combined physical, speech, or occupational therapy
over the 60-day episode, to identify ``high'' therapy cases. The 8-hour
threshold was converted to a threshold of 10 therapy visits because the
average visit length for therapy noted in our data was approximately 48
minutes. We instituted the threshold based on clinical judgment about
the level of therapy that reflects a clear need for rehabilitation
services and that would reasonably be expected to result in meaningful
treatment over the course of 60 days.
Since the implementation of the therapy threshold in the HH PPS, we
have received comments from the public requesting that we study and
refine this approach to accounting for rehabilitation needs in the
case-mix system. Commenters have suggested that a single therapy
threshold did not fairly reflect the variation in therapy utilization
and need. Some commenters requested that we re-examine the 10-visit
threshold. Other commenters recommended that we work to eliminate the
therapy threshold, in part due to concerns that the therapy threshold
might introduce incentives to distort service delivery patterns for
payment purposes.
Our data analysis revealed evidence of undesirable incentives from
the 10-visit therapy threshold. Our analysis suggested that the 10-
visit therapy threshold might have distorted service delivery patterns.
In our analysis sample, of all episodes at or above the threshold, half
were concentrated in the range of 10 to 13 therapy visits. This range
had the highest concentration of therapy episodes among episodes with
at least one therapy visit. In contrast, a large analysis sample from a
period immediately preceding the HH PPS indicated that the highest
concentration of therapy episodes was in a range
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below the 10-visit threshold--approximately 5 to 7 therapy visits.
Under the HH PPS, there were two peaks in the graphic depiction of
numbers of episodes according to the number of therapy visits delivered
during the episode. One peak was below the therapy threshold and the
other was the 10 to 13 visit peak above the therapy threshold. In the
pre-PPS sample, there was only one peak in the depiction, and it was
the concentration of episodes at 5 to 7 therapy visits--below the
current 10-visit therapy threshold. All of these results suggested that
the 10-visit threshold was responsible for a marked shift in
rehabilitation services delivery under the HH PPS, a shift that we
believe would probably not have occurred in the absence of the therapy
threshold. Commenters have reinforced our belief that the impact of the
single 10-visit threshold on therapy provision frequently distorted the
clinically based decision-making that should drive the delivery of
rehabilitation services.
In our early efforts to address problems inherent in using a
therapy threshold, we conducted analyses to identify new predictors of
therapy resource use, with the goal of achieving large gains in
explanatory power that would render the therapy threshold unnecessary.
We used predictor variables including pre-admission status on
activities of daily living (ADL), more diagnoses with a focus on
conditions such as stroke, and more OASIS variables. However, models
that included these particular explanatory variables predicted the
probability of using therapy, but not how much therapy would be used.
Successive studies to account for therapy resources followed the
goal of reducing the impact of a therapy threshold on the payment
weights. The main conclusion from these studies was that therapy
resources cannot be predicted with sufficient accuracy to eliminate the
need for therapy thresholds in the HH PPS case-mix system. Although we
tried several alternative approaches, no approach added sufficient
predictive power to the case-mix model. Therefore, continued analysis
focused primarily on refining the therapy threshold approach to reduce
undesirable incentives. This work involved experimentation with
alternative sets of thresholds consisting of more than one threshold.
After testing several sets of thresholds, and in consideration of
the comments received, we proceeded to construct case-mix models with
thresholds at 6, 14, and 20 therapy visits. We used these thresholds
based on data analysis and, in part, on policy considerations.
Data analysis suggested it would be appropriate to add new
thresholds both below and above the 10-visit level. One reason was that
our review of data from the HH PPS period showed agencies provided
large numbers of episodes with therapy visits in an interval below 10
visits. Moreover, data analysis suggested that, of all episodes with
numbers of therapy visits below the 10-visit therapy threshold, some
subsets did not receive an appropriate case-mix weight under the HH
PPS. Specifically, episodes with 6 to 9 therapy visits had resource
costs that seemingly exceeded the payment proxied in our analysis by
the predicted resource cost under the current case mix model. However,
we now believe that several common treatment plans require only about 6
visits, for example, assessments and treatment of certain types of
patients at high risk for falls. We are therefore proposing that one
threshold be added at 6 therapy visits.
In considering thresholds above the current 10-visit threshold, we
observed that nearly half of episodes involving therapy comprise
episodes with 6 to 13 therapy visits. Therefore, we are proposing a
second threshold at 14 therapy visits, which would have two advantages.
First, this range covers the two peaks (that is, the one we observed
below the 10-visit therapy threshold and the one we observed above the
10-visit threshold) in the distribution of therapy visits under the HH
PPS. By avoiding a therapy threshold within this range, we hope to
reduce the influence of payment incentives on treatment decisions.
Second, we believe that the interval of 6 to 13 therapy visits
represents a reasonable range of treatment levels for most
rehabilitation episodes. For example, the range of 6 to 13 therapy
visits encompasses typical treatment plans for both knee- and hip-
replacement patients. As we describe later in this section, we propose
to use further steps to address payment accuracy, by adding payment
gradations within the intervals bounded by the three thresholds we are
proposing.
We further observed that only a relatively small fraction of
patients use 14 or more therapy visits. While no bright-line tests are
available to distinguish a 14-visit case, we have received comments
indicating that medical review staff at the fiscal intermediaries will
have less difficulty judging appropriateness of treatment plans at this
level, because such plans are intensive and not the norm.
