[Federal Register Volume 63, Number 173 (Tuesday, September 8, 1998)]
[Notices]
[Pages 47506-47513]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 98-24085]
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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Health Care Financing Administration
[HCFA-1045-N]
RIN 0938-AJ16
Medicare Program: Request for Public Comments on Implementation
of Risk Adjusted Payment for the Medicare+Choice Program and
Announcement of Public Meeting
AGENCY: Health Care Financing Administration (HCFA), HHS.
ACTION: Solicitation of comments; announcement of meeting.
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SUMMARY: This notice solicits further public comments on issues related
to the implementation of risk adjusted payment for Medicare+Choice
organizations. Section 1853(a)(3) of the Social Security Act (the Act)
requires the Secretary to implement a risk adjustment methodology that
accounts for variation in per capita costs based on health status and
demographic factors for payments no later than January 1, 2000. The
methodology is to apply uniformly to all Medicare+Choice plans. This
notice outlines our proposed approach to implementing risk adjusted
payment.
In order to carry out risk adjustment, section 1853(a)(3) of the
Act also requires Medicare+Choice organizations, as well as other
organizations with risk sharing contracts, to submit encounter data.
Inpatient hospital data are required for discharges on or after July 1,
1997. Other data, as the Secretary deems necessary, may be required
beginning July 1998.
The Medicare+Choice interim final rule published on June 26, 1998
(63 FR 34968) describes the general process for the collection of
encounter data. We also included a schedule for the collection of
additional encounter data. Physician, outpatient hospital, skilled
nursing facility, and home health data will be collected no earlier
than October 1, 1999, and all other data we deem necessary no earlier
than October 1, 2000. Given any start date, comprehensive risk
adjustment will be made about three years after the year of initial
collection of outpatient hospital and physician encounter data.
Comments on the process for encounter data collection are requested in
that interim final rule. We intend to consider comments received in
response to this solicitation as we develop the final methodology for
implementation of risk adjustment.
This notice also informs the public of a meeting on September 17,
1998, to discuss risk adjustment and the collection of encounter data.
The meeting will be held at the Health Care Financing Administration
headquarters, located at 7500 Security Boulevard, Baltimore, MD,
beginning at 8:30 a.m. Additional materials on the risk adjustment
model will be available on or after October 15, 1998, and may be
requested in writing from Chapin Wilson, Health Care Financing
Administration, Department of Health and Human Services, 200
Independence Avenue, S.W., Room 435-H, Washington, DC 20201.
DATES: We request that comments be submitted on or before October 6,
1998.
ADDRESSES: Mail written comments (1 original and 3 copies) to the
following address: Health Care Financing Administration, Department of
Health and Human Services, Attention: HCFA-1045-N, P.O. Box 26688,
Baltimore, MD 21207.
If you prefer you may deliver your written comments (1 original and
3 copies) to one of the following addresses:
Room 309-G, Hubert H. Humphrey Building, 200 Independence Avenue, SW.,
Washington, DC 20201, or
Room C5-09-26, 7500 Security Boulevard, Baltimore, MD 21244-1850
Because of staffing and resource limitations, we cannot accept
comments by facsimile (FAX) transmission. In commenting, please refer
to file code HCFA-1045-N. Comments received timely will be available
for public inspection as they are received, generally beginning
approximately 3 weeks after publication of a document, in Room 309-G of
the Department's offices at 200 Independence Avenue, SW., Washington,
DC, on Monday through Friday of each week from 8:30 a.m. to 5 p.m.
(phone (202) 686-7890).
FOR FURTHER INFORMATION CONTACT: Cynthia Tudor, (410) 786-6499.
SUPPLEMENTARY INFORMATION:
I. Background
Since 1985, Medicare payments to risk contracting Health
Maintenance Organizations (HMOs) for aged and disabled beneficiaries
living in a given county have been based on actuarial estimates of the
per capita cost Medicare incurs paying claims on a fee-for-service
(FFS) basis in that county. (Medicare's costs in paying claims for
beneficiaries with end-stage renal disease are not considered in these
county estimates, but are treated separately on a statewide basis.)
These county estimates have been adjusted for the demographic
composition of that county (age, gender, Medicaid eligibility status,
and institutional status) in order to produce a figure representing the
costs that would be incurred by Medicare on behalf of an average
Medicare beneficiary in the county. These county per capita payment
rates, adjusted for the average beneficiary, have been published
annually as the county rate book. Prior to January 1998, actual
payments for a given HMO enrollee were based on this county rate book
amount, adjusted by demographic factors associated with each enrollee.
