Medicare: Changes to HMO Rate Setting Method Are Needed to Reduce Program
Costs (Chapter Report, 09/02/94, GAO/HEHS-94-119).
During the 1980s, the per capita costs of providing health care to the
elderly under Medicare increased 59 percent, even after adjusting for
inflation. To slow this cost spiral, Congress allowed Medicare to
contract with health maintenance organizations (HMO) under an
alternative payment system. Medicare's traditional fee-for-service
payment method created incentives for overuse of medical care because
providers could boost their incomes by encouraging greater use of
services. By contrast, HMOs receive an up-front fixed monthly fee for
each patient's care instead of a fee for each service. Government
researchers and outside analysts, however, have claimed that HMOs can be
more expensive than fee-for-service care. These analysts argue that
beneficiaries enrolled in Medicare HMOs are healthier (and less costly
to care for) than beneficiaries in the fee-for-service sector and that
Medicare payments to HMOs do not fully reflect these differences in
costs. In addition to this problem, industry representatives and other
analysts claim that Medicare payment rates are too low in some areas and
show unjustifiably wide variation across geographic boundaries. This
report examines Medicare's HMO rate setting methodology to determine the
existence and the magnitude of these problems and to review proposed
solutions. Specifically, GAO discusses the impact of favorable selection
and rate variation on the ability of the Medicare risk contract program
to yield cost savings.
--------------------------- Indexing Terms -----------------------------
REPORTNUM: HEHS-94-119
TITLE: Medicare: Changes to HMO Rate Setting Method Are Needed to
Reduce Program Costs
DATE: 09/02/94
SUBJECT: Medicare programs
Medical economic analysis
Medical services rates
Health maintenance organizations
Medical fees
Medical information systems
Health services administration
Payments
Health care cost control
Health insurance cost control
IDENTIFIER: Medicare Prospective Payment System
Medicare Resource-Based Relative Value Scale
Medicare Risk Contract Program
Medicare Health Care Prepayment Plan
Health Security Act
Clinton Health Care Plan
National Health Care Reform Initiative
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Cover
================================================================ COVER
Report to Congressional Committees
September 1994
MEDICARE - CHANGES TO HMO RATE
SETTING METHOD ARE NEEDED TO
REDUCE PROGRAM COSTS
GAO/HEHS-94-119
Medicare HMO Rate Setting Method Needs Change
Abbreviations
=============================================================== ABBREV
AAPCC - adjusted average per capita cost
ACG - Ambulatory Care Group
ACR - adjusted community rate
CCI - Clinical Complexity Index
DCG - Diagnostic Cost Group
ESRD - end-stage renal disease
FFS - fee-for-service
HCFA - Health Care Financing Administration
HHS - Department of Health and Human Services
HMO - health maintenance organization
IPA - independent practice association
OPHCOO - Office of Prepaid Health Care Operations and Oversight
PACS - Payment Amount for Capitated Systems
TEFRA - Tax Equity and Fiscal Responsibility Act of 1982
USPCC - United States per capita cost
Letter
=============================================================== LETTER
B-251657
September 2, 1994
The Honorable Samuel Gibbons
Acting Chairman
The Honorable William Archer
Ranking Minority Member
Committee on Ways and Means
House of Representatives
The Honorable John Dingell
Chairman
The Honorable Carlos J. Moorhead
Ranking Minority Member
Committee on Energy and Commerce
House of Representatives
The Honorable Daniel P. Moynihan
Chairman
The Honorable Robert Packwood
Ranking Minority Member
Committee on Finance
United States Senate
This report responds to provisions in the Omnibus Budget
Reconciliation Act of 1990 (P.L. 101-508) and the Omnibus Budget
Reconciliation Act of 1987 (P.L. 100-203) that require us to study
the payment system used by Medicare's risk contract program for
health maintenance organizations (HMO). We include recommendations
to the Administrator of the Health Care Financing Administration that
could ultimately make the program more cost-effective.
We are sending copies of the report to the Secretary of Health and
Human Services, the Administrator of the Health Care Financing
Administration, and other interested committees and parties. We also
will make copies available to others on request.
This report was prepared under the direction of Jonathan Ratner,
Assistant Director, and Scott Smith, Assistant Director, Health
Financing and Policy Issues. If you or your staff have any questions
about this report, please call Scott Smith on (202) 512-7119. Major
contributors to this report are listed in appendix VI.
Sarah F. Jaggar
Director, Health Financing
and Policy Issues
EXECUTIVE SUMMARY
============================================================ Chapter 0
PURPOSE
---------------------------------------------------------- Chapter 0:1
During the 1980s, the per capita costs of providing health care to
the elderly under the government's Medicare program increased 59
percent, even after adjusting for inflation. Searching for ways to
reduce this cost spiral, the Congress has allowed Medicare to
contract with health maintenance organizations (HMO) under an
alternative payment system. Under the Medicare program,
beneficiaries have traditionally been served by individual physicians
and other providers, who were reimbursed for each allowable service.
However, this fee-for-service payment method can create incentives
for overuse of medical care, because providers could increase their
incomes by encouraging greater use of services. By contrast, HMOs
receive an up-front fixed monthly fee (sometimes called a capitated
rate) for each beneficiary's care, instead of a fee for each service.
Because payments are not related to service use, this payment method
does not encourage overuse of health care services and could create
cost savings in the Medicare program.
Although the Congress anticipated that HMOs would save money for the
Medicare program, government researchers and outside analysts have
claimed that providing services to Medicare beneficiaries through
HMOs can be more expensive than fee-for-service care. According to
these analysts, beneficiaries enrolled in Medicare HMOs are healthier
(and less costly to care for) than beneficiaries in the
fee-for-service sector, and Medicare's payments to HMOs do not fully
reflect these differences in costs. In addition to this problem,
industry representatives and other analysts have charged that
Medicare's payment rates are too low in some areas and exhibit
unjustifiably wide variation across geographic boundaries.
The Congress asked GAO to examine Medicare's HMO rate setting
methodology to assess the existence and magnitude of these problems
and to review proposed solutions. Specifically, this study reviews
the impact of favorable selection and rate variation on the ability
of the Medicare risk contract program to provide cost savings.
Issues concerning appropriate rate setting and risk adjustment are
also central to current efforts to reform the nation's health care
system. The Medicare risk contract program shares several features
with those systems proposed under health care reform--for example,
the use of capitated payments and the need for risk adjustment.
Nonetheless, these proposals differ from the Medicare risk contract
program in several important respects--most notably, health care
reform would include people under 65, who have very different health
care needs from the Medicare population. GAO will discuss these and
other differences and examine their consequences in a forthcoming
report.
BACKGROUND
---------------------------------------------------------- Chapter 0:2
Hoping to take advantage of the potential cost savings associated
with HMOs, in 1982 the Congress created the Medicare risk contract
program. Under this program, HMOs are paid a flat fee for each
Medicare beneficiary enrolled. The law sets this fee at 95 percent
of the estimated average cost to Medicare of treating the patient in
the fee-for-service sector. The Health Care Financing Administration
(HCFA), which oversees the Medicare program, calculates these payment
rates by following a three-step process:
First, HCFA calculates the projected Medicare expenses for the
average beneficiary in the next year--the base rate.
Second, HCFA adjusts the base rate for variations in medical costs
among counties--the geographic adjustment.\1 The result,
multiplied by 0.95, is called the adjusted average per capita
cost (AAPCC).
The final step is the risk adjustment, when HCFA adjusts the AAPCC
for enrollees' demographic characteristics--age, sex, Medicaid
eligibility, and whether or not the enrollee is in an
institution such as a nursing home--to arrive at a capitation
rate for each HMO.
Payment rates are subjected to this risk adjustment in an attempt to
prevent risk contract HMOs from benefiting from favorable
selection.\2 Favorable selection occurs when HMO enrollees are
healthier, and therefore less costly to care for, than enrollees in
the fee-for-service sector. By adjusting payments for demographic
characteristics, HCFA tries to set payment rates that reflect
differences in the cost of treating HMO enrollees versus
fee-for-service beneficiaries.
The risk contract option remains a relatively small part of the
Medicare program. Although approximately 100 HMOs participated in
the program in 1993, the Medicare risk contract program treats only
about 5 percent of Medicare beneficiaries, accounting for an
estimated $7.2 billion in budget outlays for fiscal year 1993. Most
of these beneficiaries live in a few major markets in large cities
such as Los Angeles, Miami, and Minneapolis.
--------------------
\1 At this point, HCFA adjusts the expected Medicare costs to a
fee-for-service basis by subtracting the enrollment and reimbursement
for Medicare HMO enrollees.
\2 Administrative means, as well as risk adjustment, are in place to
reduce favorable selection. For example, risk contract HMOs are
required to accept all Medicare beneficiaries who wish to enroll,
except hospice patients and beneficiaries with end-stage renal
disease.
RESULTS IN BRIEF
---------------------------------------------------------- Chapter 0:3
The Medicare risk contract program has not achieved its goal of
reducing Medicare costs, because HCFA's rate setting methodology and
administrative controls have proven insufficient to prevent HMOs from
benefiting from favorable selection. Numerous independent and
HCFA-sponsored research studies have demonstrated that HMO enrollees
tend to be healthier than beneficiaries who remain in the
fee-for-service sector. Because the healthier HMO enrollees are more
than 5 percent cheaper to care for than comparable fee-for-service
beneficiaries, HCFA paid HMOs more for beneficiaries' treatment than
it would have spent had those same beneficiaries remained in the
fee-for-service sector. In light of these research findings, HCFA's
administrator has recognized the need for change in the current rate
setting methodology.
Responding to the problem of favorable selection, researchers have
proposed a number of alternative risk adjustment methods. Each of
these alternative methods--unlike HCFA's current system--measures the
health status of enrollees. Although none of these proposals has
emerged as the definitive alternative to HCFA's methodology, any one
of several available proposals would probably improve the current
system. Of the 10 alternative risk adjustors GAO reviewed, 4 are
most likely to reduce favorable selection and allow Medicare to
achieve cost savings under the risk contract program.
The Medicare risk contract program faces difficulties not only with
risk adjustment, but also with constructing the base payment rate to
which these risk adjustments apply. Payment rates to HMOs vary
substantially--and unjustifiably--across the country. This rate
variability stems not from the risk adjustment process, but from the
statutory linkage between local fee-for-service expenditures and HMO
payment rates. Local expenditures reflect variations in both the
price of medical services and the utilization of services by the
local Medicare population. If local fee-for-service prices and/or
utilization rates are inappropriately high, then local HMO payment
rates will also be excessive.
The wide variation in HMO payment rates is reflected in uneven
participation in the Medicare risk contract program. In some areas
of the country, generous payment rates have induced many HMOs to
enter the market. In other areas, however, lower payment rates have
discouraged HMOs from participating in the Medicare program, thereby
limiting Medicare beneficiaries' access to an HMO option. Although
researchers and policy analysts have suggested several alternative
rate setting methods, evidence is insufficient to assess the impact
of any of these proposals on Medicare costs and on HMO participation
in the risk contract program.
PRINCIPAL FINDINGS
---------------------------------------------------------- Chapter 0:4
HMOS EXPERIENCE FAVORABLE
SELECTION UNDER HCFA'S
CURRENT RATE SETTING METHOD
-------------------------------------------------------- Chapter 0:4.1
Under HCFA's current rate setting method, HMOs have a strong
financial incentive to attract the healthiest possible Medicare
clientele. When a relatively healthy Medicare patient joins an HMO,
the HMO will provide less treatment than for the average patient, but
HCFA's capitated payment for that person will not fully reflect the
lower expected costs. In addition, as more healthy beneficiaries
join HMOs, the Medicare fee-for-service population on average becomes
sicker, driving up Medicare's average cost of treating
fee-for-service patients. When this average cost rises, so does the
capitation rate HCFA pays to risk contract HMOs.
Favorable selection could come about in two ways: (1) if Medicare
beneficiaries enrolling in HMOs are healthier than those remaining in
the fee-for-service sector and (2) if beneficiaries leave risk
contract HMOs and return to fee-for-service medicine when they become
ill. These enrollment and disenrollment patterns, which are at the
root of favorable selection, can arise either through the actions of
an HMO or the actions of a patient. So long as HCFA's capitation
rate does not fully reflect the cost differences of treating
healthier rather than sicker populations, HMOs can benefit from
favorable selection.
Extensive academic research has found that risk contract HMOs do
benefit from favorable selection. For example, a HCFA-contracted
study of favorable selection in the risk contract program\3 found
that 54 to 63 percent of Medicare HMOs in 1990 experienced favorable
selection, while the rest experienced neutral selection; no HMO
experienced adverse selection.\4 Overall, researchers estimate that
HCFA's payments to risk contract HMOs were from approximately 6 to 28
percent higher than the costs of treating those same patients in the
Medicare fee-for-service sector.\5
These cost increases to HCFA do not necessarily correspond to
increased profits for risk contract HMOs. Although favorable
selection contributes to HMO profits, it does not guarantee that
participating HMOs will be profitable. Most HMOs in the risk
contract program are profitable, but fewer HMOs are participating in
the program than in 1987. A number of factors contribute to HMOs'
profitability from the risk contract program, and favorable selection
is only one of these. (For example, risk contract HMOs must incur
marketing and administrative costs to participate in the program.)
Therefore, losses to both the HMOs and HCFA can occur simultaneously.
--------------------
\3 See Mathematica Policy Research, Inc., Biased Selection in the
Medicare Risk Contract Program (Sept. 21, 1990).
\4 Neutral selection implies that the HMO's Medicare beneficiaries
were, on average, no sicker or healthier than Medicare beneficiaries
in the fee-for-service sector. Adverse selection would occur if an
HMO's enrollees were, on average, more costly to treat than the
average comparable enrollee in the fee-for-service sector.
\5 For a summary of this research, see Mathematica Policy Research,
Inc., The Impact of the Medicare Risk Contract Program on the Use of
Services and Costs to Medicare (Dec. 3, 1992).
NO RISK ADJUSTOR IS BEST,
BUT SEVERAL COULD IMPROVE
THE CURRENT SYSTEM
-------------------------------------------------------- Chapter 0:4.2
In response to the prevalence of favorable selection in the Medicare
risk contract program, researchers and industry experts have urged
HCFA to include a measure of health status, along with demographic
factors, in its risk adjustment methodology.\6 Analysts have examined
a number of health status measures, each designed to reduce HMOs'
incentives to enroll only relatively healthy Medicare beneficiaries.
These proposals can be judged according to a number of generally
accepted operational criteria. For example, a good risk adjustor
would be inexpensive to administer, would reduce favorable selection,
would create incentives for HMOs to provide appropriate care, and
would not be subject to manipulation by participating HMOs. However,
no risk adjustor is likely to exhibit all these positive traits
because there are trade-offs among these criteria. For example, a
more complex risk adjustor may be more successful in reducing
favorable selection, but may do so only at a high administrative
cost.
We used these criteria to evaluate competing risk adjustment
solutions. However, no one risk adjustment method has emerged as the
definitive alternative to the current system. Because research
evidence is incomplete, the qualitative differences among adjustors
can be determined, but the magnitude of those differences cannot be
measured precisely.
Despite these difficulties, four of the ten adjustors GAO examined
were clearly superior to the others, as well as to the current
system. One of these adjustors--clinical indicators--would adjust
capitation rates for the presence or absence of a particular chronic
health condition (such as heart disease, stroke, or cancer). Two
other promising clinically based risk adjustors include information
not only on whether a beneficiary has a specific condition but also
on the severity of the illness. In the fourth approach, HMO
capitation payments would be linked to beneficiaries' own views of
their physical and emotional health.
--------------------
\6 Researchers' recommendations to HCFA to include a health status
adjustor date as far back as 1982.
IMPROVEMENTS IN RISK
ADJUSTMENT WILL BE
INSUFFICIENT TO REMEDY
PROBLEMS WITH CURRENT
PAYMENT SYSTEM
-------------------------------------------------------- Chapter 0:4.3
Because risk adjustment does not affect the base rate, improving the
risk adjustment methodology will not correct the problems associated
with what many industry experts believe are unjustifiably wide
variations in HMO payment rates. Because these base payment rates
are constructed from Medicare fee-for-service expenditures, HMO
capitation rates reflect both access problems in some geographic
areas and inefficient medical practice patterns in others. For
example, in a rural county where Medicare beneficiaries have poor
access to care, their low utilization will be reflected in low HMO
base payment rates. Similarly, if Medicare fee-for-service
beneficiaries in another county tend to use more services, their high
utilization will increase HMO payment rates. As a result, payment
rates in some areas are too low to induce participation in the risk
contract program, but in other areas payment rates are too high for
Medicare to fully realize the potential cost savings generated by
capitated payments.
Recognizing these problems, researchers and HMO industry
representatives have proposed a number of alternatives for
determining base payment rates under the risk contract program. For
example, several analysts have suggested setting Medicare HMO payment
rates through competitive bidding, and others have supported changing
HCFA's rate setting formula to raise the lowest rates or reduce the
highest ones. However, research evidence is insufficient to
determine whether any of these proposals would improve the current
system.
RECOMMENDATIONS
---------------------------------------------------------- Chapter 0:5
Because current knowledge of risk adjustment is limited, no single
risk adjustment method has emerged as the best solution for the
Medicare risk contract program. However, researchers agree that
change is necessary if the program is to achieve Medicare cost
savings.