Additionally, although few episodes require 20 or more therapy
visits, we set the third therapy threshold at 20 visits. Our concern is
to ensure access to appropriate treatment in the rare cases where such
intensive treatment is necessary. Our analysis suggested that these
episodes are extremely costly for agencies, so a payment adjustment to
accommodate this service level is appropriate. Furthermore, commenters
indicated that, because only rare cases should warrant this high number
of therapy visits, monitoring of claims to prevent abuse of this
payment provision, using our medical review resources, is feasible
operationally.
Adding therapy thresholds in the revised case-mix regression model
improves the ability of the model to predict resource use. The R-
squared values for a three-therapy threshold model increased
substantially for both early and later episodes over the R-squared
values for a single therapy threshold model. In other words, using
additional therapy thresholds clearly improved the case-mix system's
ability to classify episodes into homogeneous cost groups.
The combined effect of the new therapy thresholds and payment
gradations (to be described below) is expected to reduce the
undesirable emphasis in treatment planning on a single therapy visit
threshold, and to restore the primacy of clinical considerations in
treatment planning for rehabilitation patients.
During the analysis of the therapy threshold, we considered ways to
provide for payment gradations between the therapy thresholds. We
sought a way to implement a gradual increase in payment (see Table 1)
between the proposed first and third therapy thresholds. We believe a
case-mix model that increases payment with each added visit between the
proposed first and third thresholds would achieve two goals. First, a
gradual increase better matches payments to costs than the therapy
thresholds alone. Second, a gradual increase avoids incentives for
providers to distort patterns of good care created by the increase in
payment that would occur at each proposed therapy threshold. However,
as a disincentive for agencies to deliver more than the appropriate,
clinically determined number of therapy visits, we are also proposing
that any per-visit increase incorporate a declining, rather than
constant, amount per added therapy visit. We implemented this in the
case-mix model by decreasing slightly the added amount per therapy
visit as the number of therapy visits grew above the proposed 6-visit
threshold. Specifically, we began with a value determined from our
sample--the estimated marginal
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resource cost incurred by adding a 7th therapy visit to the treatment
plan. This is the first additional visit above the proposed six-visit
therapy threshold. The estimated marginal cost of adding a 7th therapy
visit to an episode with six therapy visits was $36. Using this value
as our starting point, we required the case-mix model to add a slightly
lower value to the total episode resource cost with each additional
therapy visit provided, up to the 19th therapy visit. This proposed
approach imposes a deceleration of the growth in payment with each
additional therapy visit. However, this proposed approach does not
reduce total payments to home health providers, because the regression
analysis still predicts the full resource cost of the episode. Table 1
shows the values that we imposed in the four-equation model estimation
procedure to implement a deceleration in the added resource cost for
individual therapy visits between 6 and 20 therapy visits. The
individual values begin at $36 and then decline at a constant rate of
one resource cost dollar per therapy visit between 6 and 20 therapy
visits. These values represent the score that was imposed in the model
for adding each additional therapy visit. The case-mix model that
incorporates the imposed scores is called a ``restricted regression
model.'' The results of the restricted regression model of the four-
equation system, including scores for diagnoses and conditions, and R-
squared statistics, exhibited little change from imposing this pattern
of deceleration in cost growth due to additional therapy visits.
Table 1.--Resource Cost Values Imposing Deceleration Trend in Four-
Equation Model
------------------------------------------------------------------------
Number of Resource cost
Equation and services utilization therapy visits values imposed
severity level in severity in regression
level procedure
------------------------------------------------------------------------
1st and 2nd Episodes, 6-13 Therapy
Visits
S3.............................. 7, 8, 9 36, 35, 34
S4.............................. 10 33
S5.............................. 11, 12, 13 32, 31, 30
1st and 2nd Episodes, 14-19 Therapy
Visits
S1*............................. 15 28
S2.............................. 16, 17 27, 26
S3.............................. 18, 19 25, 24
3rd+ Episodes, 6-13 Therapy Visits
S3.............................. 7, 8, 9 36, 35, 34
S4.............................. 10 33
S5.............................. 11, 12, 13 32, 31, 30
3rd+ Episodes, 14-19 Therapy Visits
S1*............................. 15 28
S2.............................. 16, 17 27, 26
S3.............................. 18, 19 25, 24
------------------------------------------------------------------------
* For the second and fourth equations of the four equation model, S1
includes 14 therapy visits, but no value was imposed in the regression
procedure for a 14th therapy visit because the regression intercept
estimate automatically includes the resource cost impact.
The case-mix model at this stage was very detailed, because it
included variables incorporating information about thresholds and
therapy visit counts. We were concerned that, without streamlining the
therapy-related information in the case-mix model, the ultimate system
of case-mix groups would contain an excessive number of case-mix
groups. We recognize an extremely large number of case-mix groups would
make the HH PPS complex to administer. Because the therapy-related
details of the case-mix model are based on numbers of therapy visits,
another issue would be that many case-mix groups would be
differentiated based on visit counts, thereby making the system
dependent on visits and less of a bundled system of services.
Therefore, in order to form case-mix groups from the results of the
case-mix model, we grouped the individual levels of therapy visits into
small aggregates (1, 2, or 3 visits) (see Table 1). By doing so, we
avoided creating a per-visit schedule of payment to account for therapy
visits. We implemented these aggregations as differing severity levels
at a subsequent stage of payment system development, the payment
regression, which is described later in this section.
The proposed four