Again, the demographic factors have been age, gender, Medicaid
eligibility, and institutional status. This methodology is known as the
``Adjusted Average Per Capita Cost'' (AAPCC) methodology, and HMOs with
Medicare contracts under section 1876 of the Social Security Act (the
Act) were paid on this basis between 1985 and 1997.
[[Page 47507]]
In enacting the new Part C of Title XVIII to create the
Medicare+Choice program, the Congress provided, a new section 1853 of
the Act, for a new methodology for paying organizations that enter into
Medicare+Choice contracts. Under this new methodology, the equivalent
of the above-described county rate book (that is, the county-wide
amount that is adjusted by an individual enrollee's demographic status
to determine the final payment amount) is based on the greatest of
three amounts. The first amount is a new blended payment rate
methodology that would combine the area specific amounts with national
data and would be subject to other adjustments. The second amount is a
new minimum specified rate amount (for example, $367 per month per
enrollee in 1998). The third amount is based on a 2 percent increase
over the prior year's rates, with the rate book for 1997 serving as the
baseline. As in the case of the AAPCC methodology described above, the
county rates under section 1853 of the Act, are adjusted for the
demographic status of each enrollee.
Under section 1876(k)(3) of the Act, the new Medicare+Choice
payment methodology under section 1853 of the Act applies to existing
HMO contracts under section 1876 for 1998, and to Medicare+Choice plans
beginning in 1999.
Section 1853(a)(3) of the Act requires the Secretary to develop and
implement a new risk adjustment methodology to be used to adjust the
county-wide rates under section 1853 of the Act to reflect the expected
relative health status of each enrollee. This new methodology, which
must be implemented by January 1, 2000, would replace the current
method of adjusting county-wide rates based on the four demographic
factors of age, gender, Medicaid eligibility, and institutional status.
The goal is to pay Medicare+Choice organizations based on better
estimates of health care costs of the population they enroll (relative
to the FFS population).
While the Medicare+Choice legislation mandates the implementation
of risk adjustment in general, the legislation provides the Secretary
with broad discretion to develop a risk adjustment methodology that
would ``account for variations in per capita costs based on health
status and other demographic factors.'' Because Medicare+Choice
legislation does not allow for the collection of any data other than
inpatient hospital data (in the near term), we are constrained
initially to using a model that requires only inpatient data. We are
currently receiving these data. In previous public meetings on
encounter data requirements, organizations have been briefed on the
Principal Inpatient Diagnostic Cost Group (PIP-DCG), created by HHS-
sponsored researchers at Health Economics Research, Inc., and Boston
and Brandeis. This is the only risk adjuster model that has been
developed to run solely on inpatient data. The model was recently
updated using 1995 and 1996 Medicare data.
The remainder of this notice outlines our proposed approach for
implementation of risk adjusted payments on January 1, 2000, discussing
both the risk adjustment methodology and the proposed risk adjustment
payment model. In the development of all risk adjustment payment
models, there are two tasks that must be performed: (1) The estimation
of the risk adjustment model, and (2) application of the risk
adjustment model to a payment system. The estimation of the PIP-DCG
model is described first.
A. The Principal In-Patient Diagnostic Cost Group (PIP-DCG) Model
In constructing a risk adjustment model, it is important to
determine which set of conditions should be used to adjust payments.
Under the current payment system, all enrollees are placed in a base
group paid according to demographic characteristics. In this risk
adjustment system, all conditions that appear as inpatient principal
diagnoses are candidates for adjusting payments. The base payment
category decreases as more conditions are placed into separate disease
groups. Because an inpatient hospital-based system depends on a
person's site of service, only a subset of conditions should be
recognized for changing payments. That is, the system should recognize
admissions for which inpatient care is most frequently appropriate. For
example, admissions for diseases most commonly treated on an outpatient
basis should remain in the base group and should not be used for
adjustment.
The PIP-DCG model was estimated using diagnostic information for
Medicare FFS enrollees from inpatient hospital stays during calendar
year 1995. The sample used in the estimation analyses consisted of
individuals included in the 5-percent sample of Medicare beneficiaries
who were alive and enrolled in Medicare during all of 1995, and on
January 1, 1996. Beneficiaries with certain characteristics (for
example, HMO enrollees and end-stage renal disease enrollees, new
Medicare eligibles in 1996) were excluded from the analyses. In
general, these exclusions were made to increase confidence that a
complete set of Medicare claims for each beneficiary in the sample data
set was included in the model development. The final estimation data
set included 1.4 million Medicare beneficiaries.