HCFA has sponsored substantial research documenting the extent of
favorable selection, and the agency has also supported research on
alternative risk adjustment methods. In view of the potential cost
savings from improved risk adjustment, GAO recommends that the
Administrator of HCFA
extend the agency's research and demonstration agenda to include
work on the four risk adjustors that GAO believes have the
greatest merit (see p. 33) and
conduct preliminary research on payment methodologies that could
replace the reliance on fee-for-service reimbursement to
determine base payment rates for HMOs (see p. 42).
AGENCY COMMENTS AND GAO
EVALUATION
---------------------------------------------------------- Chapter 0:6
HCFA provided written comments on a draft of our report. (See app.
IV.) In its overall comments, HCFA emphasized its continuing and
ongoing research on several risk adjustment approaches. HCFA also
stated that opportunities for risk adjustment demonstration projects
are limited by the voluntary nature of demonstrations. GAO agrees
that interpreting results can be more difficult when demonstrations
must be limited to voluntary participants, because the health plans
that are most willing to participate may differ from the HMOs that
are more reluctant. GAO recognizes this feature in its
recommendation that HCFA design demonstrations that encourage HMO
participation, especially by ensuring that HMOs do not suffer
financially by participating in a demonstration program. GAO
believes that well-designed demonstrations are necessary because they
provide the only mechanism for incorporating actual experience into
evaluations of new risk adjustment methods.
HCFA also pointed to provisions in the proposed Health Security Act
that are aimed at improving the Medicare risk contract program. GAO
agrees that these provisions might improve the risk contract program
but believes that the potential effectiveness of these measures
cannot yet be determined.
Finally, HCFA disagreed with GAO's decision to describe the cost
impact of favorable selection by using a range of research estimates,
rather than the most recent study. Although this study was carefully
researched, GAO believes that no single study provided a definitive
estimate. Reporting the range of research estimates conveys a
perspective on the uncertainty surrounding estimates of the cost
impact of favorable selection.
HCFA also provided technical comments, which were incorporated as
appropriate. Technical comments related to substantive matters are
presented in appendix IV, with GAO's evaluation.
INTRODUCTION
============================================================ Chapter 1
Over the past decade, Medicare has looked to health maintenance
organizations (HMOs) to provide cost savings compared to
fee-for-service care. In a traditional fee-for-service system, the
provider is paid for each service rendered to the patient--the more
services, the greater the payment received. This fee-for-service
payment method gives providers an incentive to provide more services
and thus to increase costs. By contrast, a capitated payment system,
like the one used in the Medicare risk contract program, creates
incentives for cost reduction. Under a capitated payment system, an
HMO is paid an up-front fee (sometimes called a capitation rate) for
each person enrolled in the HMO, regardless of the services that
patient uses. Because payment is made per person, not per service,
HMOs have an incentive to reduce treatment that is unnecessary or of
marginal benefit. Today, some policymakers and analysts view
Medicare's HMO alternative as promising; others, reviewing the
history of Medicare's experience with HMOs, view it as a
disappointment. The pivotal issue in this debate is whether or not
Medicare's risk contract program can save federal dollars by
providing Medicare benefits through HMOs, while ensuring that
Medicare beneficiaries receive quality care.
MEDICARE AND THE RISK CONTRACT
PROGRAM
---------------------------------------------------------- Chapter 1:1
Medicare is a federal program (authorized effective July 1, 1966, by
title XVIII of the Social Security Act) that assists most elderly
aged 65 or older and certain disabled people in paying for their
health care. The program is administered by the Health Care
Financing Administration (HCFA), under the Department of Health and
Human Services (HHS). It provides two basic forms of protection:
Part A, Hospital Insurance, is financed primarily by social
security payment taxes and covers inpatient hospital services,
post-hospital care in skilled nursing facilities, hospice care,
and care in patients' homes.
Part B, Supplemental Medical Insurance, is a voluntary program
financed by enrollee premiums (25 percent of total costs) and
federal general revenues. It covers physician services and a
variety of other health care services, such as laboratory and
outpatient hospital services.
THE HISTORY AND GOALS OF THE
MEDICARE RISK CONTRACT
PROGRAM
-------------------------------------------------------- Chapter 1:1.1
Congressional interest in the cost-saving potential of HMOs dates
from the Social Security Act Amendments of 1972 (P.L. 92-603). This
law authorized prepayments to HMOs that provide health care services
to Medicare beneficiaries. Under the 1972 law, if an HMO's costs
were less than its capitation payments, it was required to share
these profits with Medicare. In addition, an HMO's profits from this
program were capped at 10 percent of its total payment from HCFA. If
an HMO's costs exceeded its payments from Medicare, it had to absorb
the loss or carry it over to offset future profits from its Medicare
business. Because an HMO's profit potential was limited, while its
exposure to losses was unlimited, only a few HMOs contracted with
Medicare under this arrangement.
The Congress modified the Medicare reimbursement method in 1982,
creating the Medicare risk contract program.\7 For each HMO Medicare
patient, the law mandates that risk contract HMOs be paid a
capitation rate equal to 95 percent of the average cost of treating
the patient in the fee-for-service sector. HCFA estimates this
average cost of fee-for-service care and sets the HMO payment rate.
In addition, the Congress eliminated the 1972 law's requirement that
an HMO's Medicare profits be completely shared with HCFA. Instead,
HMOs were permitted to retain all profits up to the level earned on
their non-Medicare business--known as the adjusted community rate
(ACR).\8 Despite the increase in allowed HMO profits, the Congress
anticipated that this payment mechanism would result in a 5 percent
savings to Medicare for each HMO patient, because HMOs would be paid
95 percent--not 100 percent--of the average cost of treating the
patient in the fee-for-service sector.\9
While the primary goal of the risk contracting program is to reduce
Medicare expenditures, some current and former HCFA program officials
have suggested that HMOs can offer additional advantages to Medicare
beneficiaries. According to these officials, managed care may
improve the quality of patient care because one primary doctor
coordinates the provision of all services. In addition, some
Medicare program officials believe that Medicare beneficiaries should
have the choice of receiving care through either a fee-for-service
plan or an HMO--an option often available in the private sector.
HMOs may also provide Medicare beneficiaries with more benefits, and
copayments or deductibles lower than those offered by traditional
indemnity plans. Patients may be attracted by an HMO's coordination
of specialty and primary care and its reduction in claims paperwork.
The Medicare risk contract program now treats about 5 percent of
Medicare beneficiaries, accounting for an estimated $7.2 billion in
budget outlays for fiscal year 1993. Most of these beneficiaries
live in a few major markets in large cities such as San Francisco,
Los Angeles, Miami, and Minneapolis. One hundred four HMOs were
participating in the program as of August 1993, down from a high of
157 in 1987.\10 As of August 1993, 26 states had HMOs with Medicare
risk contracts.
The HMOs in the Medicare risk contract program differ in their
organizational structures. Staff-model HMOs, for example, hire
physicians directly, whereas group-model HMOs contract with one or
more large physician group practices. Other HMOs are formed from
individual practice associations (IPA),\11 or networks of independent
physicians that may contract with HMOs but may also serve non-HMO
patients covered by other insurance. Therefore, fee-for-service
Medicare beneficiaries who join a staff- or group-model HMO will
usually be required to select new providers, whereas Medicare
beneficiaries who join an IPA-model HMO may be able to continue with
their current providers.
--------------------
\7 These changes are contained in section 114 of the Tax Equity and
Fiscal Responsibility Act (TEFRA) (P.L. 97-248).
\8 If the HMO's estimated profit rate on its Medicare risk contract
exceeds the estimated profit on its non-Medicare business, the plan
must use the excess funds to provide added benefits or reduced
copayments or deductibles for enrolled Medicare beneficiaries, refund
the excess to HCFA, or contribute to a benefit stabilization fund.
\9 To gain experience with HMO risk-based reimbursement and other
aspects of Medicare contracting with HMOs, HCFA contracted with 34
HMOs to operate demonstration projects between 1980 and 1984. In May
1984 HHS published proposed regulations to implement the TEFRA HMO
amendments, and in January 1985, HHS issued the final implementing
regulations, which became effective on February 1, 1985.
\10 Some of this decline in the number of participating HMOs may have
been associated with corporate mergers in the HMO industry.
\11 IPAs may also be called independent practice associations.
HCFA OFFERS THREE OTHER
TYPES OF CONTRACTS TO
MEDICARE HMOS
-------------------------------------------------------- Chapter 1:1.2
The risk contract program is HCFA's largest HMO program, accounting
for 67 percent of those Medicare beneficiaries enrolled in HMOs.\12
In addition to risk contracting, however, HMOs can also serve
Medicare patients through three other contracting arrangements, each
of which puts the HMO at less financial risk for Medicare
beneficiaries' care.
Under a cost contract, HCFA pays HMOs for the actual service and
administrative costs of caring for Medicare beneficiaries in the
plan. However, under these cost contracts, unlike risk
contracts, beneficiaries are free to seek care outside of the
plan at Medicare's expense.
Health Care Prepayment Plans pay HMOs on a cost basis for physician
and other outpatient services only.
A few so-called "social HMOs" provide integrated health and
long-term care services on a prepaid capitated basis. The
capitation rate is the sum of 100 percent of average
fee-for-service costs plus monies from Medicaid.\13
--------------------
\12 As of March 1993, approximately 54 percent of all Medicare HMOs
were risk contractors, 13 percent were cost contractors, and 33
percent were Health Care Prepayment plans.
\13 Medicaid is a government program that provides health care
(including long-term care) to persons with low income.
ENROLLMENT IN THE RISK
CONTRACT PROGRAM
-------------------------------------------------------- Chapter 1:1.3
Medicare beneficiaries can join an HMO with a Medicare risk contract
only if they are enrolled in Part B of Medicare,\14 if they live in
the HMO's service area, and if the HMO is accepting new members.
With the exception of patients with end-stage renal disease (ESRD)
and hospice patients,\15 risk contract HMOs may not refuse enrollment
to a Medicare beneficiary because of a medical condition. Risk
contract HMOs are required to hold at least one 30-consecutive-day
open enrollment period each year to enroll additional Medicare
members.\16 Medicare beneficiaries may disenroll from a risk-contract
HMO at any time by submitting a signed and dated request for
disenrollment to the HMO or to a Social Security office.
To participate in the Medicare risk contract program, HCFA requires
HMOs to meet federal qualification requirements or meet another, less
stringent list of federal standards. Among other requirements, HMOs
must be fiscally sound, have a minimum of 5,000 members (Medicare and
non-Medicare combined),\17 and participate in a quality assurance
program. In addition to these requirements, HCFA reviews risk
contract HMOs' Medicare-related marketing material to ensure that it
is not misleading.
Several offices within HCFA have responsibility for the Medicare risk
contract program. Within HCFA's Office of Managed Care, the Office
of Coordinated Care Policy and Planning develops national policies
and objectives for the development, qualification, and ongoing
compliance of HMOs, and develops and implements programs to encourage
greater access of federal Medicare beneficiaries to HMOs. Another
division of the Office of Managed Care, the Office of Prepaid Health
Care Operations and Oversight (OPHCOO), determines which HMOs meet
the standards for certification as federally qualified HMOs and
provides operational policy direction for the program. In addition,
working with HCFA's 10 regional offices, OPHCOO administers Medicare
risk, cost, and Health Care Prepayment Plan contracts. The regional
offices also (1) review any HMO marketing materials that were not
reviewed by OPHCOO at the time an HMO submitted its contract; (2)
monitor enrollment and disenrollment; (3) conduct on-site performance
reviews; and (4) provide technical assistance to participating health
plans.
HCFA's Office of the Actuary calculates the AAPCC rates annually.
The Bureau of Data Management and Strategy develops, implements, and
maintains the computer software necessary to calculate and generate
payments to HMOs under the risk contract programs. Specifically, it
translates rates from the Office of the Actuary into HMO monthly
payments. In addition, the Health Standards Quality Bureau within
HCFA is responsible for quality of care reviews on contracting HMOs.
Lastly, the Office of Research and Demonstrations awards contracts to
outside researchers and conducts internal research on HCFA's
programs.
--------------------
\14 Most U.S. citizens aged 65 and over are automatically enrolled
in Part A of Medicare. However, individuals may elect not to enroll
in Medicare Part B.
\15 A Medicare beneficiary who has ESRD and who previously belonged
to a risk contract HMO may remain with that HMO upon becoming
eligible for Medicare.
\16 Some HMOs who have reached their enrollment capacity are allowed
to forgo the annual enrollment period.
\17 Rural plans are required to have only 1,500 members (Medicare and
non-Medicare combined).
HOW HCFA COMPUTES HMO
PAYMENTS IN THE RISK
CONTRACT PROGRAM
-------------------------------------------------------- Chapter 1:1.4
In accordance with its legislative mandate, HCFA pays Medicare HMOs
based on local fee-for-service costs. The AAPCC is central to HCFA's
method of computing HMO payments. The AAPCC represents an actuarial
projection of what Medicare would have paid had the beneficiary
remained in traditional fee-for-service Medicare.
HCFA recalculates HMO payment rates every calendar year. First,
based on historical spending data on Medicare costs, HCFA's Office of
the Actuary projects per capita costs for the nation. This national
estimate is known as the United States per capita cost, or USPCC.
These projections take account of expected inflation, changing
utilization patterns, and changes in the Medicare program.\18 HCFA
calculates separate cost figures for Medicare Part A services and
Part B services for the aged, the disabled, and people with ESRD.
Second, HCFA adjusts the USPCC for geographic differences in Medicare
expenditures.\19 Through this process, HCFA determines
county-specific Medicare expenditures for Part A and Part B services
for the elderly and the disabled. A state-specific rate cost
estimate is calculated for ESRD patients.\20
Third, the county-specific cost estimate is then adjusted for the
following demographic factors--age, sex, institutional status, and
Medicaid status--to arrive at a county-specific HMO payment rate, of
which Medicare will pay 95 percent. To determine the payment amount
for each prepaid plan, HCFA applies these same demographic adjusters
to each enrollee in the plan.\21 For example, under this system an
HMO receives a higher capitation rate for an 80-year-old man than for
a 65-year-old man living in the same county, and will receive
different capitation rates for two 80-year-old men who live in
different counties.
--------------------
\18 For example, in 1983, Medicare introduced the Prospective Payment
System, which changed the way Medicare pays for hospital care, and in
1992 Medicare implemented the Resource Based Relative Value Scale
system for setting physician rates. Because USPCC calculations are
based largely on historical experience, HCFA must adjust its cost
projections to account for these changes in Medicare fee-for-service
payments.
\19 To ensure that the data for the AAPCC calculations are complete,
HCFA bases the USPCC calculations on data from 3 years previously.
Because HCFA uses a 5-year moving average of fee-for-service claims
data to derive the county-specific cost from the USPCC, data as old
as 8 years can affect the final AAPCC figures.
\20 At this point, HCFA adjusts the expected county costs to a
fee-for-service basis by removing the reimbursement and enrollment
attributable to Medicare HMO enrollees.
\21 One hundred twenty-two "rate cells" constitute Medicare
capitation for each county. Ten HCFA-defined age groups are
multiplied by Medicare's two parts (Part A and Part B), which are in
turn multiplied by two (for the two sexes), for a total of 40 groups.
There are therefore 40 institutionalized cells, 40
non-institutionalized/Medicaid cells, and 40 non-institutionalized/
non-Medicaid cells. HCFA adds 2 rate cells for ESRD patients to
these 120 cells, which yields 122 rate cells for each county.
FAVORABLE SELECTION COULD
REDUCE COST SAVINGS
-------------------------------------------------------- Chapter 1:1.5
Using these four demographic factors, HCFA adjusts HMO payments to
reduce the potential for favorable selection. Favorable selection
occurs when HMO enrollees are healthier on average than those
beneficiaries remaining in the fee-for-service sector, and this
difference in health status is not fully reflected in the payments
the HMO receives. Unless HMO payments are adjusted for
beneficiaries' health status, HMOs will have an incentive to enroll
only those patients expected to have lower-than-
average health care costs. If favorable selection exists, HMOs will
be paid more for providing enrollees' care than that care would have
cost in the fee-for-service sector; that is, favorable selection can
increase Medicare's costs. Despite HCFA's current payment
adjustments, critics have charged that favorable selection persists
in the Medicare risk contract program.
OBJECTIVES, SCOPE, AND
METHODOLOGY
---------------------------------------------------------- Chapter 1:2
This report responds to two congressional mandates--section 4017 of
the Omnibus Budget Reconciliation Act of 1987 (P.L. 100-203) and
section 4204 of the Omnibus Budget Reconciliation Act of 1990 (P.L.
101-508). These mandates required GAO to (1) describe the rate
setting methods used in the current Medicare risk contract program,
(2) evaluate the success of the current system in reducing Medicare
costs, and (3) evaluate the potential of alternative HMO rate setting
methods to improve on the current system.
We limited our review to evaluating HCFA's current rate setting
methodology and possible alternatives. We did not evaluate the
quality of care provided by Medicare risk contract HMOs. We did not
examine the accuracy of the data used by HCFA in the current program,
nor did we evaluate the program's administration or HCFA's internal
controls.
To evaluate the current rate setting methodology, to develop criteria
for evaluating risk adjustors and alternative payment systems, and to
assess the evidence on selection bias, we surveyed the extensive
academic literature on these subjects and interviewed researchers.
(See the bibliography at the end of this report.) To obtain
information on the current rate setting methodology and the risk
contract program in general, we interviewed HCFA officials in the
Office of Prepaid Health Care Operations and Oversight, the Bureau of
Data Management and Strategy, the Office of Research and
Demonstrations, the Office of the Actuary, and the Boston and Seattle
HCFA regional offices. To evaluate how the program affects Medicare
beneficiaries, we interviewed representatives of a Medicare
beneficiary advocacy group headquartered in Los Angeles.
To understand how the risk contracting program affects HMOs, we
interviewed officials from 2 HMO trade associations and
representatives from 14 HMOs. These HMOs had risk contracts in
several states: California, Florida, Maryland, Massachusetts,
Minnesota, Oregon, Washington, and Wisconsin. The HMO officials we
interviewed represented a range of organizational structures and HCFA
payment rates, including two HMOs that dropped out of the risk
contracting program. A number of the HMOs we interviewed have
participated in the risk contract program since its beginning.
We conducted our analysis from January 1993 to April 1994 in
accordance with generally accepted government auditing standards.
HCFA'S CURRENT SYSTEM IS UNABLE TO
PREVENT FAVORABLE SELECTION FROM
INCREASING MEDICARE COSTS
============================================================ Chapter 2
Because the payments they receive do not vary with the amount of
services used by the beneficiary, Medicare risk contract HMOs have
strong incentives to enroll only those beneficiaries who will not
require costly services. If HMO enrollees are healthier and
therefore less costly to treat than their fee-for-service
counterparts, and this difference in health status is not reflected
in an HMO's payments, favorable selection results. Despite HCFA's
administrative controls, and despite payment adjustments based on the
age, sex, Medicaid eligibility and institutionalized status of HMO
enrollees, favorable selection has persisted in the Medicare risk
contract program. This favorable selection has resulted in increased
costs for HCFA, compared to what HCFA would have spent for HMO
beneficiaries' care in the fee-for-service sector.
FAVORABLE SELECTION CAN ARISE
IN CAPITATED PAYMENT SYSTEMS
---------------------------------------------------------- Chapter 2:1
Because payment is made per person, and not per service, HMOs have an
incentive to reduce treatment that is unnecessary or of marginal
benefit.\22 These cost-reduction incentives, however, come with an
important qualification: unless payments are adjusted for
differences in individuals' health, insurers will have an incentive
to enroll only those patients who are expected to have relatively low
health care costs, and to discourage enrollment by patients who are
expected to have greater health care needs. This problem, known as
selection bias, biased selection, or favorable selection, can reduce
or eliminate the potential cost savings arising from a capitated
payment system.
Favorable selection can come about in two ways: (1) if Medicare
beneficiaries enrolling in HMOs are healthier than those remaining in
the fee-for-service sector and (2) if beneficiaries leave managed
care organizations and return to fee-for-service medicine when they
become ill. These enrollment and disenrollment patterns, which are
at the root of favorable selection, can arise either because of the
actions of the HMO or because of the actions of the patient. For
example, HMOs can encourage favorable selection by marketing in
settings such as shopping malls or senior fairs, which cater to more
mobile and healthy seniors. However, patient choice can also result
in favorable selection; for example, sicker patients are more likely
to have a long-term relationship with a particular physician, and may
be less willing to surrender their free choice of provider to join an
HMO.\23 We did not evaluate the extent to which favorable selection
arises from patient, provider, or HMO actions.
--------------------
\22 These incentives could also lead HMOs to reduce medically
necessary treatment. Studies of quality in the Medicare risk
contract program have concluded that Medicare HMOs generally provide
equal or better-quality care compared with the fee-for-service
sector, although a few HMOs may provide lower-quality care.
\23 However, some physicians, who belong to IPA-model HMOs, may see
both fee-for-service and HMO patients. Patients' willingness to join
an HMO may be increased if they can use the same doctor in the IPA as
in the fee-for-service sector. We might, therefore, theoretically
expect to find less favorable selection in an IPA- model HMO than in
a staff-model HMO. However, the evidence on this issue is
inconclusive.
TO REDUCE FAVORABLE SELECTION,
HCFA USES ADMINISTRATIVE
CONTROLS AND RISK ADJUSTMENT
---------------------------------------------------------- Chapter 2:2
In general, there are two ways of countering favorable selection:
administrative means and risk adjustment. Administrative means aim
to curb those HMO behaviors that can fuel favorable selection. HMOs
could potentially exert at least partial control over their patient
mix by refusing enrollment because of a patient's preexisting medical
condition. When these exclusions are prohibited--as they are in the
Medicare risk contract program--plans have less opportunity to select
healthier enrollees. Similarly, HMOs can design their packages to
include services attractive to healthier individuals, such as
wellness programs, and to limit benefits, such as prescription drug
coverage, that appeal to those with chronic conditions. By
standardizing benefits or mandating a minimum benefit package,
employers or health plan regulators can limit a health plan's ability
to design benefit packages that maximize the opportunities for
favorable selection.
Instead of affecting HMOs' actions directly, risk adjustment
represents an attempt to affect HMOs' actions indirectly, by altering
the incentives HMOs face to enroll healthier persons. By paying HMOs
a larger fee for a potentially sicker enrollee, the payer compensates
the HMO for the greater anticipated health needs of that enrollee.\24
For example, through an age-based risk adjustment, HMOs can receive
greater payments for enrolling older people (who generally use more
medical services), or through a disease-based risk adjustor, HMOs
could receive greater payments for treating people with specific
chronic conditions (such as cancer or diabetes). Therefore, a
perfectly accurate risk adjustment mechanism would make it equally
financially advantageous for an HMO to enroll a sicker person or a
healthier person. Such a perfectly accurate risk adjustor is
probably not obtainable, however, for it would necessitate a
prohibitively expensive data collection effort.
Neither administrative means nor risk adjustment is likely to
eliminate favorable selection entirely, because favorable selection
can result from patient choice as well as from HMO behavior. For
example, administrative means cannot prevent the favorable selection
that may arise when sicker Medicare beneficiaries, who have stronger
ties to fee-for-service physicians, are reluctant to join an HMO.
Similarly, risk adjustment can reduce, but is unlikely to eliminate,
HMOs' incentive to seek healthier Medicare beneficiaries. As long as
the HMO has more information on its enrollees than the payer, the HMO
will have the opportunity to discriminate among enrollees based on
health status.
HCFA uses both administrative means and risk adjustment in the
Medicare risk contract program. HCFA administrative requirements are
designed to detect and deter favorable selection. In addition to
generally prohibiting HMOs from excluding Medicare beneficiaries
because of medical conditions, the agency monitors HMO marketing
materials and practices. HCFA also surveys recent HMO disenrollees
to determine their reasons for leaving the HMO. In these surveys,
HCFA administrators often look to see if Medicare beneficiaries move
repeatedly from one HMO to another or if beneficiaries disenroll from
an HMO in response to changes in health status.\25
HCFA couples its administrative efforts with a risk adjustment
strategy in order to reduce HMOs' incentives to seek healthier
Medicare beneficiaries. HCFA pays each HMO different capitated rates
for each enrollee, based on the beneficiary's age, sex, Medicaid
eligibility, and institutional status.\26 For example, because older
Medicare beneficiaries generally need more care, rates for HMO
enrollees aged 85 and older are 1.4 to 2.0 times the rates paid for
otherwise comparable enrollees aged 65 to 69.
--------------------
\24 Although the payment the HMO receives depends on the patient's
health status, the patient's health insurance premium does not.
\25 Under current law, HCFA cannot limit favorable selection by
restricting disenrollment--that is, HCFA cannot "lock in"
beneficiaries by refusing to allow them to leave an HMO before a
specific amount of time has passed. (For example, federal employees
are allowed to change their health plan only once a year, unless
there is a change in family composition.) Such regulations would also
have strong disadvantages, however. Not only could disenrollment
restrictions "lock in" beneficiaries to low quality HMOs, but they
could discourage beneficiaries from joining HMOs.
\26 Medicare HMOs are also paid separate rates for disabled
beneficiaries and beneficiaries with ESRD.
FAVORABLE SELECTION PERSISTS IN
THE MEDICARE RISK CONTRACT
PROGRAM
---------------------------------------------------------- Chapter 2:3
Despite HCFA's administrative controls and risk adjustment efforts,
independent researchers have confirmed that Medicare HMOs experience
favorable selection, increasing HCFA's costs. These studies have
established that Medicare beneficiaries are healthier than their
fee-for-service counterparts, and therefore the cost of treating
these beneficiaries is less than 95 percent of the cost of treating
the typical fee-for-service beneficiary. However, researchers'
estimates of the magnitude of these cost differences varied.
According to these independent studies, costs under the risk contract
program were approximately from 5.7 to 28 percent higher than
Medicare would have spent had those beneficiaries remained in the
fee-for-service sector.\27
Not all HMOs benefit from favorable selection, however. A recent
comprehensive evaluation of biased selection in the Medicare risk
contract program found that 54 to 63 percent of Medicare HMOs enjoyed
favorable selection, and the remainder experienced neutral selection;
the study found no evidence than any plan suffered from adverse
selection (that is, no plan had sicker patients overall than the
fee-for-service sector).\28
Although earlier studies of favorable selection were criticized on
methodological grounds, more sophisticated work has continued to show
widespread favorable selection in the Medicare risk contract program.
Similarly, while some speculated that favorable selection might
decrease as HMOs gain a larger share of the Medicare market and fewer
seniors remain in the fee-for-service sector, research on areas with
high HMO market penetration has not supported this conjecture.\29
Therefore, in the absence of changes in the risk contract program,
favorable selection can be expected to persist.
--------------------
\27 For a summary of this literature, see The Impact of the Medicare
Risk Contract Program on the Use of Services and Costs to Medicare,
Mathematica Policy Research, Inc., report to HCFA (Dec. 3, 1992).
\28 Biased Selection in the TEFRA HMO/CMP Program, Mathematica Policy
Research, Inc., report to HCFA (Sept. 21, 1990).
\29 For example, see Biased Selection in the TEFRA HMO/CMP Program
and K.W. Adamache and L.F. Rossiter, "The Entry of HMOs Into the
Medicare Market: Implications for TEFRA's Mandate," Inquiry, Winter
1986, 23 (4), pp. 349-364.
FAVORABLE SELECTION EXISTS
DESPITE REGRESSION TO THE
MEAN
-------------------------------------------------------- Chapter 2:3.1
Early studies of favorable selection were criticized for failing to
account for statistical regression to the mean. Regression to the
mean could occur in this context if HMO enrollees were very healthy
at the time of their initial enrollment, but over time their unusual
good health faded. To the extent that health is determined by random
events, regression to the mean may imply that estimates of favorable
selection drawn at the point of initial enrollment are overestimates
of long-term favorable selection (although they would be accurate
estimates of favorable selection in the short term). However, good
health tends to persist--if patients are healthy today, they are
likely to be healthy tomorrow; if they are sick today, they are
likely to be sick tomorrow. In addition, if sicker people tend to
disenroll from HMOs, and new enrollees tend to be relatively healthy,
favorable selection can persist in an individual HMO, despite
regression to the mean in health status by earlier enrollees. That
is, even if healthy new HMO enrollees get sicker over time, favorable
selection will persist if beneficiaries disenroll from an HMO as they
get sicker and if new enrollees tend to be healthy. Estimates of
favorable selection over time--that take into account regression to
the mean--show that favorable selection persists in individual
HMOs.\30
--------------------
\30 For example, see Health Status, Financial Barriers, and the
Decision to Enroll in Medicare Risk Plans, Mathematica Policy
Research, Inc., report to HCFA (June 19, 1992), and The Impact of the
Medicare Risk Contract Program on the Use of Services and Costs to
Medicare, Mathematica Policy Research, Inc., report to HCFA (Dec. 3,
1992).
FAVORABLE SELECTION PERSISTS
IN AREAS OF HIGH HMO MARKET
PENETRATION
-------------------------------------------------------- Chapter 2:3.2
Some researchers have conjectured that increasing HMO market
penetration would attenuate favorable selection--that is, as HMOs
enrolled a larger section of the Medicare market, their ability to
attract healthier populations would diminish. However, recent
research suggests that favorable selection persists even where HMO
market penetration is relatively high. A study of Medicare HMO
disenrollment revealed no obvious relationship between HMO market
penetration and favorable selection, and another research study
reported that areas of relatively high market penetration had both a
higher proportion of plans with very favorable selection and a higher
proportion of plans with little favorable selection.\31
Therefore, favorable selection is not likely to disappear once larger
numbers of Medicare beneficiaries are enrolled in HMOs.
--------------------
\31 See Disenrollment Experience in the TEFRA HMO/CMP Program: 1985
to 1988, Mathematica Policy Research, Inc., report to HCFA (May 19,
1989), and Biased Selection in the TEFRA HMO/CMP Program.
FAVORABLE SELECTION INCREASES
HCFA'S COSTS AND MAY REDUCE
BENEFICIARIES' ACCESS TO
MANAGED CARE
---------------------------------------------------------- Chapter 2:4
Favorable selection in the risk contract program increases Medicare
costs--that is, as a result of favorable selection, HCFA's costs of
serving Medicare HMO beneficiaries are greater than they would have
been if the same beneficiaries had remained in the fee-for-service
sector. Specifically, favorable selection can increase Medicare
costs in two related ways. First, because Medicare HMO beneficiaries
are healthier on average, their treatment costs less, on average,
than the capitation rate Medicare pays HMOs. Second, favorable
selection results in Medicare's paying a higher capitation rate than
it would otherwise. This occurs because--as required by law--the
capitation rate is based on fee-for-service costs, and those costs
increase when relatively healthier beneficiaries join the HMOs.
Beneficiaries remaining in the fee-for-service sector must therefore
be relatively less healthy and consequently more costly.
Favorable selection may also affect patient choice. To the extent
that favorable selection arises from plan actions in discriminating
among enrollees, sicker beneficiaries' access to managed care may be
reduced.
FAVORABLE SELECTION
CONTRIBUTES TO, BUT DOES NOT
GUARANTEE, HMO PROFITS
-------------------------------------------------------- Chapter 2:4.1
Although favorable selection has increased the government's cost of
serving Medicare beneficiaries, these cost increases do not
necessarily result in greater HMO profits. Although favorable
selection contributes to HMO profits, it is only one of a number of
factors that determine an HMO's financial success with the risk
contract program. These other factors could offset the financial
benefits of favorable selection. According to industry experts and
research studies, these other factors include the following:
1. Administrative costs of participating in the risk contract
program. To participate in the risk contract program, plans must
incur the costs of calculating the adjusted community rate (ACR) for
HCFA's approval, based on financial and actuarial information on
their commercial and Medicare businesses; submitting their records
for quality review by a peer review organization; following
HCFA-specified review procedures to settle complaints; and submitting
marketing materials to HCFA.
2. Having sufficient Medicare enrollees over which to spread risk
and overhead. According to some participating HMOs, Medicare risk
contracts cannot be profitable unless enrollment is sufficiently high
to spread various program-related costs. The representatives we
interviewed from one of the plans, which had a small Medicare
enrollment, indicated that the administrative costs of their risk
contract contributed to their decision to drop out of the risk
contract program. As a rule of thumb, some analysts have recommended
a minimum enrollment of 10,000 for success in the risk contract
program.
3. HMO enrollees may increase their utilization of services after
joining an HMO, compared to what utilization would have been had the
enrollees remained in the fee-for-service sector. Some HMOs have
hypothesized that, with lower copayments and deductibles, HMO
enrollees may increase their utilization of services and thereby
increase HMO costs. However, because many HMOs use case management
and gatekeepers--primary care providers who screen access to
specialized services--to control Medicare beneficiaries' utilization,
it seems unlikely that new HMO enrollees could markedly increase
their use of services. One study that directly compared new
enrollees' utilization in risk contract HMOs with their utilization
in the fee-for-service sector found no evidence of a "pent-up demand"
among new HMO enrollees.\32 Even if such a pent-up demand were to
exist, however, estimates of favorable selection's effect on Medicare
costs would be unaffected. Because HMO enrollees would not have
sought additional services had they remained in the fee-for-service
sector, they would not have generated a bill for HCFA. As a result,
favorable selection can still increase Medicare costs because the
cost to HCFA of serving beneficiaries could have increased compared
to what it would have been if enrollees had used fee-for-service
care.
--------------------
\32 Health Status, Financial Barriers, and the Decision to Enroll in
Medicare Risk Plans.
CONCLUSION
---------------------------------------------------------- Chapter 2:5
Despite HCFA's administrative controls and risk adjustment efforts,
current Medicare risk contract procedures are inadequate to ensure
cost savings and to expand beneficiary choice of delivery systems.
Because HMOs have enrolled only relatively healthier Medicare
beneficiaries, favorable selection has interfered with the capitated
payment system's ability to reduce costs. For these cost savings to
materialize, favorable selection in the Medicare risk contract
program must be substantially reduced. Opportunities to reduce
favorable selection come from proposed improvements to HCFA's risk
adjustment system.
HEALTH-BASED RISK ADJUSTMENT AND
ADMINISTRATIVE OVERSIGHT NECESSARY
TO DECREASE FAVORABLE SELECTION
AND REDUCE MEDICARE COSTS
============================================================ Chapter 3
The prevalence of favorable selection in the Medicare risk contract
program has increased the government's cost, prompting calls for
improvement in Medicare's HMO payment system. In response to this
need for change, researchers and industry experts have urged HCFA to
include a measure of health status, along with demographic factors,
in its risk adjustment methodology.\33 Analysts have examined several
health status adjustors, each designed to reduce incentives for HMOs
to enroll only relatively healthy Medicare beneficiaries. Although
these alternative methods of risk adjustment are unlikely to
eliminate favorable selection entirely, they do promise to reduce
favorable selection and thereby decrease program costs. While no
single risk adjustor has emerged as the definitive alternative to
HCFA's current system, we identified a set of criteria to evaluate
risk adjustment options. Using these criteria, we selected four risk
adjustment systems as promising directions for further research.