While the PIP-DCG model uses only inpatient diagnoses in creating
the risk adjustment classification system, the model predicts total
expected costs for the following year across multiple sites of
services. Consequently, all Medicare expenditures, other than those for
hospice care, were included in the calculation. Medicare expenditures
for hospice care were not included because Medicare+Choice
organizations are not responsible for hospice care. The model was
estimated assuming no time lag between the base year (diagnostic
information) and the predicted expenditures; that is, calendar year
1995 beneficiary diagnoses were used to predict calendar year 1996
expenditures.
1. From Diagnosis Groups (DxGroups) to PIP-DCGs
The risk adjustment model estimation process begins with a
classification system, forming the inherent logic of the model. For the
PIP-DCG model, diagnoses are classified into DxGroups based on the
principal inpatient diagnosis. The DxGroups comprise an exhaustive
classification of all valid International Classification of Diseases,
Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic codes. For
example, DxGroup 1, Central Nervous System Infections, includes ICD-9-
CM diagnostic codes for such conditions as encephalitis and meningitis.
The primary criteria in forming the DxGroups were clinical coherence
and an adequate sample size to estimate average expenditures.
Beneficiaries with multiple different inpatient diagnoses could have
multiple hospital stays, and would initially be placed in multiple
DxGroups.
Next, DxGroups were aggregated into payment groups, or PIP-DCGs,
using a sorting algorithm that ranked DxGroups based on 1996 actual
expenditures. For example, DxGroup 7 (Metastatic Cancer with a mean
future expenditure of $26,331) was placed in PIP-DCG 26. Highest
expenditure DxGroups were grouped into the ``highest'' PIP-DCG. Once
beneficiaries with the highest costs were placed into a DxGroup, those
beneficiaries and all their associated expenditures were removed from
the data for other DxGroups and then the DxGroups were re-ranked. The
DxGroups with the next most costly diagnoses were grouped into the next
highest numbered PIP-DCG, and those beneficiaries were removed from the
[[Page 47508]]
remaining DxGroups. The process was repeated until each beneficiary and
his or her expenditures were assigned to a single PIP-DCG group.
Beneficiaries with multiple inpatient diagnoses were placed in their
highest expenditure PIP-DCG group.
In this way, each PIP-DCG group was defined according to average
total expenditures for beneficiaries with inpatient diagnoses,
categorized and sorted using the DxGroups rather than diagnosis by
diagnosis. Based upon this sorting algorithm, more than 20 initial PIP-
DCGs were defined. Lower average expenditure PIP-DCG groups had lower
cost ranges (or intervals), while the highest average expenditure PIP-
DCG groups had wider ranges.1
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\1\ The PIP-DCG groupings were further refined using a number of
criteria. First, each original PIP-DCG group remained in the final
payment model only if it contained at least 1,000 beneficiaries from
the original sample; this minimum sample size was defined to assure
stability of estimated payments in the final model. If sample sizes
were smaller than 1,000, the potential PIP-DCG was expanded to
include DxGroups with average expenditures in the next lower range
until the sample size criteria was satisfied. If at any time during
the sorting algorithm a DxGroup had fewer than 50 beneficiaries
assigned to it, it was assigned to the base payment category. This
base payment category also included all beneficiaries (and
expenditures) for whom there was no inpatient diagnosis during 1995.
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2. Modifications to the PIP-DCG Model
After the initial sorting of DxGroups into PIP-DCG groups was
complete, a clinical panel reviewed the placement of the DxGroups and
their resulting predicted expenditures, to determine the
appropriateness of their application in a payment model. Through this
process, 75 DxGroups (covering about \1/3\ of the admissions) were
identified as: (1) Representing only a minor or transitory disease or
disorder, not clinically likely to result in significant future medical
costs, (2) rarely the main cause of an inpatient stay, or (3) vague or
ambiguous. These groups, as recommended by the clinical panel, were
identified as those most likely to result in inconsistent or
inappropriate reimbursements and were placed (with their associated
expenditures) in the base payment category (for which the payment is a
function of demographic factors). Examples of these groups include the
DxGroup for fluid/electrolyte disorders and malnutrition. Though the
treatment for individuals with this diagnoses are often quite costly in
the following year, the diagnosis is clinically vague and, therefore,
represented a likely target for coding ``creep.'' The clinical panel
concluded that many of the sickest individuals with this diagnosis were
likely to have another hospitalization that would trigger appropriate
increased reimbursements. Then, the remaining DxGroups were resorted
and placed into revised DCGs for the payment model. A total of 10 PIP-
DCGs (above the base payment category) are included in the current
model.