--------------------
\33 HCFA could make such a change to the methodology without seeking
congressional approval.
RISK ADJUSTMENT CAN REDUCE
INCENTIVES FOR HMOS TO ENROLL
ONLY HEALTHIER PATIENTS
---------------------------------------------------------- Chapter 3:1
Both administrative means and risk adjustment are available to
mitigate favorable selection. Whereas administrative means attack
favorable selection by reducing HMOs' ability to select healthier
patients, risk adjustment attacks favorable selection by reducing
HMOs' incentive to select healthier patients. In a risk-adjusted
capitated payment system, the fixed rate the HMO receives for
treating a given patient is adjusted for that patient's health
status. By paying higher rates for patients expected to have greater
health care needs, and lower rates for patients expected to have
lower health care needs, risk adjustment reduces HMOs' financial
incentive to enroll only healthy patients. A perfectly accurate risk
adjustor would structure HMO payments so that the HMO would receive
equal financial rewards, regardless of whether its enrollees were
sick or healthy.
LIMITATIONS OF RISK
ADJUSTMENT
-------------------------------------------------------- Chapter 3:1.1
A perfectly accurate risk adjustor is probably not achievable.
Although adjusting prospective payment rates on the basis of the
patient's health can reduce the incentives that lead to favorable
selection, the currently feasible risk adjustment methodologies are
unlikely to prevent favorable selection completely. This inability
to forestall all favorable selection stems from the fact that no
measure of health status, no matter how exact, can capture all the
variation in health care costs. There are two sources of variation
in health care costs to consider--those that are random in nature and
those that are not.
Some health care costs arise because of unavoidable, unpredictable
accidents--for example, when an otherwise healthy person slips on an
icy sidewalk and breaks an arm. Such costs are unforeseeable and
cannot be captured by risk adjustment measures. However, given that
these conditions are unforeseeable or random variations in
beneficiaries' health status, they do not create incentives for HMOs
to seek favorable selection.
By contrast, the nonrandom variation in health care costs--such as
that arising from chronic conditions--may be foreseeable by HMOs.
Risk adjustment can account for some portion of this variation.
However, just as no risk adjustor will contain sufficient information
to predict all health care expenditures, no operational risk adjustor
will contain sufficient information to eliminate favorable selection
entirely. So long as the HMO has more information on individual
beneficiaries than can be captured by the risk adjustor, the HMO will
have an opportunity to create favorable selection.\34 For example, if
the HMO is paid more for cancer patients than for those without
cancer, the HMO may encourage enrollment by relatively healthy cancer
patients (for example, those in long-term remission) and discourage
enrollment by those cancer patients who are relatively sicker.
Because the HMO can distinguish between healthier and sicker cancer
patients, whereas the risk adjustor does not, the HMO can take
advantage of the opportunity for favorable selection. For this
reason, administrative means of controlling favorable selection
remain important, even if payments are adjusted based on health
status. While the risk adjustment reduces the HMO's incentives and
opportunities to create favorable selection, administrative means and
oversight (such as requiring HMOs to accept all patients,
standardizing benefit packages, and prohibiting HMOs from encouraging
sicker patients to disenroll) can lessen HMOs' ability to take
advantage of any opportunities for favorable selection that remain.
--------------------
\34 We recognize that administrative measures may prevent an HMO from
engaging in some behaviors that generate favorable selection.
Nonetheless, administrative means cannot eradicate all opportunities
an HMO has to attempt to attract a more favorable case mix nor
eliminate favorable selection caused by the decisions of
beneficiaries.
CRITERIA EXIST FOR EVALUATING
ALTERNATIVE RISK ADJUSTMENT
METHODS
---------------------------------------------------------- Chapter 3:2
By reviewing and evaluating the available literature and meeting with
HCFA representatives and HMO officials, we identified generally
accepted operational criteria for evaluating alternative risk
adjustment schemes.\35 Specifically, a good risk adjustment scheme
would (1) accurately predict health care costs, (2) treat
participating HMOs reasonably and fairly, (3) be difficult for
participating health plans to manipulate, (4) respect patient privacy
and confidentiality, (5) create incentives for appropriate care, and
(6) be feasible and inexpensive to administer. Trade-offs exist
among these criteria, making evaluations more difficult. For
example, a more complex risk adjustment method, by accounting for
more of the nonrandom variation in health care costs, may be more
successful in reducing favorable selection, but may do so only at a
relatively high administrative cost.
--------------------
\35 For a more detailed description of these criteria, see appendix
II.
RISK ADJUSTMENT VARIABLES
MUST PREDICT HEALTH CARE
COSTS ACCURATELY
-------------------------------------------------------- Chapter 3:2.1
To be useful in preventing selection bias, a risk adjustment variable
must have predictive power--that is, the risk adjustment variable
must be closely related to health care costs. Ideally, a risk
adjustor's predictive power should enable it to predict the health
care costs of the most expensive group of beneficiaries, because
these patients account for the majority of health care
expenditures.\36 If the risk adjustment variable has insufficient
predictive power, the adjusted payments will not strongly reflect
differences in the cost of treating patients, and the HMO will
continue to have strong incentives and opportunities to encourage
favorable selection.
Assessing the relative predictive power of alternative risk adjustors
is difficult, largely because predictive power is not easy to measure
accurately. In addition, methodological shortcomings in existing
studies make it difficult to evaluate the predictive power of
competing risk adjustment proposals.
--------------------
\36 In fact, 4 percent of Medicare patients account for 50 percent of
Medicare costs.
RISK ADJUSTMENT PROCESS
SHOULD NOT IMPOSE UNDUE
ADMINISTRATIVE BURDEN ON
HCFA OR ON PARTICIPATING
HMOS
-------------------------------------------------------- Chapter 3:2.2
A feasible risk adjustor should not be overly burdensome to
administer. Health-based risk adjustment requires that patients'
health status be measured, reported to the payer (HCFA), and
converted to capitated payments. Each of these tasks can impose an
additional administrative burden on HCFA and on participating health
plans.
The lighter HCFA's administrative burden, other things being equal,
the greater the opportunity for the risk contract program to expand
choice and to potentially result in cost savings. If plans must
incur high administrative costs to participate in the program, they
are less likely to participate, and this lack of participation can
limit Medicare beneficiaries' access to an HMO option. Despite the
importance of minimizing administrative burden, it is difficult to
assess how HMOs will respond to changes in the payment system.
HMOS EXPRESS CONCERN ABOUT
THE FAIRNESS OF THE RISK
ADJUSTMENT PROCESS
-------------------------------------------------------- Chapter 3:2.3
In our interviews with representatives of participating HMOs, they
expressed concern that any new risk adjustment system be fair and
even-handed in its treatment of competing HMOs. Fairness is
difficult to define and even harder to assess. Indeed, some plans'
perceptions of fairness may conflict--for example, officials from one
HMO said that HCFA should take into account the special circumstances
facing different plans, while another HMO's representative stressed
the importance of a uniform set of rules.
RISK ADJUSTMENT PROCESS CAN
BE DESIGNED TO LIMIT ITS
SUSCEPTIBILITY TO
MANIPULATION BY
PARTICIPATING HMOS
-------------------------------------------------------- Chapter 3:2.4
Ideally, a risk adjustor would limit HMOs' ability to manipulate the
risk adjustment data or to adopt strategies for recruiting or
retaining only healthier enrollees. For example, the potential for
fraud may increase if risk adjustment data are gathered directly from
the HMOs, without outside verification. In addition, some risk
adjustment mechanisms are more vulnerable to "within-cell selection"
by participating HMOs--that is, HMOs can continue to select healthy
enrollees within a given risk cell, leaving sicker Medicare
beneficiaries to the fee-for-service sector and thus driving up
costs. For example, a risk adjustor that increases an HMO's payment
when a patient has heart disease creates opportunities for the HMO to
benefit by enrolling only the healthiest patients with heart disease.
By contrast, a risk adjustor that accounts for the severity of
illness will create fewer such opportunities.
RISK ADJUSTMENT MECHANISM
MUST BE COMPATIBLE WITH
INCENTIVES TO PROVIDE
APPROPRIATE MEDICAL CARE
-------------------------------------------------------- Chapter 3:2.5
Analysts of alternative risk adjustment mechanisms have expressed
fears that risk adjustment systems could create incentives for HMOs
to deviate from an appropriate standard of care. For example, risk
adjustment systems that pay HMOs more for their sicker members may
reduce HMOs' financial incentives to provide preventive care.
However, little agreement exists on a general standard of appropriate
care, nor has consensus been reached on how to judge when that
standard has been violated.
RISK ADJUSTMENT PROCESS
RAISES QUESTIONS OF PATIENT
PRIVACY AND CONFIDENTIALITY
OF MEDICAL RECORDS
-------------------------------------------------------- Chapter 3:2.6
Because risk adjustment requires evaluation of patients' health
status, some risk adjustment methods incorporate individual patients'
medical information. The need for such data raises questions of
patient privacy and confidentiality. The invasiveness of a risk
adjustor can be determined, in part, by considering the following:
who has access to the data, how sensitive the data are, and how
easily the information can be understood by those who may observe it.
ALTERNATIVE RISK ADJUSTORS RELY
ON DIFFERENT MEASURES OF
BENEFICIARIES' HEALTH STATUS
---------------------------------------------------------- Chapter 3:3
The persistence of favorable selection in the Medicare risk contract
program has prompted analysts to call for a health status based risk
adjustment scheme. Such a risk adjustment method would require the
rate payer (in this case, HCFA) to obtain measures of beneficiaries'
health status. The large variety of ways to measure individuals'
health status has prompted researchers to develop an array of
potential risk adjustment mechanisms.\37 All of these
alternatives--unlike HCFA's current system--measure the health status
of HMO enrollees. We classified these risk adjustors into
categories, according to the information on which risk adjustment is
based. For example, several proposed risk adjustors use clinical
information on enrollees, while others use utilization data and other
measures use self-reported information gathered directly from
patients.\38
Clinically-based adjustors base HMO payments on the medical diagnoses
of HMO patients. Risk adjustors based on clinical data range from
simple to complex. The simplest clinical measures base payments on
the presence or absence of particular medical conditions--for
example, a history of cancer, heart attack, or stroke. More
sophisticated clinical risk adjustors, like the Ambulatory Care
Groups (ACG) method, can take into account not only the presence or
absence of a disease, but also its severity. Other relatively
complex clinical risk adjustment systems are used by some HMOs to
adjust the capitation rates they pay participating physicians. In
some of these systems, panels of physicians assess patients' medical
diagnoses to generate a more detailed health status indicator.
Prior utilization measures--for example, the number of days in the
hospital or total hospital costs for the previous year--have also
been examined as risk adjustors.
Combinations of prior utilization and clinical measures have also
been examined. These measures base payments both on prior
utilization of medical services and on current diagnostic
information. A prominent combination measure is the Diagnostic Cost
Groups (DCGs) method, which combines a clinical measure (diagnosis
code) with a utilization measure (hospital days).\39
Self-reported health status--that is, patient responses to questions
about how they perceive their own health--has also been suggested as
a potential risk adjustor. One widely used questionnaire asks
patients to report about a number of aspects of health, including
their physical and emotional health, their energy level, how well
they can function in daily tasks, and how they expect their health to
change in the future.
Functional status measures--exemplified by the Activities of Daily
Living or Independent Activities of Daily Living protocols--examine
beneficiaries' ability to function and to perform various tasks, such
as grocery shopping, housecleaning, eating, dressing, and food
preparation. These evaluations could be used to compensate HMOs for
the care of particularly frail patients.
Life-style and socioeconomic factors that are associated with high
medical costs could be used as risk adjustors. For example, data on
smoking, marital status, education, and occupation could be used to
adjust HMO capitation payments.
In addition to choosing a health status measure, HCFA officials may
consider whether to implement risk adjustment retrospectively or
prospectively. Retrospective adjustment mechanisms would adjust HMO
payments after services had been rendered to patients, whereas
prospective adjustment takes place at the beginning of the payment
period, before any services are rendered. Retrospective adjustments
are often discussed in the context of cost-based reinsurance, where
HMOs' payments would be adjusted retrospectively, on the basis of the
costs incurred during that period. This type of retrospective
adjustment can serve as a way for HCFA to share with HMOs the
financial risk of caring for high-cost beneficiaries. Compared with
prospective payment systems, retrospective adjustments could limit
HMOs' cost-saving incentives.
--------------------
\37 We identified 10 alternative risk adjustors. For a more detailed
description of these alternative risk adjustors, see appendix I.
\38 Also, some have considered adjusting HMO payment rates for
mortality experience--that is, increasing rates for HMOs with higher
mortality rates, on the assumption that those HMOs must have had
sicker patients. However, such an adjustor would raise ethical
questions and could create incentives for poor care.
\39 Another proposed combination method is the Payment Amount for
Capitated Systems (PACS) method, which combines inpatient and
outpatient prior use data with the Medicare wage index.
ALTHOUGH NO ONE RISK ADJUSTOR
IS BEST, SEVERAL ALTERNATIVES
WOULD IMPROVE THE CURRENT
SYSTEM
---------------------------------------------------------- Chapter 3:4
Because of the trade-offs among different criteria and because of the
limited state of knowledge in this area, we were unable to
definitively recommend one risk adjustor as optimal. However, of the
10 alternatives we examined, we concluded that 4
adjustors--self-reported health status, simple clinical indicators, a
more complex clinical measure (the ACG method), and one system that
combines clinical and utilization data (the DCG method)--hold the
most promise for improving the current system.
Our four preferred risk adjustors combine some of the virtues of the
current system--administrative feasibility and incentives for
appropriate care--with the potential for greater effectiveness
against favorable selection. Like HCFA's current system, each of
these methods is administratively feasible and should be compatible
with appropriate care. Unlike the current system, each of these
methods has sufficient predictive power to be an effective risk
adjustor.
For example, a simple clinical measure (such as a variable that
indicates a history of heart disease) has a number of
advantages--especially in predictive power and administrative ease.
In addition, the simple clinical measure provides fewer opportunities
for within-cell selection than the current method. However, health
plans would still have more opportunities to select within cells than
they would with some of the other measures. Self-reported health
status probably creates fewer opportunities for within-cell selection
than simple clinical measures do, although it may be invasive for
some beneficiaries and less reliable in its predictive power. ACGs
probably create the fewest opportunities for within-cell selection,
and they have strong predictive power, but they may be
administratively burdensome for participating HMOs. Although DCGs
would be easier to administer than ACGs, they could create incentives
for HMOs to substitute inpatient for outpatient care. In the long
run, a combination of the ACG and DCG methods may allow HCFA to
combine some of the advantages of both, but research on this
possibility is only in its nascent stages.
Evaluated against our criteria, the other risk adjustment mechanisms
appeared less suitable for the Medicare risk contract program.
Life-style and socioeconomic measures, for example, have less
predictive power, can be intrusive, and may reduce HMOs' incentives
to provide preventive care. Functional status measures would be
administratively burdensome, provide little advantage in predictive
power, and be invasive to beneficiaries. Prior utilization and prior
costs, although appealing in terms of predictive power, can reduce
cost-saving incentives by compensating HMOs based on the number of
services they have provided in the past. For a more detailed
description of this analysis, see appendix III.
CONCLUSION
---------------------------------------------------------- Chapter 3:5
Although little agreement exists on which of the many available
alternative risk adjustment schemes is best for the Medicare risk
contract program, researchers do agree that change is necessary.
Available evidence indicates that the four risk adjustors we
identified are the most promising candidates for long-term
improvement in the Medicare risk contract program. However, in the
short run, the Medicare risk contract program may be better able to
implement the less complex risk adjustment schemes. Specifically, a
simple clinical indicator system would impose more limited
administrative costs on HCFA and on participating HMOs. Such an
adjustment would add a history of cancer, heart disease, or stroke to
HCFA's current risk adjustment structure, reducing HMOs' incentives
to exclude these costlier individuals. In addition, the data for
such an adjustment would be relatively easy to verify and would need
to be updated only for beneficiaries who first encountered such
health problems after enrolling in the HMO. However, because
within-cell selection would likely persist with a clinical indicator
adjustment, such an interim fix is unlikely to provide a long-term
solution. Therefore, even if HCFA were to implement a clinical
indicator system, the agency should continue its research efforts to
identify potential long-term improvements in the risk adjustor
methodology.
RECOMMENDATION
---------------------------------------------------------- Chapter 3:6
We recommend that the Administrator of HCFA direct the agency to
sponsor further research and demonstration work on the four risk
adjustors we have identified. HCFA should identify ways to
incorporate research on these adjustors into its overall research
agenda. The demonstration projects we recommend should be
independently evaluated in terms of each of the criteria we
identified in this report, and should be wide ranging enough to
permit general conclusions. Specifically, demonstration projects
should cover a wide geographic range and a sufficient number and
variety of participating HMOs. To achieve this goal, the
demonstration project must be attractive enough to encourage HMO
participation--that is, HMOs must be compensated fairly for any
increase in administrative costs, and (for the duration of the
demonstration) they should not be paid less than under the current
system because they volunteered for the demonstration.
PROBLEMS WITH HMO PAYMENT SYSTEM
EXTEND BEYOND RISK ADJUSTMENT
============================================================ Chapter 4
The Medicare risk contract program faces difficulties not only with
risk adjustment, but also with constructing the base rate to which
these risk adjustors apply. Because these base rates are constructed
from Medicare fee-for-service costs, HMO capitation payments reflect
both access problems in some geographic areas and inefficient
practice patterns in others. As a result, rates in some areas are
too low to induce HMO participation in the risk contract program,
while in other areas rates are too high for Medicare to fully realize
the potential cost savings generated by capitated payments.
Recognizing these problems, researchers and HMO industry
representatives have proposed a number of alternatives to the
existing risk contracting system. However, evidence is limited as to
the impact of any of these proposals on plan participation and
Medicare costs.
HMO PAYMENTS VARY WITH
UTILIZATION IN THE
FEE-FOR-SERVICE SECTOR
---------------------------------------------------------- Chapter 4:1
HMO PAYMENT RATES CAN DIFFER
SUBSTANTIALLY ACROSS STATES
AND BETWEEN ADJACENT
COUNTIES
-------------------------------------------------------- Chapter 4:1.1
Across the nation and sometimes even across neighboring counties, HMO
payment rates vary substantially. For example, Medicare's unadjusted
HMO payment rate for Part A and Part B combined ranges from $110.46
(in Cabo Rojo, Puerto Rico) to $653.44 (in Bronx County, New
York).\40 (Figure 4.1 shows the distribution of Medicare HMO payment
rates.) Many of the HMO officials we interviewed complained about
this variation in HCFA capitation rates from one region of the
country to another. Several HMO officials also asserted that they
were being paid too little, compared with plans in locations with
higher rates.
Figure 4.1: Distribution of
Medicare Risk Contract HMO
Payment Rates Across Counties
(See figure in printed
edition.)
Notes:The rates given in this chart are the combined rates for Part A
and Part B services for a 70-year-old man without Medicaid status who
does not live in an institution.
The mean HMO payment rate across all counties in the United States is
$310.02, and the median rate is $305.09.
Source: HCFA.
Not only do rates vary across the country, but rates can also vary
significantly between adjacent counties. For example, Medicare's
unadjusted 1994 HMO payment rate is 28 percent lower in Montgomery
County, Maryland, than in adjacent Prince George's County, Maryland.
Many of the HMO officials we interviewed cited these adjacent county
differences as one of the biggest deficiencies of the current rate
setting methodology.
--------------------
\40 The figures given in this section are the standard rates for a
70- to 74-year-old noninstitutionalized man who does not receive
Medicaid.
RATE VARIATION STEMS FROM
LINK BETWEEN FEE-FOR-SERVICE
EXPENDITURES AND HMO PAYMENT
RATES
-------------------------------------------------------- Chapter 4:1.2
This wide variation in HMO payment rates across areas is a
consequence of the variation in local Medicare fee-for-service
expenditures and is not linked with HCFA's risk adjustment. Because
the law requires that HMO base rate payments be determined from prior
fee-for-service Medicare expenditures, differences in these base
rates across counties reflect local variations in both the prices of
medical services and the quantities of medical services used. HMO
capitation rates may differ across counties if doctors' services are
more expensive in one county than another or if Medicare
fee-for-service beneficiaries in one county tend to use more services
than demographically similar beneficiaries in another county. If
Medicare fee-for-service beneficiaries in one area face barriers in
seeking medical care (such as inadequate transportation or a lack of
providers in rural areas), their low utilization will be reflected in
low HMO payment rates. By contrast, if Medicare fee-for-service
beneficiaries in another county tend to use a large number of
services (either because beneficiaries demand these services or
because their doctors order additional tests), their high utilization
will increase HMO payment rates.
Medicare costs would be minimized if payments to HMOs reflected the
minimum cost to the HMO of providing appropriate care. While
differences in physician wages, for example, reflect the true cost of
providing appropriate care, patterns of overutilization or
underservice do not. From the pattern of HMO rates, industry experts
and health care researchers have inferred that the variation in rates
across the country and across county boundaries exceeds the variation
in local medical prices. The extent of the variation in
fee-for-service expenditures suggests that HMO payments may bear
little relationship to the HMO's actual costs of providing care, but
little evidence is available to determine the extent of the
disparity.
IN SOME AREAS, LOW PAYMENT
RATES MAY LIMIT HMO
PARTICIPATION IN THE RISK
CONTRACT PROGRAM, RESTRICTING
BENEFICIARY CHOICE
---------------------------------------------------------- Chapter 4:2
Many HMOs have entered the Medicare risk contract program in areas
with high rates, but very few HMOs in low-rate counties have joined.
(Not surprisingly, HMOs are more likely to participate in the risk
contract program if they believe they can make a profit.) Research
confirms that low payment rates in some areas discourage plans from
signing or renewing Medicare risk contracts. Correspondingly, we
interviewed representatives of several HMOs who dropped risk
contracts or switched to cost contracts\41 because they believed
HCFA's payment rates were too low in their local areas.\42
Rates tend to be especially low in rural counties, discouraging HMOs
in such areas from adopting risk contracts. Researchers have found
that plans with substantial rural enrollment are more likely to drop
out of the risk contract program. For example, representatives of a
national HMO chain told us that a low payment rate compelled their
North Carolina affiliate in a largely rural area to terminate its
Medicare risk contract. Such plans may switch to cost contracts,
which have weaker cost reduction incentives. The low population in
rural areas may also discourage plan participation, because a
sufficiently large Medicare enrollment may be required to spread the
fixed costs of a Medicare risk contract. For a plan with a larger
number of Medicare beneficiaries, in contrast to a plan with a
smaller enrollee population, the fixed administrative costs of a risk
contract may be a small percentage of total costs, and therefore
would not interfere with the HMO's ability to earn a profit on its
Medicare risk contract.
Because participating HMOs are largely concentrated in a few major
market areas, such as Minneapolis, Miami, and Los Angeles, Medicare
beneficiaries outside these areas may not have the choice of joining
a risk contract HMO.\43 Reduced HMO participation in the risk
contract program may decrease Medicare beneficiaries' choice of
health care delivery systems and decrease the cost-saving potential
of the risk contract program.
Rate variation across adjacent counties (in contrast to low rates in
a particular county) may also make HMOs reluctant to participate in
the risk contract program. A recent study reported that plans with
sizeable differences in adjoining counties are more likely to
withdraw from risk contracting. This phenomenon may be partially
explained by the fact that many HMOs' Medicare marketing reaches more
than one county. HMOs are paid on the basis of where their Medicare
beneficiaries live, rather than where services are provided. If two
demographically comparable beneficiaries who live in different
counties belong to the same HMO and use the same services, the HMO
can be paid very different rates for their care.
--------------------
\41 Cost contracts essentially allow an HMO to treat Medicare
beneficiaries, whereas HCFA pays HMOs on a cost-reimbursement basis.
Thus, cost contracts may not create the same incentives to decrease
unnecessary utilization that are associated with risk contracts.
\42 Officials of one HMO operating in an area with a significant
military population complain that the current formula harms HMOs
operating in such counties. The cost of treatment for Medicare
beneficiaries who receive inpatient treatment in military facilities
is not included in the fee-for-service cost estimates that form the
basis of HCFA's payment rates. Nonetheless, these beneficiaries are
included in the count of area Medicare beneficiaries. This formula
can result in payment rates that are lower than they would have been
if the Medicare beneficiaries who receive care in military treatment
facilities were instead treated in other facilities that Medicare
pays. Objective data to assess the local or national impact of this
effect were not available.
\43 Medicare beneficiaries in areas without a risk contract HMO may
still have an HMO option, if an HMO in their area has a Medicare cost
contract or health care prepayment plan contract. As of March 1993,
approximately 54 percent of all Medicare HMOs were risk contractors,
13 percent were cost contractors, and 33 percent were health care
prepayment plans. However, 67 percent of all Medicare HMO
beneficiaries were enrolled in risk contract HMOs.
RATE INSTABILITY AND
ACCURACY POSE DILEMMA FOR
HCFA
-------------------------------------------------------- Chapter 4:2.1
Plan officials have complained about the instability of rates over
time, as well as low rate levels. For a few counties, particularly
in rural areas, HMO payment rates have fluctuated considerably from
one year to the next. Rates in rural counties can fluctuate because
of the small number of Medicare beneficiaries in such counties--a few
very expensive illnesses can drive up Medicare fee-for-service
expenditures (and thereby HMO rates) for a county, while an
especially "healthy" year can reduce expenditures. Officials of one
nonrural HMO told us that HMO rate instability impaired its long-term
planning efforts--for example, by complicating decisions about
investing in new clinics and expanding its physician network. In
addition, this plan did not want to subject its members to the wide
swings in premiums that this rate instability might require to keep
the plan financially healthy.
To reduce rate instability, HCFA forecasts increases in expenditures
using historical data from 5 years, rather than only the previous
year's experience. This methodology can level out swings in rates,
but any errors in HCFA's forecasts or historical data will persist
for several years afterward.
IN SOME AREAS, INEFFICIENT
FEE-FOR-SERVICE PRACTICE
PATTERNS INCREASE HCFA'S
PAYMENTS TO HMOS
---------------------------------------------------------- Chapter 4:3
Some geographic areas exhibit particularly inefficient
fee-for-service practice patterns--that is, fee-for-service
physicians in some areas tend to provide more services, some of which
are unnecessary or of marginal benefit. When Medicare makes payments
to HMOs that are based on these high fee-for-service costs, the HMOs
and their enrollees, rather than HCFA, benefit from the cost-saving
potential of capitated payments.
HMOs can profit from inefficient practice patterns because the
capitated Medicare payments they receive are likely to exceed the
cost of efficiently providing appropriate care. However,
beneficiaries may also benefit from these high rates. The Medicare
risk contract program, through its adjusted community rate (ACR)
requirement, allows HMOs to profit from the program only up to their
rate of profit on their commercial business. Any profits above and
beyond that rate must be returned to the beneficiaries in the form of
additional benefits or rebated to HCFA.\44 In practice, HMOs in areas
with high payment rates, such as Florida and Southern California,
choose to provide additional benefits to their Medicare enrollees,
including zero premiums, reduced deductibles or copayments, extended
hospital coverage, and (in some HMOs) prescription drug coverage. In
these high-rate areas, then, the cost savings derived from the more
efficient medical practice of the HMOs accrue to the HMOs and their
enrollees, rather than to HCFA.
--------------------
\44 An HMO also has the option of contributing to a benefit
stabilization fund.
EFFECT OF HMO MARKET
PENETRATION ON
FEE-FOR-SERVICE COSTS IS
UNCERTAIN
-------------------------------------------------------- Chapter 4:3.1
Although changes in Medicare fee-for-service expenditures will have a
direct effect on risk contract HMOs, increasing HMO market
penetration may have indirect effects on Medicare's costs in the
fee-for-service sector. For example, officials at some HMOs believe
that in areas where HMOs have concentrated, managed care plans have
made the fee-for-service sector more efficient, as the practice
patterns found most commonly in managed care organizations have
spread to the fee-for-service sector. Therefore, these HMO officials
conclude that increased HMO market penetration leads to a decline in
average fee-for-service costs. However, some researchers and
industry analysts have suggested that favorable selection may lead to
the opposite effect--that is, rising HMO market penetration may
increase, not decrease, average fee-for-service costs. These
analysts believe that as HMOs attract more of the relatively
healthier beneficiaries, then those beneficiaries who remain in the
fee-for-service sector are the relatively less healthy and more
costly on average. Nonetheless, research evidence has been unable to
determine if a relationship exists between HMO market penetration and
average fee-for-service costs.
LINK BETWEEN FEE-FOR-SERVICE
COSTS AND HMO PAYMENT RATES IS
PROBLEMATIC
---------------------------------------------------------- Chapter 4:4
Health policy analysts have argued that capitated payments to health
plans should be based not on a fee-for-service standard, as
legislation requires for risk contract HMOs, but on the minimum
reasonable cost of providing appropriate care. Deviations from this
"right price," however, can have costly consequences--in the Medicare
risk contract program, rates that are too high can increase Medicare
spending, while rates that are too low can reduce HMOs' willingness
to accept beneficiaries under a Medicare risk contract.
There is widespread agreement that the current fee-for-service-based
system does not set the right price for HMO care. Without an
established standard for appropriate care, local variations in HMO
costs cannot be distinguished from differences in utilization.
Therefore, many researchers and HMO representatives believe that the
law should be changed to break the link between fee-for-service costs
and HMO reimbursement. These analysts agree that the current rate
setting method perpetuates overpayments in areas where there are
expensive practice patterns and underpayments in areas where
fee-for-service patients are underserved.
In addition, some HMOs and academic experts feel that the link
between HMOs and fee for service will become less viable as more
Medicare enrollees join HMOs. With fewer people in fee-for-service
Medicare, estimates of fee-for-service costs will become more
unstable and unreliable, and small inaccuracies in the rate-setting
calculations or data will have a larger effect on rates. According
to one expert, "the AAPCC is a built-in small system" that is
feasible only because HCFA can observe the vast majority of Medicare
beneficiaries in the fee-for-service sector.
ALTERNATIVE RATE-SETTING
PROPOSALS ARE LARGELY UNTESTED
---------------------------------------------------------- Chapter 4:5
Managed care representatives and academic experts have suggested a
number of alternative rate-setting methods--ranging from
modifications to the current method to a radically redesigned system.
These proposed solutions are largely untested, and evaluating their
cost-effectiveness and administrative feasibility is difficult.
Given that the Congress created the risk contract program to save
money, one HCFA official stated that "it is hard to argue for paying
HMOs more than fee-for-service" under a new system. In addition, any
redesigned system could (if not phased in gradually) disrupt the
relationship between current Medicare enrollees and their providers
in risk contract HMOs.
COMPETITIVE BIDDING
-------------------------------------------------------- Chapter 4:5.1
Several HMO representatives and industry researchers advocate a
competitive bidding process to determine the base HMO payment rate.
Under such a system, HCFA's base reimbursement rate would be
determined by the bids submitted by HMOs. HCFA's rate could be based
on the lowest bid, the average bid, or a more complex formula.
Competitive bidding has been used in Arizona's Medicaid managed care
program but has not been tested on a national basis for Medicare
beneficiaries.
Under competitive bidding, competition among health plans, rather
than fee-for-service practice patterns, would determine HMO
reimbursements. However, competitive bidding presents several
practical difficulties. First, insufficient competition may exist if
too few plans bid. This situation is likely to occur in areas of low
HMO market penetration, such as rural areas. Second, it is unclear
how HCFA would set rates if there were no acceptable bids. For these
reasons, competitive bidding may be most workable in areas with high
HMO penetration rates and high fee-for-service costs.
GEOGRAPHIC RECONFIGURATION
-------------------------------------------------------- Chapter 4:5.2
Rather than breaking the link between HMOs' rates and fee-for-service
expenditures, some researchers and industry representatives have
suggested that HCFA reconfigure HMO payment rates--that is, change
the geographic unit over which rates are calculated. For example,
HCFA could pay one flat rate for a given metropolitan area and its
adjacent rural counties. Others have suggested using a flat rate for
an entire standard metropolitan statistical area, with a special
adjustment for rural areas.\45 However, an empirical study of
specific reconfiguration proposals found that there is likely to be a
trade-off between homogeneity of rates across geographic areas and
stability of rates over time. In other words, smaller areas would
have more unstable rates, as a few high-cost cases could distort
payment rates in a smaller county more than in a larger county.
However, this trade-off may not matter--these researchers discovered
that proposed reconfigurations would have had minimal effects on
rates and concluded that differences in HMO payment rates were too
large to be rectified by reconfiguration.
--------------------
\45 Other proposals for reducing the widespread variation in rates
have been offered as well. One HCFA regional official pointed out
that while HMO payment rates are calculated by county, there are only
two to three different doctor or hospital Medicare payment rates per
state in the fee-for-service sector and suggested that the risk
contract program change to mirror the number of rates per state in
the fee-for-service sector. In addition, the President's proposed
Health Security Act would reduce the differences between the highest
and lowest rates by setting rate ceilings and floors. We have no
data against which to evaluate these proposals.
NEGOTIATION TO DETERMINE HMO
PAYMENTS
-------------------------------------------------------- Chapter 4:5.3
Some authors have suggested government-HMO negotiations--both as a
pricing mechanism and as a mechanism to determine risk adjustors.
Negotiation may provide the government and HMOs with the ability to
set prices according to local conditions. Nonetheless, negotiation
could be expensive, difficult to administer, and vulnerable to
collusion by HMOs.
ECONOMICALLY BASED MODELS
-------------------------------------------------------- Chapter 4:5.4
Some managed care representatives have proposed that the Congress
consider allowing HCFA to set HMO payment rates using an
"economically based model." Under this system, HCFA would base
capitation payments on HMO input costs. However, presently available
data appear insufficient to estimate HMO input costs accurately. In
addition, HMOs differ by size, physician payment methods, and model
type, and their input costs may vary correspondingly. It is unclear
how HCFA could, would, or should account for these differences.
Proposals that HCFA use economically based models to pay HMOs
therefore do not seem viable, at least in the short term.