As a second strategy to ensure consistent and appropriate payment
levels, beneficiary diagnoses reported as a result of a short hospital
stay (1 day or less) were left in the base payment category. Since the
majority of 1-day stays are for diagnoses already assigned to the base
group, the effect on payment is small. Also, short stays are often
indicative of less serious, and, hence, less costly cases. It is
important to note that these modifications do not mean that these
expenditures have been excluded from the model. Rather, the payments
associated with these diseases are captured in increased payments for
the base payment category, where the majority of enrollees are paid
based on demographic factors.
Under the proposed PIP-DCG model, beneficiaries who are
hospitalized for chemotherapy (V58.1 and V66.2) were treated as
exceptions. These codes are indicators of a treatment method, rather
than a particular disease. Recognizing, however, that Medicare's
current inpatient coding rules require that the diagnoses for
beneficiaries who are hospitalized for chemotherapy must be coded using
these V-codes as the principal diagnoses, the most appropriate PIP-DCG
group for these beneficiaries would be assigned based on the type of
cancer, using a secondary diagnosis. A model will be estimated that
uses secondary diagnoses to determine risk scores for hospitalized
beneficiaries that were assigned chemotherapy V-codes (as defined
above). This modification could be made for payment in calendar year
2000. The model described in this notice has left these admissions in
the base group.
3. Addition of Demographic and Other Factors
The next phase in the estimation of the model was the creation of
demographic variables (age, sex, and disability status) for the PIP-DCG
groups. In this phase of the calibration, 24 age and sex groupings were
created. Separate groupings were created for males and females, by 5-
year age increments, except where numbers were too small to get good
estimates (that is, age group 0 through 34 and greater than 94 for
males and females).
Separate parameters were also included to estimate the unique cost
effects of whether an aged beneficiary was formerly eligible because of
a disability, and whether an aged or disabled beneficiary is eligible
for Medicaid. The estimated adjustments for the demographic categories
are the same irrespective of which PIP-DCG an enrollee falls into. The
Medicaid adjustment, however, depends on a person's status as aged or
disabled.
New enrollees to Medicare, for whom there are no claims history,
will be assigned a score based on a separate HCFA analysis of actual
new enrollee expenditures. At this time, a separate parameter is not
anticipated for the institutionalized because institutional status is
not needed as an indicator of high Medicare utilization. Under the
demographically adjusted system, institutional status was an indicator
of a beneficiary with relatively poor health status. It, therefore,
increased payments over the age and sex based amounts. The risk
adjuster model has health status measures built in, and on the average,
compensates for poor health status. In fact, preliminary estimates
indicate that after accounting for inpatient hospital admissions, the
institutional adjustment would be negative. Adjustments for the
working-aged will be made in a manner similar to the current system. As
a last step during the estimation, expenditures were adjusted to create
an estimate of annual payments as if each beneficiary had been alive
and enrolled for the entire year. This is equivalent to an expenditure
per month measure. Estimation of the incremental costs associated with
each of the variables (for example, demographics, DCGs) was made by the
linear regression technique, which takes account of all the variables
that apply to an individual.
4. The Current PIP-DCG Model
The current PIP-DCG model contains a total of 37 parameters (10
PIP-DCGs and 27 demographic or Medicaid factors). The model will
continue to be refined over the next few months. While there are a
number of ways to assess the ``accuracy'' of the model, payment for
different groups of beneficiaries is improved with risk adjustment
compared to the application of a demographic only model. Preliminary
coefficients for the PIP-DCG model are presented in Table 1. The
current placements of DxGroups into PIP-DCG groups are shown in Table
2. The next section of this notice details how we are proposing to use
the PIP-DCG model in the Medicare+Choice payment system as of January
1, 2000.
[[Page 47509]]
B. Proposed Payment System Application of the PIP-DCG Model
In its basic form, the PIP-DCG model is an algorithm that uses base
year inpatient diagnoses, along with demographic factors and Medicaid
eligibility, to predict total health spending in the following year. In
applying the PIP-DCG model to risk adjusted payments for the
Medicare+Choice program, however, the model will be used to determine
relative risk scores. These relative risk scores will be used, in place
of the current demographic factors, to adjust county rate book payments
for the relative health status of the individual enrollee.
1. Estimating Beneficiary Relative Risk Factors
The PIP-DCG model was developed to be ``additive'', meaning that
incremental dollars are added together based on each beneficiary's
characteristics. Referring to Table 3, the following examples
illustrate how the PIP-DCG model will be used for estimating relative
risk factors.