BLENDED PAYMENT SYSTEMS
-------------------------------------------------------- Chapter 4:5.5
Under another suggested reform scheme, HCFA would pay HMOs a
"blended" rate--a weighted average of the nationwide average base
rate and the payment rate for that HMO's own county, with the weights
dependent on HMO market penetration. Proponents of this strategy
claim that it would reduce rate variability. A blended rate system
would weaken the link between the HMO's capitation rate and local
medical practice patterns, reducing variation in national rates.
However, a blended rate does not ensure appropriate rate variation.
Like the current system, a blended rate system does not distinguish
between true variation in the costs of medical care and variation
caused by underutilization or overutilization.
CONCLUSION AND RECOMMENDATION
---------------------------------------------------------- Chapter 4:6
Although the problems in linking HMO and fee-for-service payments are
widely acknowledged, there is little agreement over proposed
solutions. The range of options is wide, but practical experience
with these other systems is limited or nonexistent. More research,
evaluation, and demonstration of these alternatives is clearly
necessary. To help the Congress address this issue, we recommend
that the Administrator of HCFA direct the agency to conduct
preliminary research on payment methods that could replace the
reliance on fee-for-service reimbursement to determine base payment
rates for HMOs.
RESEARCHERS PROPOSE A NUMBER OF
ALTERNATIVE RISK ADJUSTMENT
METHODS
=========================================================== Appendix I
HOW RISK ADJUSTMENT WORKS
--------------------------------------------------------- Appendix I:1
Although different risk adjustors incorporate different measures of
beneficiaries' health status, each risk adjustment system must
translate this information into HMO payment rates. Base payment
rates can be adjusted for risk information, prospectively or
retrospectively, through a three-step process--measuring the risk
adjustment variables, estimating the relationship between these
variables and health care costs, and then making payment adjustments
based on these relationships.
The first step in any risk adjustment process is to gather data and
measure the risk adjustment variables. HCFA's current HMO payment
system, for example, requires HCFA to gather information on the age,
sex, and Medicaid and institutional status of each beneficiary.
Second, HCFA must estimate the relationship between the set of risk
adjustment variables and the health care costs generated by
beneficiaries. For example, we might expect that an 80-year-old man
would generate higher health care costs than a 65-year-old woman
(other risk factors being equal). However, if risk adjusted payments
are to reflect the true cost of patients' care, HCFA must estimate
the portions of the difference in expected costs that can be
attributed to age and gender. Last, these estimates are used to
adjust HMO payment rates. For example, suppose we estimated that, on
average, a 70-year-old non-Medicaid non-institutionalized man
generates Medicare expenditures at the average rate of all Medicare
patients in the county where he lives and that, on average, an
84-year-old Medicaid institutionalized man generates an estimated 2.4
times the average local Medicare cost. An HMO that enrolled actual
beneficiaries matching these descriptions would be paid the average
base rate for the 70-year-old man, and 2.4 times the average base
rate for the 84-year-old man.
RISK ADJUSTMENT CAN BE
APPLIED PROSPECTIVELY OR
RETROSPECTIVELY
------------------------------------------------------- Appendix I:1.1
The three-step process described above can be applied prospectively
or retrospectively--that is, risk adjustment can be applied to
up-front payments based on the beneficiary's status at the start of
the payment period, or risk adjustment can be applied to adjust
previous payments retrospectively, on the basis of the patient's
status at the end of the payment period. HCFA's current risk
adjustments are applied prospectively. However, HCFA could also
adjust payments according to the beneficiary's status at month's end.
Retrospective adjustments are more often discussed in the context of
reinsurance, where HMOs' payments would be adjusted retrospectively,
based on the actual costs incurred during that period. For example,
HCFA could reimburse HMOs for a portion of any expenses they incurred
above the capitation payment level. These systems are similar to
prospective payments based on previously incurred costs, except that
the adjustment is made retrospectively rather than prospectively.
Table I.1 illustrates the distinction between retrospective and
prospective risk adjustment and shows the difference between
cost-based and health status-based risk adjustment. Under
prospective risk adjustment, payments at the beginning of one year
are based on information from the previous year--while under
retrospective risk adjustment, payments are adjusted at the end of
the year on the basis of information from that same year. Under
cost-based adjustment, payments are based on costs previously
generated by beneficiaries; under health status-based adjustment,
payments to HMOs are based on beneficiaries' health status.
The far-right column of table I.1 reveals the similarity between
retrospective and prospective risk adjustment. At the beginning and
end of the beneficiary's enrollment, the payments to the HMO will
differ under prospective and retrospective adjustment, but during the
overlapping periods, payments are the same in either case.
Differences in HMOs' incentives to minimize costs result not from the
prospective or retrospective nature of the payment per se, but from
the basis for that payment. Payments based on incurred costs can
blunt HMOs' cost-reduction incentives, because an increase in costs
in one period can be recovered, either by retrospective reimbursement
or by higher prospective payments in subsequent periods. Payments
based on health status, however, can compensate HMOs for high-cost
cases while preserving HMOs' incentives to manage health care costs.
Table I.1
Comparison of Prospective and
Retrospective Health Status-Based and
Cost-Based Risk Adjustors
On 1/1/98 On 1/1/99 On 1/1/2000 1/1/97-1/1/2000
-------------- -------------- -------------- -------------- ----------------
Under
Prospective,
Health Status-
Based
Adjustment
Does the HMO Yes Yes No
receive a P(H97) P(H98)
payment?
For what does For care to be For care to be
the HMO provided in provided in
receive this 1998 1999
payment?
What is the Beneficiary's Beneficiary's
basis for the health status health status
payment in 1997 in 1998
received by
the HMO?
What are total P(H97) + P(H98)
payments?
Under
Retrospective,
Health Status-
Based
Adjustment
Does the HMO No Yes Yes
receive a P(H98) P(H99)
payment?
For what does For care For care
the HMO provided in provided in
receive this 1998 1999
payment?
What is the Beneficiary's Beneficiary's
basis for the health status health status
payment in 1998 in 1999
received by
the HMO?
What are total P(H98) + P(H99)
payments?
Under
Prospective,
Cost-Based
Adjustment
Does the HMO Yes Yes No
receive a P(C98) P(C99)
payment?
For what does For care to be For care to be
the HMO provided provided
receive this during 1998 during 1999
payment?
What is the Health care Health care
basis for the costs costs
payment generated by generated by
received by beneficiary in beneficiary in
the HMO? 1997 1998
What are total P(C98) + P(C99)
payments?
Under
Retrospective,
Cost-Based
Adjustment
Does the HMO No Yes Yes
receive a P(C98) P(C99)
payment?
For what does For care For care
the HMO provided in provided in
receive this 1998 1999
payment?
What is the Health care Health care
basis for the costs costs
payments generated by generated by
received by beneficiary in beneficiary in
the HMO? 1998 1999
What are total P(C98) + P(C99)
payments?
--------------------------------------------------------------------------------
ALTERNATIVE RISK ADJUSTORS RELY
ON DIFFERENT MEASURES OF
BENEFICIARIES' HEALTH STATUS
--------------------------------------------------------- Appendix I:2
Medicare's need for a more sophisticated risk adjustor has prompted
industry experts and academic researchers to develop alternative risk
adjustment mechanisms that incorporate a measure of health status.
Although each of these alternative risk adjustors incorporates a
direct measure of health status, they derive these health status
measures from different sources. For example, one potential risk
adjustment method uses beneficiaries' own assessment of their
health--that is, beneficiaries fill out a questionnaire describing
their physical and emotional health, and these self-reported data are
used to risk-adjust payments to health plans. Self-reported data on
lifestyle or socioeconomic risk factors have also been proposed as a
risk adjustor. Some researchers have also considered risk adjustors
that measure health status by looking at data on functional status.
Rather than rely on self-reported data, several risk adjustors use
information derived from medical records (such as diagnoses) to
measure health status. Risk adjustment might also be based on the
beneficiary's prior utilization of health care services--for example,
the number of days the beneficiary spent in the hospital in the past
year. Other researchers have considered using mortality data to risk
adjust payments to health plans.
RISK ADJUSTORS BASED ON
CLINICAL INFORMATION
------------------------------------------------------- Appendix I:2.1
Clinically based risk adjustors would base HMOs' payments on the
clinical diagnoses of their Medicare beneficiaries. Clinically based
measures range from simple to complex. For example, one potential
risk adjustor is formed by adding clinical indicators--simple
indicators of a history of a specific health problem (called a tracer
condition)--to demographic variables. Under this type of system,
health plans would be paid a higher-than-average amount for a patient
with a tracer condition, and similarly, plans would be paid
lower-than-average amounts for patients without the tracer condition.
Tracer conditions would be chosen primarily on the basis of their
relationship to health care costs, and the number of tracer
conditions could vary with the available data. For the Medicare
population, the most frequently suggested tracer conditions are heart
disease, cancer, and stroke.
A mortality adjustment could also be used to risk adjust capitated
payments. To the extent that a sicker patient population translates
into a higher mortality rate, then adjusting this year's payments on
the basis of last year's mortality rates could compensate those HMOs
that treat relatively sicker patients.
More sophisiticated clinical risk adjustors can take into account not
only the presence or absence of a particular disease, but also the
severity of that condition. One such clinical measure is the
ambulatory care groups (ACG) method. By analyzing large data sets,
researchers can identify beneficiaries whose expected medical costs
are similar, although their actual illnesses may be very different.
The ACG system is based on diagnoses gathered from outpatient
records. The ACG system categorizes patients into 51 cost groups
that are based on clinical diagnosis codes and the beneficiary's age
and sex. Categories are based on whether the condition is expected
to persist, the need for specialty care, potential for
hospitalization, likelihood of disability or death, and expected cost
of treatment. The category grouping system allows the ACG
methodology to account for persons with more than one diagnosis.
Several relatively complex clinical risk adjustors were originally
developed to adjust the capitation rates used by some HMOs to pay
their participating physicians. For example, in one of these
adjustment mechanisms, the clinical complexity index (CCI), panels of
participating physicians assign severity codes to medical diagnoses,
according to the resources needed to treat that case. The severity
code assigned to the patient's most severe illness in a given year is
used to measure that patient's health status. This system relies on
data from both the inpatient and outpatient settings.
RISK ADJUSTORS BASED ON
PRIOR UTILIZATION OR
PREVIOUS COSTS
------------------------------------------------------- Appendix I:2.2
Risk adjustors derived from prior utilization would base HMO
capitation payments on the services used by the beneficiary in a
previous period. For example, an HMO might be paid a
higher-than-average amount if a Medicare beneficiary spent several
days in the hospital in the past year, and conversely, the HMO would
receive a lower rate if the Medicare beneficiary had not visited the
hospital or doctor in the previous year. Risk adjustment could be
based on prior health care dollar costs, as well as prior utilization
of health care services. Such cost-based payments could be made
prospectively, based on utilization in prior periods; or a similar
system could make retrospective adjustments. For example, if a
patient incurred high costs last year, the HMO could receive a higher
capitation rate for that enrollee in the following year; or the HMO
could receive an additional payment at the end of this year, but no
higher capitation rate for the next year. Such reinsurance schemes
are sometimes advocated not as risk adjustment schemes, but instead
as mechanisms to promote plan solvency and stability.
RISK ADJUSTORS BASED ON
COMBINATIONS OF PRIOR
UTILIZATION AND CLINICAL
MEASURES
------------------------------------------------------- Appendix I:2.3
Some of the more widely known risk adjustors represent combinations
of clinical and prior utilization measures. For example, one
much-studied combination measure is the Diagnostic Cost Group (DCG)
method. The DCG method combines a clinical measure (diagnosis code
from inpatient data) with a utilization measure (number of days in
the hospital). Beneficiaries are assigned DCG categories based on
how long they stayed in the hospital and the illnesses that brought
them there. For example, a patient who did not visit the hospital
last year, or was hospitalized for fewer than 3 days, or a patient
for whom hospitalization was highly discretionary, would be assigned
to category DCG 0. The most severely ill patients, those whose
illnesses are expected to be the most costly in the future, would be
assigned to DCG 7. By adjusting for conditions in which
hospitalization is more discretionary, the DCG system partially
guards against "upcoding"--that is, the incentive for physicians or
health plans to exaggerate the severity of a patient's illness by
assigning a more serious--and more profitable--diagnosis code.
Another combination measure proposed for use in the Medicare risk
contract program is the Payment Amount for Capitated Systems (PACS)
method. The PACS method combines information on demographics,
inpatient and outpatient utilization, and clinical diagnoses with an
urban/rural dummy variable and the Medicare wage index for that area.
RISK ADJUSTORS BASED ON DATA
GATHERED FROM PATIENTS
------------------------------------------------------- Appendix I:2.4
Although several risk adjustment measures are based on
diagnosis-related information derived from medical records or claims
data, several other risk adjustors stem from data gathered from
patients themselves or from their employers. For example, some
analysts have proposed using information based on lifestyle or
socioeconomic factors that are associated with high medical costs.
For example, measures of smoking, occupation, marital status, and
education have been suggested as risk adjustors.
Other researchers advocate functional status measures, as exemplified
by the Activities of Daily Living or Independent Activities of Daily
Living protocols. These measures examine beneficiaries' ability to
perform various living tasks such as grocery shopping, eating,
dressing, housecleaning, and preparing food. Functional status
information can be gathered from patient surveys.
The most commonly discussed risk adjustor that is based on
information from beneficiaries is a general measure of self-reported
health status, in which patients fill out a questionnaire describing
how they perceive their own health. A common multiple-inventory
questionnaire--the SF-36 developed by researchers at RAND and at the
New England Medical Center's Health Institute--asks patients to
report about many aspects of their physical and emotional health.
For example, the SF-36 questionnaire asks patients whether they feel
depressed, whether they feel fatigued, how well they can function in
their daily tasks, and how they expect their health to change in the
future. In addition, such questionnaires could be used to gather
utilization data--for example, the frequency of physician visits.
Relying on distinct measures of health status, these alternative risk
adjustors may capture different aspects of health. For example, a
questionnaire that asks specific questions about emotional health
could capture variations in mental health status that might not be
apparent from a clinical measure.
CRITERIA FOR EVALUATING
ALTERNATIVE RISK ADJUSTMENT
METHODS
========================================================== Appendix II
Although risk adjustment systems are designed primarily to prevent
biased selection, implementing a risk adjustment scheme will involve
a number of other considerations. For example, risk adjustment
requires an administrative effort to collect and process health
status information, and this collection of risk adjustment data may
also raise questions of patient confidentiality. Because these
implementation issues reflect potentially conflicting goals, no
single yardstick can measure the desirability of alternative risk
adjustors. In addition, available information on these alternative
risk adjustors is qualitative and incomplete. Despite the complex
nature of the problem, however, criteria exist for evaluating
alternative risk adjustment schemes.
RISK ADJUSTMENT VARIABLES MUST
PREDICT HEALTH CARE COSTS
ACCURATELY
-------------------------------------------------------- Appendix II:1
To prevent biased selection, a risk adjustment variable must be able
to predict health care costs. Risk adjustment aims to pay HMOs less
for those patients who are less costly to treat, and to compensate
HMOs for the additional costs of caring for the more seriously ill.
If successful, risk adjustment can thereby limit HMOs' incentives to
enroll only healthy beneficiaries. Successful matching of an HMO's
capitation rate with the treatment costs requires that risk
adjustment variables be closely associated with health care costs.
The stronger this link between the risk adjustment factors and costs,
the more effective the risk adjustor will be in removing HMOs'
incentives to seek favorable selection.
In addition to overall predictive power, a risk adjustor should
identify the most costly cases--that is, ideally, a risk adjustor's
predictive power would extend to the high-cost end of the health care
cost distribution. Because a few high-cost cases account for a
substantial portion of health care costs,\46 HMOs will have
considerable financial incentives not to enroll these high-cost
beneficiaries. A risk adjustment variable that does not identify
these high-cost cases cannot remove this incentive. A risk
adjustment factor that can distinguish between very-high- and
above-average-cost cases may be a more accurate and valuable risk
adjustor than a variable that separates only low-cost and
average-cost cases.\47
--------------------
\46 In fact, 1 percent of the population accounts for an estimated 30
percent of health care costs.
\47 This point is explained more fully in Hornbrook, et al.,
"Adjusting the AAPCC for Selectivity and Selection Bias Under
Medicare Risk Contracts," Advances in Health Economics and Health
Services Research, 1989, p. 115.
PREDICTIVE POWER IS
DIFFICULT TO MEASURE
------------------------------------------------------ Appendix II:1.1
Although predictive power is the most widely accepted and frequently
discussed criterion for evaluating risk adjustors, existing measures
of predictive power are imperfect. Furthermore, such estimates are
frequently misconstrued.
The most frequently used measure of predictive power is the
"R-squared" (R\2 ). R\2 is the coefficient of determination of a
linear regression, often interpreted as the percentage of linear
variation in the dependent variable (in this case, health care costs)
explained by the group of independent variables (in this case, the
risk adjustment variables). R\2 is often interpreted as a measure of
"goodness of fit"--that is, in our case, how well the set of risk
adjustment variables predicts health care costs.
However, as a measure of goodness of fit, R\2 is subject to a number
of qualifications. For example, R\2 will generally increase whenever
an additional explanatory variable is added to the regression,
whether or not the relationship between this additional variable and
costs is meaningful. For this reason, some discussions of predictive
power have used an alternative measure--Theil's adjusted R\2 .
Theil's adjusted R\2 will increase only when the additional variable
has a 50 percent probability of independently influencing the
dependent variable. Another difficulty in using R\2 is that the
increase in R\2 after adding an additional variable is not a reliable
indicator of the predictive power of that additional variable.