A beneficiary is placed in a PIP-DCG group, based on inpatient
diagnoses reported. In this example, ``Beneficiary A'' was hospitalized
twice during the base year. The diagnoses reported were Asthma (PIP-DCG
8) and Lung Cancer (PIP-DCG 18). The highest PIP-DCG category then for
this beneficiary is PIP-DCG 18, which carries with it an estimated
future year expenditure of $12,883. The beneficiary is also placed in
the appropriate demographic groups. In this case, Beneficiary A is
male, aged 82. This age group carries an estimated expenditure of
$5,617. In addition, Beneficiary A had originally been Medicare
eligible because of a disability (which carries an incremental
expenditure of $2,381), but is not eligible for Medicaid (no
expenditure increment). Adding together these increments based on the
PIP-DCG model, the predicted expenditures for this beneficiary are
$20,881.
As another example, consider ``Beneficiary B.'' Beneficiary B had
no inpatient admissions during the base year. Therefore, no specific
PIP-DCG increment is added; expenditures for non-hospitalized
beneficiaries are included in the demographic factors. Beneficiary B is
placed in the appropriate age and sex grouping; in this case, female
aged 72, which carries a predicted expenditure of $3,118. Beneficiary B
is also placed in the Aged with Medicaid eligibility group, which adds
$2,124 to her annual predicted expenditures. Since she has never been
disabled, no additional expenditures are added. Therefore, total annual
predicted expenditures for Beneficiary B are $5,242.
Because Medicare+Choice program payments are based on the county-
wide rates determined under section 1853(c) of the Act, the predicted
annual expenditures described above will be converted to relative risk
scores. This is accomplished by dividing the predicted expenditures for
each beneficiary by the national average predicted expenditure
($5,300). Individuals whose risk scores are equal to 1.00 are
``average.'' In the examples described above, Beneficiary A's relative
risk score is 3.9 (indicating a high expected cost individual), while
Beneficiary B's relative risk score is 0.99 (indicating a slightly
lower than average risk individual).
After Medicare+Choice organizations submit inpatient hospital
encounter data, we will use the demographic information and diagnostic
information from all Medicare+Choice organizations a beneficiary may
have joined and from FFS to determine the appropriate risk factor for
each beneficiary. When a Medicare+Choice organization forwards
enrollment information to us, we, in turn, will send the
Medicare+Choice organization the appropriate risk factor, as well as
the resultant payment. Because the risk factor is computed for each
individual beneficiary, the factor follows that beneficiary. In
addition, since all beneficiaries will have risk factors, information
will be immediately available for payment purposes as beneficiaries
move among Medicare+Choice organizations.
Risk adjustment factors for new Medicare beneficiaries (for whom
health status information) is not available will be based on
demographic information only. Examples of persons using the demographic
model are new 65-year-olds and new Medicare disabled individuals.
Similar to the current system, a ``demographic only'' model is being
developed that will be used to determine the risk adjustment factors
for these beneficiaries.
2. Risk Adjusted Payment Model
To determine risk adjusted monthly payment amounts for each
Medicare+Choice enrollee, individual risk factors (described above)
will be multiplied by the appropriate payment rate for the county
determined under section 1853(c) of the Act. Beginning with the
implementation of risk adjustment, the separate aged and disabled rate
books (incorporating combined Medicare Parts A and B) will be combined.
Risk adjusted payments will be made using a single, combined
Medicare+Choice county rate book. This change will be made because
there is a single risk adjustment methodology for the entire Medicare
population (excluding persons with end-stage renal disease).
In addition to combining the current aged and disabled county rate
books into a single combined county rate book, an adjustment to these
rate book amounts will be required before applying the risk adjustment
factors discussed above. This adjustment, or re-scaling factor, is
necessary in order to account for the fact that the existing county
rate book already accounts for demographic factors that are addressed,
in a more precise way, in the risk adjustment factors we will be using.
If the PIP-DCG model risk adjustment factors were applied to unadjusted
county rate book amounts, this would create unintended distortions that
would produce adjustments inconsistent with Congress' mandate in
section 1853(c) of the Act. The application of the rescaling factor we
are proposing would in effect translate the rate book amounts into the
same language used under the risk adjustment methodology, so that we
are not comparing ``apples to oranges.'' As a result of rescaling,
payment for a person with the average risk score in a county would be
the same as payment for a person with the average demographic score in
that county. (However, a person with the average demographic score does
not necessarily have the average risk score.) To the extent that an
organization enrolls sicker people, the organization will receive
higher payments.
C. Summary of HCFA's Proposed Approach for 2000
The proposed approach we will use to meet the year 2000 mandate for
risk adjusted payments will--
(1) Be based on inpatient data;
(2) Utilize a prospective PIP-DCG risk adjuster to estimate
relative beneficiary risk scores;
(3) Apply a re-scaling factor to address inconsistencies between
demographic factors in the rate book and new risk adjusters;
(4) Apply individual enrollee risk scores in determining fully
capitated payments;
(5) Include the auditing of medical records to validate encounter
data;
(6) Implement processes to collect encounter data on additional
services; and
(7) Continue to refine the risk adjustment system based on ongoing
research.