Similarly, R\2 values from regressions with different dependent
variables cannot be compared. Nor can the R\2 value tell us whether
the risk adjustor is drawing its predictive power from the high-cost
or the low-cost end of the distribution. R\2 statistics will be
influenced by the amount of variation in the dependent variable as
well. Lastly, R\2 is a measure of goodness of fit only if the
relationship between health care costs and health status is a linear
one.
Because of these difficulties in interpreting the R\2 statistic, a
more balanced assessment of predictive power should supplement the
use of R\2 . Specifically, Theil's adjusted R\2 should be used,
predictive power should be assessed over different subsections of the
distribution, and more complex econometric techniques (such as
non-linear least squares) should be investigated. Without this
additional information on current risk adjustment variables,
estimates of predictive power remain of questionable value.
RISK ADJUSTMENT SYSTEM SHOULD
NOT IMPOSE UNDUE ADMINISTRATIVE
BURDENS ON HCFA OR
PARTICIPATING HEALTH PLANS
-------------------------------------------------------- Appendix II:2
The implementation of any risk adjustment system requires that
beneficiaries' health status be measured, reported to the
administrator of risk adjustment (in this case, HCFA), and translated
into adjusted payment rates. Each of these activities entails a
financial cost, either to HCFA or to participating HMOs. The fewer
resources HCFA must spend to administer the program, other things
being equal, the greater the opportunity for the risk contract
program to achieve cost savings for Medicare. However,
administrative costs may accrue to HMOs, as well as to HCFA. If HMOs
must incur heavy administrative burdens to participate in the risk
contract program, they may be less likely to participate, and this
lack of participation could limit Medicare beneficiaries' access to
an HMO option.
The administrative burden of any proposed risk adjustment system is
difficult to assess before the fact, because it is difficult to
anticipate firms' ability and willingness to respond to government
mandates to provide data. In addition, HMO information systems and
administrative structures are constantly changing as new information
technology is adopted by the HMO industry. With the onset of health
care reform, and the emphasis in many reform proposals on
administrative simplicity, the type of information gathered by HMOs
may change and may also become more standardized. However, different
risk adjustment systems require varying amounts of information. For
example, the various clinically based risk adjustors differ in the
level of detailed medical information they require. In addition, the
information required for some risk adjustment systems may be easier
to gather than the data needed for other methods. For example, data
on services provided outside of the HMO, such as hospital services,
are typically easier to assemble than data on services provided
within the organization. Similarly, the degree to which the risk
adjustment factor changes over time can add to or lighten the
administrative burden. These differences can help us compare the
administrative burdens of alternative risk adjustment systems.
RISK ADJUSTMENT SYSTEMS
INVOLVE BOTH START-UP AND
OPERATING COSTS
------------------------------------------------------ Appendix II:2.1
The administrative cost of implementing a new risk adjustment
mechanism will involve both initial expenses to introduce new
procedures and ongoing operating expenses to maintain the system over
time. For example, HCFA's initial expenses might include estimating
the computer model that would determine the weights applied to
payments; collecting the initial data for the current set of
enrollees; and providing technical support to HMOs in providing this
data. HCFA's operating expenses could include updating the weights
over time, if necessary; gathering subsequent data on enrollees and
gathering new data on new enrollees; and providing ongoing technical
assistance. For HMOs, start-up costs might involve designing a
system to collect risk adjustment data and report this information to
HCFA; operating expenses could include the costs of gathering and
reporting the information.
ADMINISTRATIVE BURDEN FOR
GIVEN RISK ADJUSTOR MAY VARY
ACROSS HMOS
------------------------------------------------------ Appendix II:2.2
Both the start-up costs and the ongoing costs of a new risk
adjustment system could vary considerably across participating HMOs.
Currently, HMOs vary greatly in the sophistication of their
administrative systems and the data they can provide on their
enrollees. For example, several officials of the HMOs we interviewed
indicated that it would be burdensome for them to provide the type of
enrollee information that might be found on an indemnity plan's
claims form. However, HMOs who voluntarily participated in a HCFA
demonstration program, where they were required to provide
hospitalization information to HCFA, reported a surprisingly small
data burden.
HMOs' ongoing costs of gathering data may also vary. Specifically,
some of the variation in HMOs' ability to collect and report enrollee
data may be associated with the HMO's organizational structure. For
example, HMOs organized as individual practice associations (IPAs)
tend to collect more utilization data than staff-model HMOs, and so
they may be better equipped to make such data available to HCFA for
risk adjustment. However, individual medical records are more
decentralized in an IPA setting, where doctors are located in their
own practices, than in a staff-model HMO, where doctors practice
together in a clinic. Gathering data from medical records, then, may
be more difficult and costly for an IPA than for a staff-model HMO.
These potential differences in administrative burdens across HMOs
raise issues of fairness.
RISK ADJUSTMENT PROCESS SHOULD
TREAT PARTICIPATING HMOS FAIRLY
-------------------------------------------------------- Appendix II:3
In our interviews with HMO industry representatives, they expressed
concern that any new risk adjustment system be fair and even-handed
in its treatment of competing HMOs. However, fairness is difficult
to define and even harder to assess. Fairness can reflect the
differential administrative burdens imposed on HMOs, the incentives
HMOs face to prevent illness, and the degree of flexibility given to
HCFA administrators. Indeed, some HMOs' definitions of fairness may
conflict--for example, officials we interviewed at one HMO said that
HCFA should take into account the special circumstances facing
individual HMOs, whereas representatives of another HMO stressed the
importance of a uniform set of rules.
One operational definition of fairness suggests that the risk
adjustment system should be objective--that is, HCFA administrators
should have only limited discretion over the measurement of risk
adjustment variables and the estimation of payment weights. In
addition, a system that is transparent to plans, so that HMOs can
understand exactly how their payment calculations are made, could add
to HMOs' perception of fairness.
Fairness is closely related to other criteria. For example, a risk
adjustor with strong predictive power will be closely related to
HMOs' costs, and so may be fairer to participating HMOs. In
addition, a risk adjustment scheme that does not create great
differences in administrative burdens will likely be perceived as
fair, and a risk adjustment system that is less vulnerable to
manipulation by HMOs will probably also be perceived as fair.
RISK ADJUSTMENT PROCESS SHOULD
NOT BE VULNERABLE TO
MANIPULATION BY PARTICIPATING
HMOS
-------------------------------------------------------- Appendix II:4
A good risk adjustment system should minimize opportunities for HMOs
to manipulate the risk adjustment data or to create favorable
selection. For example, opportunities for fraud and abuse may arise
if data for risk adjustment purposes are gathered directly from HMOs,
without independent verification. In addition, the more transparent
the risk adjustment system is to participating HMOs, the greater
potential for health plans to manipulate the data to increase their
payments.
More likely, some risk adjustment mechanisms are more susceptible to
"within-cell selection" by participating HMOs. Without manipulating
the risk adjustment data, HMOs can select a low-risk patient
population by enrolling the lowest-risk beneficiaries within each
category.\48 For example, if an HMO is paid a higher sum for patients
with cancer, that HMO can try to attract those beneficiaries with the
least severe forms of cancer, or whose cancer is in remission. More
sophisticated risk adjustors can mitigate this problem by adjusting
payments not only for the presence or absence of a particular
condition, but the severity of the illness as well--for example, not
only paying HMOs higher rates for patients with cancer, but also
paying them an additional premium for more severely ill cancer
patients. A more transparent risk adjustment system may be more
vulnerable to manipulation by within-cell selection as well as by
fraud and abuse.
Both potential fraud and within-cell selection could dilute cost
savings for the risk contract program. Fraud would obviously
increase Medicare costs, and HCFA's efforts to avoid fraud could also
be potentially costly. Within-cell selection can have a similar
effect, if sicker beneficiaries are shunted to the fee-for-service
sector and increased favorable selection drives up Medicare costs.
Despite the importance of avoiding these situations, however,
anticipating plans' responses to a new risk adjustment system is
difficult before the new system is in place. Not only are potential
fraud and within-cell selection hard to anticipate before they occur,
they are difficult to observe even when they have taken place.
--------------------
\48 In HCFA's current risk adjustment system, HMOs are paid higher
rates for older beneficiaries. However, HMOs can benefit from
favorable selection by enrolling the youngest and healthiest
beneficiaries within each age category.
RISK ADJUSTMENT SYSTEM MUST BE
COMPATIBLE WITH INCENTIVES TO
PROVIDE APPROPRIATE MEDICAL
CARE
-------------------------------------------------------- Appendix II:5
Researchers and industry experts have expressed concern that risk
adjustment could undermine quality in Medicare HMOs by creating
incentives for HMOs to deviate from an appropriate standard of care.
If Medicare is to provide high-quality care efficiently, HCFA's risk
adjustment system should not reward HMOs for providing too much or
too little care to beneficiaries.
Little agreement exists on a general standard of appropriate care,
nor has consensus been reached on how to judge whether that standard
has been violated after the fact. Evaluating the quality of care
requires making the distinction between the underlying health
condition, which is largely out of the HMO's control, and the effects
of the treatment given by the HMO. An HMO with a low mortality rate,
for example, could have had healthier people to begin with, or it
could have provided high-quality care that made patients more likely
to recover from serious illnesses. Such assessments are more
straightforward when looking at the presence or absence of largely
unpreventable illnesses. If one HMO has more patients with
Parkinson's disease, for example, the difference is unlikely to be
due to differences in the quality of care. However, when the
severity of conditions is considered, it becomes more difficult to
distinguish between the results of the HMO's treatment and the
unavoidable results of the underlying illness. One HMO's Parkinson's
patients may be sicker than those of another HMO, for example, either
because the patients were sicker to start with or because of
differences in the quality of care.
RISK ADJUSTMENT MECHANISM
SHOULD REWARD HMOS FOR
APPROPRIATE PREVENTIVE
EFFORTS
------------------------------------------------------ Appendix II:5.1
If risk adjustment mechanisms pay more to HMOs with sicker people,
then the risk adjustment mechanism may also reduce HMOs' incentive to
prevent costly illnesses. Such preventive disincentives can be
focused--that is, specific to a particular illness--or more general.
For example, a risk adjustor that pays HMOs more for patients with
heart disease may reduce HMOs' incentives to focus preventive
education efforts on beneficiaries with several risk factors for
heart disease. Similarly, a risk adjustor that measures health
status in a more general way may weaken HMOs' incentives to provide
general preventive care and patient education on diet, exercise, and
stress management.
RISK ADJUSTMENT SYSTEM
SHOULD NOT ENCOURAGE
INEFFICIENT MEDICAL PRACTICE
------------------------------------------------------ Appendix II:5.2
Risk adjustment systems can also alter physician practice patterns.
For example, a risk adjustment system that is based only on inpatient
data can change physicians' incentives to substitute hospital
services for outpatient care. A risk adjustment system that
encourages inefficient medical practice can have the unintended
result of increasing health care costs.
RISK ADJUSTMENT PROCESS RAISES
QUESTIONS OF PATIENT PRIVACY
AND CONFIDENTIALITY
-------------------------------------------------------- Appendix II:6
Because risk adjustment requires evaluation of patients' health
status, some risk adjustment methods incorporate individual
beneficiaries' medical information. The need for such data raises
questions of patient privacy and confidentiality for policymakers, or
society in general, to consider.
The invasiveness of a risk adjustor involves several considerations.
First, how much data is collected? Other things being equal, the
less data required, the less invasive the risk adjustment process is.
Second, how sensitive are the data? Although beneficiaries might not
care if their sex is known to HCFA administrators, for example, they
might not want HCFA administrators to have information on their
specific medical conditions, especially if these conditions carry a
social stigma. Third, who has access to the data, and in what form?
If beneficiaries' identities can be shielded from most of those with
access to the data, for example, the privacy issues in the risk
adjustment procedure might be considered less troubling. Similarly,
working Medicare beneficiaries may be concerned if their employers
were to have access to individual health status information.
Finally, how transparent are the data? If the information is
transparent to anyone who observes it, privacy concerns may be more
acute than if specific medical or technical knowledge were required
to decipher the meaning of the information.
EVALUATING ALTERNATIVE RISK
ADJUSTMENT METHODS
========================================================= Appendix III
Because of the trade-offs between desirable goals for risk
adjustment, and because of the limited knowledge in this area, we
were unable to recommend any one risk adjustor. However, using the
criteria described in appendix II, we identified four risk
adjustors--clinical indicators, ambulatory care groups (ACG),
diagnostic cost groups (DCG), and self-reported health status--that
are the most promising candidates for improving the Medicare risk
contract program.
HCFA'S CURRENT RISK ADJUSTMENT
VARIABLES HAVE INSUFFICIENT
PREDICTIVE POWER TO LIMIT
FAVORABLE SELECTION
------------------------------------------------------- Appendix III:1
Under HCFA's current risk adjustment system, payments to risk
contract HMOs are adjusted for the age, sex, Medicaid and
institutional status of the enrollee. This risk adjustment method
has several virtues. First, the current system requires less
information than any other risk adjustor, and most of this
information is available from HCFA files, minimizing the data burden
on participating HMOs. In addition, HCFA's current risk adjustment
mechanism does not require relatively sensitive information on
medical diagnoses, and it is compatible with efforts to provide
appropriate preventive care. However, despite these administrative
strengths, HCFA's demographically based risk adjustment system has
insufficient predictive power to adequately limit favorable
selection. HCFA's current system is vulnerable to considerable
within-cell selection, and the demographic variables HCFA uses are
only loosely associated with health care costs, particularly for
persons with unusually low or high health care costs. The system's
inadequacy in preventing favorable selection indicates that a new
risk adjustment system is needed in the Medicare risk contract
program.
ANY OF SEVERAL ALTERNATIVE RISK
ADJUSTORS WOULD IMPROVE THE
MEDICARE RISK CONTRACT PROGRAM
------------------------------------------------------- Appendix III:2
The need for a more sophisticated risk adjustor to reduce favorable
selection has led analysts and industry experts to propose and test
an array of alternative risk adjustment mechanisms. Although the
research community has been unable to identify a single risk adjustor
that is clearly superior to all others, several of these risk
adjustment choices are more promising than HCFA's current system.
CLINICAL INDICATORS COMBINE
SIMPLICITY WITH PREDICTIVE
POWER, BUT MAY BE SUBJECT TO
MANIPULATION BY HEALTH PLANS
----------------------------------------------------- Appendix III:2.1
A simple clinical measure of health status represents the middle
ground in the trade-off between administrative simplicity and
predictive ability. A clinical-indicator system requires more
information than HCFA's current system--in addition to age, sex, and
Medicaid and institutional status, the risk adjustment process would
include a clinical variable to indicate the presence of specific
health conditions. However, a clinical-indicator risk adjustment
system embodies less information than more complex clinical measures.
These more detailed health status measures include not only
information on the presence of specific conditions, but they also
incorporate information on the severity of the illnesses. Therefore,
a clinical-indicator system would carry a greater administrative
burden than HCFA's current system, but it would impose a smaller
administrative burden than several more complex clinical methods.
The actual amount of information required by a clinical-indicator
risk adjustment system will depend on the number and definition of
the conditions chosen. The choice of conditions could be customized
to local disease burdens, medical practice patterns, and the
information base. This adaptability could be used to mitigate the
potential unevenness of the administrative burden across HMOs.
Several studies have found that clinical indicators that mark the
presence of tracer conditions such as heart attack, cancer, or stroke
are good individual predictors of health costs. The predictive power
of these variables will depend on which tracer conditions are
selected and how many are incorporated. Because these tracer
conditions are specially selected, this measure can be designed to
better predict the high-cost tail of the distribution of health care
costs. (See app. II.)
Thus, compared with HCFA's current method, a clinical-indicator
system can increase predictive power without generating the heavier
administrative cost of more complicated clinical risk adjustment
systems. Nonetheless, clinical indicators may be quite vulnerable to
within-cell selection. Further, the transparency of a
clinical-indicator system may increase HMOs' perceptions of fairness,
but it may also make the payment system more open to fraud and abuse.
A clinical-indicator system may also be subject to issues of patient
privacy, particularly if the tracer conditions carry a social stigma.
In addition, a clinical-indicator system may weaken incentives for
HMOs to provide focused prevention services. For example, a risk
adjustment system that sets higher rates for patients with
osteoporosis might weaken HMOs' incentives to educate patients on
preventive diet and exercise.
DCGS HAVE PREDICTIVE POWER
AND USE EXISTING DATA, BUT
MAY CREATE INAPPROPRIATE
INCENTIVES
----------------------------------------------------- Appendix III:2.2
DCGs combine clinical diagnostic information from hospital stays with
a measure of inpatient prior utilization (hospital days). Because
HMOs tend to collect more information on inpatient stays than on
ambulatory care, DCGs may be easier to implement than comparable risk
adjustors that are based on outpatient data. When HCFA experimented
with DCGs in a small, voluntary demonstration project, participating
HMOs reported only a small data burden.\49 By adding inpatient
information to demographic variables, DCGs achieve a greater
predictive ability than HCFA's current system. DCGs' emphasis on
inpatient care may enhance their ability to predict the high-cost
tail of the distribution of health care costs. Despite this
predictive power, however, DCGs may be vulnerable to within-cell
selection, particularly in the DCG 0 group (enrollees who spent
little or no time in the hospital). In addition, DCGs' reliance on
inpatient data may also be problematic, because it could create
potentially undesirable incentives to substitute hospitalization for
less costly outpatient care.\50
--------------------
\49 Because these HMOs volunteered to participate in the
demonstration, however, their experience may not be representative of
all HMOs in the risk contract program.