[[Page 47510]]
D. Other Issues
In addition to comments on the proposed risk adjustment approach,
we are interested in receiving responses to the following questions:
(1) Under one possible implementation approach we have considered, a
Medicare+Choice organization would be paid initially based on estimates
of the number of enrollees the organization has in a given risk factor
category. These estimates would be based on the most recently available
data (probably July 1998 through June 1999). Once more current data
(from January 1999 through December 1999) became available in July
2000, a retroactive adjustment would be made pursuant to section
1853(a)(2) of the Act ``to take into account any difference between the
actual number of individuals enrolled'' in a given risk category, and
the ``number of such individuals estimated to be so enrolled when the
advance payment was determined.'' These adjustments would be made
retroactive to January 2000. This would be consistent with our
longstanding practice of making retroactive adjustments to reflect the
actual number of enrollees in a current demographic category (such as
institutional status, end-stage renal disease status, dual eligible
status, or working aged status) when this number differs from the
number of enrollees estimated to be in any such category at the time
payments were initially made.
An alternative approach is to use data from an earlier period (for
example, July 1, 1998 through June 30, 1999) to determine the risk
factor for enrollees and payments to Medicare+Choice organizations for
calendar year 2000. Using data from an earlier time period introduces
some error into the estimates, but we do not believe it introduces any
systematic bias. Note that implementation of this alternative model
solves the problem of basing the payments to a plan on the estimated
number of enrollees in a given risk factor category, which would
require a retroactive adjustment as described above. Assuming a
relatively large and stable population for a plan, aggregate payments
under this approach are not likely to differ from aggregate payments
using a method requiring this type of retroactive payment adjustment.
However, on an individual basis, using data from an earlier time period
lengthens the time between a hospital stay for an enrollee and
compensation to the organization for the future predicted cost of that
illness.
Given these issues, what problems are Medicare+Choice organizations
likely to encounter with retroactive payment adjustments? Conversely,
if data from an earlier time period were used, what problems are
organizations likely to encounter?
(2) The Secretary is required to announce the annual
Medicare+Choice capitation rate for each Medicare+Choice payment area
and the risk and other factors to be used in adjusting such rates by
March 1 of the year preceding the payment year. In addition, at least
45 days prior to the annual announcement of capitation rates, the
Secretary shall provide notice to Medicare+Choice organizations of
proposed changes to be made in the methodology from the methodology and
assumptions used in the previous announcement.
The implementation of risk adjustment will alter the methodology
for calculating rates for each Medicare+Choice payment area. Given the
proposed changes, what types of information should be included in the
45-day notice and the annual announcement to assist Medicare+Choice
organizations in planning for risk adjusted payments?
(3) What types of problems are Medicare+Choice organizations likely
to encounter as capitation payments are changed from a demographic only
basis to a health status adjusted basis? How should we address these
problems, especially for small plans, rural plans, and start up plans?
While we are currently processing the inpatient hospital data for
managed care enrollees, we note that we will be unable to model the
financial impact of the risk adjustment methodology until we have
completed the processing of these data and have assigned risk scores to
plans enrollees.
II. September 17, 1998, Public Meeting
In addition to seeking written comments from the public, we will
hold a public meeting on September 17, 1998, at HCFA, 7500 Security
Boulevard, Baltimore, MD. The purpose of this meeting will be to
discuss issues and concerns from potential Medicare+Choice
organizations, organizations contracting under section 1876 of the Act,
providers, beneficiaries, and other interested parties on the
implementation of risk adjusted payment. The collection and auditing of
encounter data, which was described in the Medicare+Choice interim
final rule published on June 26, 1998, in the Federal Register, will
also be addressed in this meeting. The agenda for the meeting is likely
to cover the following topics:
Background on the Principal Inpatient Diagnostic Cost
Group (PIP-DCG) risk adjustment model.
Changes to the payment rates.
Application of the risk adjustment model for payment in CY
2000.
Description of the overall risk adjustment implementation
process.
Auditing of encounter data.
Collection of additional encounter data.
Comments on the proposed agenda are welcome. Further information on
the meeting can be obtained from Chapin Wilson, (202) 690-7874.
In accordance with E.O. 12866, this notice was reviewed by the
Office of Management and Budget.
Table 1.--Current PIP-DCG Model
------------------------------------------------------------------------
Number of Observations.................................. 1,401,274
R-Squared............................................... 0.058718
Dependent Variable Mean................................. $5,300
Root Mean Square Error.................................. 14,256
Model Parameters........................................ 37
------------------------------------------------------------------------
Base Payment Categories Payment
Increment
------------------------------------------------------------------------
Male: Aged 0-34......................................... 1,255
Male: 35-44............................................. 1,940
Male: 45-54............................................. 2,654
Male: 55-59............................................. 3,350
Male: 60-64............................................. 3,970
Male: 65-69............................................. 2,792
[[Page 47511]]
Male: 70-74............................................. 3,702
Male: 75-79............................................. 4,738
Male: 80-84............................................. 5,617
Male: 85-89............................................. 6,562
Male: 90-94............................................. 7,209
Male: 95+............................................... 7,189
Female: 0-34............................................ 1,345
Female: 35-44........................................... 2,167
Female: 45-54........................................... 2,763
Female: 55-59........................................... 3,647
Female: 60-64........................................... 4,673
Female: 65-69........................................... 2,439
Female: 70-74........................................... 3,118
Female: 75-79........................................... 3,994
Female: 80-84........................................... 4,768
Female: 85-89........................................... 5,592
Female: 90-94........................................... 5,855
Female: 95+............................................. 5,466
------------------------------------------------------------------------
Other Demographic Factors
------------------------------------------------------------------------
Previously Disabled..................................... 2,381
Medicaid, Medicare Aged................................. 2,124
Medicaid, Medicare Disabled............................. 1,744
------------------------------------------------------------------------
PIP-DCGs
PIP-DCG 6............................................... 2,265
PIP-DCG 8............................................... 4,406
PIP-DCG 10.............................................. 5,829
PIP-DCG 12.............................................. 7,950
PIP-DCG 14.............................................. 9,946
PIP-DCG 18.............................................. 12,883
PIP-DCG 20.............................................. 16,346
PIP-DCG 23.............................................. 18,950
PIP-DCG 26.............................................. 21,881
PIP-DCG 29.............................................. 29,317
------------------------------------------------------------------------
Notes: PIP-DCG 4 is combined with the demographic factors, and includes
those with no hospitalizations, modified or certain low-cost
admissions. Diagnoses from hospital stays of less than two days are
not used in assigning PIP-DCGS.
Table 2.--Diagnoses (DxGroups) Included in Each PIP-DCG--Current Payment Model
----------------------------------------------------------------------------------------------------------------
PIP-DCG 6:
DxGroup.............................. 18 Cancer of Prostate/Testis/Male Genital Organs.
14 Breast Cancer.
PIP-DCG 8:
DxGroup.............................. 82 Acute Myocardial Infarction.
146 Pelvic Fracture.
145 Fractures of Skull/Face.
77 Valvular and Rheumatic Heart Disease.
86 Atrial Arrhythmia.
84 Angina Pectoris.
80 Coronary Atherosclerosis.
92 Precerebral Arterial Occlusion.
16 Cancer of Uterus/Cervix/Female Genital Organs.
79 Hypertension, Complicated.
36 Peptic Ulcer.
110 Asthma.
96 Aortic and Other Arterial Aneurysm.
153 Brain Injury.
1 Central Nervous System Infections.
39 Abdominal Hernia, Complicated.
64 Alcohol/Drug Dependence.
PIP-DCG 10:
DxGroup.............................. 109 Bacterial Pneumonia.
42 Gastrointestinal Obstruction/Perforation.
143 Vertebral Fracture Without Spinal Cord Injury.
21 Other Cancers.
4 Tuberculosis.
97 Thromboembolic Vascular Disease.
59 Schizophrenic Disorders.
[[Page 47512]]
11 Colon Cancer.
116 Kidney Infection.
83 Unstable Angina.
94 Transient Cerebral Ischemia.
81 Post-Myocardia Infarction.
150 Internal Injuries/Traumatic Amputations/Third Degree
Burns.
32 Pancreatitis/Other Pancreatic Disorders.
147 Hip Fracture.
158 Artificial Opening of Gastrointestinal Tract Status.
PIP-DCG 12:
DxGroup.............................. 91 Cerebral Hemorrhage.
93 Stroke.
56 Dementia.
98 Peripheral Vascular Disease.
41 Inflammatory Bowel Disease.
22 Benign Brain/Nervous System Neoplasm.
48 Rheumatoid Arthritis and Connective Tissue Disease.
49 Bone/Joint Infections/Necrosis.
19 Cancer of Bladder, Kidney, Urinary Organs.
45 Gastrointestinal Hemorrhage.
87 Paroxysmal Ventricular Tachycardia.
133 Cellulitis and Bullous Skin Disorders.
57 Drug/Alcohol Psychoses.
PIP-DCG 14:
DxGroup.............................. 66 Personality Disorders.
29 Adrenal Gland, Metabolic Disorders.
70 Degenerative Neurologic Disorders.
2 Septicemia/Shock.
144 Spinal Cord Injury.
58 Delirium/Hallucinations.
61 Paranoia and Other Psychoses.
63 Anxiety Disorders.
73 Epilepsy and Other Seizure Disorders.
10 Stomach, Small Bowel, Other Digestive Cancer.
12 Rectal Cancer.
26 Diabetes with Acute Complications/Hypoglycemic Coma.
113 Pleural Effusion/Pneumothorax/Empyema.
60 Major Depression.
PIP-DCG 18:
DxGroup.............................. 34 Cirrhosis, Other Liver Disorders.
72 Paralytic and Other Neurologic Disorders.
108 Gram-Negative/Staphylococcus Pneumonia.
111 Pulmonary Fibrosis and Bronchiectasis.
89 Congestive Heart Failure.
105 Chronic Obstructive Pulmonary Disease.
95 Atherosclerosis of Major Vessel.
13 Lung Cancer.
8 Mouth/Pharynx/Larynx/Other Respiratory Cancer.
PIP-DCG 20:
DxGroup.............................. 112 Aspiration Pneumonia.
76 Coma and Encephalopathy.
75 Polyneuropathy.
17 Cancer of Placenta/Ovary/Uterine Adnexa.
55 Blood/Immune Disorders.
PIP-DCG 23:
DxGroup.............................. 134 Decubitus and Chronic Skin Ulcers.
33 End-stage Liver Disorders.
9 Liver/Pancreas/Esophagus Cancer.
88 Cardio-Respiratory Failure and Shock.
27 Diabetes with Chronic Complications.
115 Renal Failure/Nephritis.
PIP-DCG 26:
DxGroup.............................. 7 Metastatic Cancer.
PIP-DCG 29:
DxGroup.............................. 3 HIV/AIDS.
15 Blood, Lymphatic Cancers/Neoplasms.
20 Brain/Nervous System Cancers.
----------------------------------------------------------------------------------------------------------------
[[Page 47513]]
Table 3.--Estimating Prospective Beneficiary Expenditures Mean Predicted Expenditures = $5300
--------------------------------------------------------------------------------------------------------------------------------------------------------
Demographic factors base PIP- + PIP-DCG + Other factors
--------------DCG---------------------------------------------------------------------------------------------------------------------------------------
Aged Population
--------------------------------------------------------------------------------------------------------------------------------------------------------
Male 65-69..................... $2792 PIP-DCG 6 $2265 Previously Disabled....................... $2381
Male 70-74..................... 3702 PIP-DCG 8 4406 Medicaid, Medicare Aged................... 2124
Male 75-79..................... 4738 PIP-DCG 10 5829
Male 80-84..................... 5617 PIP-DCG 12 7950
Male 85-89..................... 6562 PIP-DCG 14 9946
Male 90-94..................... 7209 PIP-DCG 18 12,883
Male 95+....................... 7189 PIP-DCG 20 16,346
Female 65-69................... 2439 PIP-DCG 23 18,950
Female 70-74................... 3118 PIP-DCG 26 21,881
Female 75-79................... 3944 PIP-DCG 29 29,317
Female 80-84................... 4768
Female 85-89................... 5592
Female 90-94................... 5855
Female 95+..................... 5466
--------------------------------------------------------------------------------------------------------------------------------------------------------
Disabled Population
--------------------------------------------------------------------------------------------------------------------------------------------------------
Male 0-34...................... 1255 PIP-DCG 6 2265 Medicaid, Medicare Disabled............... 1744
Male 34-44..................... 1940 PIP-DCG 8 4406
Male 45-54..................... 2654 PIP-DCG 10 5829
Male 55-59..................... 3350 PIP-DCG12 7950
Male 60-64..................... 3970 PIP-DCG 14 9946
Female 0-34.................... 1345 PIP-DCG 18 12,883
Female 34-44................... 2167 PIP-DCG 20 16,346
Female 45-54................... 2763 PIP-DCG 23 18,950
Female 55-59................... 3647 PIP-DCG 26 21,881
Female 60-64................... 4673 PIP-DCG 29 29,317
--------------------------------------------------------------------------------------------------------------------------------------------------------
(Sec. 4002 of the Balanced Budget Act of 1997 (Public Law 105-33)
Dated: August 26, 1998.
Nancy-Ann Min DeParle,
Administrator, Health Care Financing Administration.
Dated: September 1, 1998.
Donna E. Shalala,
Secretary.
[FR Doc. 98-24085 Filed 9-2-98; 4:10 pm]
BILLING CODE 4120-01-P