\50 These incentives are likely to be mitigated, however, because the
DCG system classifies both patients with no hospital stay and
patients with a hospital stay of less than 3 days in the DCG 0 group.
ACGS HAVE PREDICTIVE POWER
AND MAY BE LESS VULNERABLE
TO MANIPULATION, BUT ENTAIL
GREATER ADMINISTRATIVE COSTS
----------------------------------------------------- Appendix III:2.3
ACGs represent a relatively "high-powered" risk adjustment choice.
They have a number of desirable properties, including good predictive
power and less vulnerability to manipulation than less sophisticated
systems. Specifically, ACGs should provide substantially improved
predictive ability over demographically based risk adjustors such as
HCFA's current system. Limited information is available to evaluate
ACGs' predictive power at the high-cost end of the health care cost
distribution. Although the ambulatory base of these measures could
limit ACGs' ability to predict the high-cost tail of the health care
cost distribution, ACGs' basis in medical diagnoses may alleviate
this problem to some extent. The sophistication of the ACG system
suggests that ACGs may be less vulnerable to within-cell selection
than more transparent risk adjustors.
This sophistication, predictive power, and invulnerability come at a
cost. Because ACGs embody specific and detailed medical diagnosis
codes, implementing an ACG system would require substantial
administrative resources from both HCFA and participating HMOs. The
administrative burden of gathering the required data may be more
pronounced for smaller, staff model HMOs with less elaborate
management information systems.
SELF-REPORTED HEALTH STATUS
CAPTURES MANY DIMENSIONS OF
HEALTH, BUT MAY BE LESS
RELIABLE AND MORE INVASIVE
----------------------------------------------------- Appendix III:2.4
Studies have indicated that adding a variable based on self-reported
health status to demographic factors would also increase predictive
power over HCFA's current risk adjustment system. However,
self-reported health status may draw this predictive power from the
center, rather than the tail, of the distribution; therefore,
self-reported health status might not sufficiently prevent HMOs from
enjoying favorable selection that is based on excluding a few
high-cost cases. Because self-reported health status measures
typically incorporate beneficiaries' answers to a number of health
questions, these risk adjustment mechanisms may be less vulnerable to
within-cell selection, because the cells are not as easily or sharply
defined. In addition, self-reported health status should be
generally compatible with appropriate care, as it does not create
incentives for plans to provide too many or too few services.
Several dimensions of health (particularly emotional health) that can
be captured in self-reported health status measures are not directly
incorporated into other risk adjustment methods. However, because of
the subjectivity of self-reported data, some analysts have raised
questions about the validity of self-reported health status
information. For example, the ability and willingness of Medicare
HMO enrollees to answer health questions accurately may depend on the
enrollee's health or cultural background.
The ability of self-reported health status to capture emotional
health, although undoubtedly contributing to predictive power, could
be considered invasive. Questions about emotional health may be
particularly sensitive, especially to older persons and to those who
consider depression or emotional distress a form of personal
weakness.
Those risk adjustors that are constructed from beneficiary-supplied
information may impose less of a burden on participating HMOs (unless
HMOs are given the responsibility for gathering this data). However,
the administration, compilation, and interpretation of beneficiary
data collection instruments could impose substantial administrative
burdens on HCFA. Researchers have developed and tested several data
collection instruments to measure self-reported health status;
however, considerable work remains on modeling the cost-score
relationship.
OTHER RISK ADJUSTORS
CONSISTENTLY RANKED LOWER ON
OUR EVALUATION CRITERIA
------------------------------------------------------- Appendix III:3
Analysts have suggested several other risk adjustment mechanisms, but
these risk adjustment factors appear to be less suitable for the
Medicare risk contract program. Several of these risk adjustors
would be difficult to administer, and others could create
inappropriate incentives for participating HMOs.
LIFESTYLE AND SOCIOECONOMIC
RISK ADJUSTORS HAVE LIMITED
PREDICTIVE ABILITY AND COULD
CREATE HEAVY ADMINISTRATIVE
BURDENS
----------------------------------------------------- Appendix III:3.1
Lifestyle and socioeconomic measures, which are generally combined
with demographic variables, appear to have greater predictive power
than HCFA's current method, but they are probably less predictive
than the more direct health status measures. In addition, many
lifestyle and socioeconomic variables are subject to the limits of
self-reported data.
Implementing a lifestyle measure could create a heavy administrative
burden for HCFA as well. Considerable efforts have been undertaken
to test self-reported health status questionnaires, but no comparable
well-developed prototype exists for lifestyle measures. Such a
questionnaire might be considered invasive (depending on what was
asked) and could also reduce HMOs' incentives to provide preventive
patient education on diet, exercise, smoking cessation, and other
lifestyle choices.
FUNCTIONAL STATUS WOULD BE
COSTLY TO ADMINISTER, AND
MAY BE INVASIVE
----------------------------------------------------- Appendix III:3.2
However functional status information is gathered, assembling these
data would entail considerable start-up costs in developing and
administering the data collection instrument as well as estimating a
model to link functional status to health care costs. In addition,
functional status information may be considered invasive. In the
literature, functional status variables show greater predictive power
than HCFA's current method, but functional status may not have
superior predictive power compared with other health status
adjustors. And because using functional status involves relatively
large administrative costs, it is unlikely to provide the best risk
adjustment system for the Medicare risk contract program.
PRIOR-UTILIZATION AND
PRIOR-COST MEASURES HAVE
GREATER PREDICTIVE POWER,
BUT CAN CREATE INAPPROPRIATE
INCENTIVES
----------------------------------------------------- Appendix III:3.3
In earlier studies of favorable selection in the Medicare risk
contract program, researchers focused largely on prior-utilization
risk adjustors. Because they are based on past experience,
prior-utilization and prior-cost measures generally have strong
predictive power. However, it is unclear whether prior-utilization
draws its predictive ability from the high-cost tail of the
distribution. One disadvantage of prior-cost or reinsurance measures
is that they may place a heavy administrative burden on participating
HMOs. Moreover, measures of prior use may be vulnerable to
within-cell selection.
Despite the appeal of their predictive power, prior-utilization and
prior-cost models may blunt the cost-saving incentives of managed
care. If capitated rates depend on beneficiaries' incurred costs,
plans will have less incentive to hold down costs, and less efficient
plans will be rewarded with higher payments.
MORTALITY ADJUSTOR FAILS
FAIRNESS TEST AND IS SUBJECT
TO ETHICAL QUESTIONS
----------------------------------------------------- Appendix III:3.4
Although mortality is associated with high health care costs, its
predictive power is limited by its inability to account for illness
that may be chronic and costly, but not life threatening. Setting
rates based on mortality also seems unfair to health plans that care
for patients with long-term, costly illnesses and health plans that
make preventive efforts to lower mortality. Further, a mortality
adjustor raises ethical questions as health plans would have a
financial interest in allowing critically ill patients to die instead
of making every effort to prolong their lives.
(See figure in printed edition.)Appendix IV
COMMENTS FROM THE HEALTH CARE
FINANCING ADMINISTRATION
========================================================= Appendix III
(See figure in printed edition.)
The following are GAO's comments on the Health Care Financing
Administration's letter dated May 31, 1994.
GAO COMMENTS
------------------------------------------------------- Appendix III:4
1. We believe that HCFA's efforts to further refine rate setting and
risk adjustment methods can improve the Medicare risk contract
program. However, although the risk adjustment literature remains
incomplete, we believe that the risk adjustment methods we recommend
are sufficiently well developed to be tested under demonstration
conditions. As a complement to academic research studies, these
demonstrations would provide actual experience with risk adjustment.
The voluntary nature of demonstrations can make interpretation of the
results more difficult, because health plans that volunteer for a
demonstration may differ from those that do not choose to
participate. However, this difficulty can be minimized if
demonstrations are designed to encourage HMO participation.\51 We
believe that demonstration experience is necessary to assess the
administrative feasibility and the reactions of different types of
HMOs to an alternative risk adjustment system.
2. We agree that provisions of the President's proposed Health
Security Act could reduce favorable selection into Medicare HMOs.
However, the potential effectiveness of the provisions of the Health
Security Act cannot be determined with available data. For example,
while setting rate floors and ceilings would reduce the rate
variation in HMO payment rates, rate ceilings and floors do not
necessarily make HMO rates correspond more closely to the costs of
appropriate care. Also, although streamlining the enrollment process
might be expected to reduce favorable selection, its effectiveness
may be limited by practical considerations. For example, the
enrollment provisions for those who are newly eligible for Medicare
may have a limited impact if HMOs in a given local area face capacity
constraints. In addition, some proposed changes in the enrollment
process might have other disadvantages--for example, extended lock-in
may dissuade seniors from joining an HMO and could limit
beneficiaries' ability to exit an HMO that offered low-quality
care.\52 For these reasons, although we recognize the potential value
of these changes, we believe that an improved payment system will
also be required if the Medicare risk contract program is to realize
cost savings.
3. We explain that favorable selection into HMOs can result from the
actions of either the HMO or the Medicare beneficiary
(self-selection). We clearly do not suggest that favorable selection
results entirely from HMO actions. Nonetheless, we have added an
additional disclaimer to p. 20, which states that we did not
evaluate the degree to which each of these factors contributes to the
favorable selection observed in the Medicare risk contract program.
Further, we did not find a consensus in the research literature on
this issue, nor were we able to use statistical methods to isolate
the factors that affect the enrollment and disenrollment decisions.
4. We believe that using a range of estimates to describe the cost
impact of favorable selection is not misleading. In our opinion,
several research efforts we reviewed were methodologically sound, and
they generated different cost estimates. Rather than single out any
one estimate, to the exclusion of all others, we chose to report the
full range of research studies. In addition, by reporting the impact
of favorable selection as a range, we could better convey the
uncertainty surrounding estimates of the cost impact of favorable
selection.
--------------------
\51 HCFA asserts in its letter that legislation would be needed to
permit demonstrations that would "create winners and losers relative
to the current system." However, HCFA can encourage participation in
voluntary demonstration projects by compensating HMOs for additional
administrative costs incurred and by designing demonstrations so that
health plans are not paid less under the demonstrations than under
the current payment system. In our recommendation, we call for such
a research design.
\52 Although we recognize that low-quality care has not been a
general problem in the risk contract program, the few seniors who may
encounter an isolated incident of low-quality care would be unable to
change their situation.
EXCERPTS FROM HCFA TECHNICAL
COMMENTS, WITH GAO EVALUATION
------------------------------------------------------- Appendix III:5
In addition to its overall comments, the Health Care Financing
Administration provided GAO with a number of additional comments that
were more technical in nature. Many of these comments were
incorporated into our report. However, some of HCFA's technical
comments revealed disagreement on substantive matters or potential
misunderstanding of our views. We have excerpted those comments
below, with our response or clarification to each.
HCFA COMMENT
----------------------------------------------------- Appendix III:5.1
In commenting on the variation in HMO payment rates, HCFA states:
"Since variations in adjusted average per capita cost (AAPCC) rates
reflect fee-for-service costs in an area, some differences between
certain areas are justifiable--for example differences between
payment rates in New York City and rural Montana could be justified
by New York's higher costs of doing business."
GAO EVALUATION
----------------------------------------------------- Appendix III:5.2
We have concluded, after reviewing the available evidence, that the
variation in HMO payment rates is inappropriate. This conclusion
stems from the extent of the rate variation and the incorporation of
utilization patterns into HMO payment rates. Although, as HCFA
points out, some of the rate variation comes from variation in the
cost of providing services, the variation in HMO payment rates
exceeds other measures of the cost of providing services. Although
there is no universal agreement on what constitutes an appropriate
rate, there is a general consensus that the current variation in HMO
payment rates is inappropriate.
HCFA COMMENT
----------------------------------------------------- Appendix III:5.3
"GAO's draft states that HMOs have a strong financial incentive to
attract the healthiest possible Medicare clientele. We would like to
point out that the Medicare risk contract program requires the HMO to
conduct an annual open enrollment where Medicare beneficiaries may
enroll without regard to health status."
GAO EVALUATION
----------------------------------------------------- Appendix III:5.4
The fact that Medicare HMOs are required to have an open enrollment
period does not mitigate the HMO's financial incentive to enroll only
healthy clients, although it may make efforts to do so more
difficult. From over a decade of consistent research results, we
have concluded that the open enrollment requirement has proven
insufficient to prevent favorable selection from increasing
Medicare's costs.
HCFA COMMENT
----------------------------------------------------- Appendix III:5.5
In a reference to the discussion on p. 19, HCFA's technical comments
suggest that "[GAO] should also mention that a large number of
Medicare enrollees of risk HMOs are age-ins--that is, individuals
enrolled in the HMO through an employer connection who retain their
HMO membership on becoming eligible for Medicare. .... GAO might
want to give further attention to the question of how much favorable
selection can be attributed to the number of Medicare enrollees who
are "age-ins" and may have better than average health."
GAO EVALUATION
----------------------------------------------------- Appendix III:5.6
As we discussed in our response to HCFA's general comments, we did
not examine the degree to which favorable selection is caused by
actions of the beneficiaries or by the actions of HMOs. Our
interviews with participating HMOs would suggest that age-ins are
important to some HMOs but unimportant to others.
HCFA COMMENT
----------------------------------------------------- Appendix III:5.7
In its technical comments, HCFA disputed our statement that "As long
as the HMO has more information on its enrollees than the payer, the
HMO will have the opportunity to discriminate among enrollees based
on health status." (See p. 27.) HCFA commented that "Medicare HMOs
would only have health status information on individuals not enrolled
in the HMO if the HMO's providers or physicians have treated a
patient and are providing such information to the HMO. Again, to
health screen on the basis of such information is illegal.
(Admittedly, HCFA would have difficulty learning about an HMO
physician who discourages his or her sicker patients from joining an
HMO in which the physician is a participating physician.)"
GAO EVALUATION
----------------------------------------------------- Appendix III:5.8
As HCFA's comment points out, HMOs would generally not have the
medical records of individuals not enrolled in the HMO. However,
HMOs could obtain health status information on would-be enrollees
from simple observation. More important, HMOs could seek favorable
selection by encouraging sicker beneficiaries to disenroll from the
HMO.
HCFA COMMENT
----------------------------------------------------- Appendix III:5.9
"We don't understand the statement that self-reported health status
creates fewer opportunities for within-cell selection than simple
clinical measures. There is a lot of unexplained variation with
self-reported health status, and, thus, plenty of chances for
selection."
GAO EVALUATION
---------------------------------------------------- Appendix III:5.10
We believe that this comment confuses unexplained variation with
opportunities for within-cell selection. Unexplained variation is
necessary, but not sufficient, for within-cell selection to take
place. For an HMO to practice within-cell selection, the unexplained
variation must be systematic and predictable by the HMO. This is
more likely in the case of simple clinical measures, where the HMO
will have information not only on who fits into which category, but
also on the possible cost of care for each person within a category.
With self-reported health status, by contrast, the categories are
"fuzzy" to the HMO and (if the questionnaire is administered by an
independent party) the HMO does not know which beneficiary is in
which category.
HCFA COMMENT
---------------------------------------------------- Appendix III:5.11
"There may be some disagreement as to whether the DCG methodology is
administratively feasible given HCFA's demonstration experience with
the methodology."
GAO EVALUATION
---------------------------------------------------- Appendix III:5.12
Our conclusion that the DCG methodology is administratively feasible
is supported by the existing DCG literature, our interviews with risk
adjustment experts, and the independent evaluation of HCFA's DCG
demonstration by Mathematica Policy Research, Inc.
HCFA COMMENT
---------------------------------------------------- Appendix III:5.13
On page 32, HCFA asked, "What is the basis for stating that these
methods have `sufficient predictive power,'" given that "the report
indicates that assessing predictive power is difficult because it is
not easy to measure accurately."
GAO EVALUATION
---------------------------------------------------- Appendix III:5.14
Although predictive power is difficult to measure precisely, we
believe that the existing literature is sufficient to allow some
qualitative conclusions about the relative predictive power of
alternative risk adjustors. These conclusions are explained in
chapter 3 and in appendix III.
HCFA COMMENT
---------------------------------------------------- Appendix III:5.15
With respect to figure 4.1, HCFA commented that "Standing alone, the
statement that the `mean HMO payment is $310.02 ...' is somewhat
misleading. It should be accompanied by some explanation of the fact
that the majority of HMO enrollees are not institutionalized or
Medicaid eligible, so the average per capita Medicare payment to
plans is less than the county AAPCC (because many enrollees have a
demographic factor of less than 1.0)."
GAO EVALUATION
---------------------------------------------------- Appendix III:5.16
We indicate that the rates we quote are those paid for a man aged 70
to 74, without Medicaid or institutionalized status. Data for "the
typical HMO enrollee's demographic factor" would be not only
difficult to calculate, but also less meaningful to the reader,
because it would not apply to any individual enrollee. In addition,
the rates are given in the context of their variation, and the
variation in rates would be the same for each level of the
demographic factor.
HCFA COMMENT
---------------------------------------------------- Appendix III:5.17
With respect to rate differences in adjacent or nearby counties, HCFA
comments: "Medicare risk HMOs apply for contracts on a
county-by-county basis--the HMO asks HCFA for the county, not the
other way around. Therefore, the fact that an HMO has requested to
have a county included in its geographic area precludes it from
claiming it's adversely affected when beneficiaries in that county
exercise their right to services within the HMO's geographic area."
GAO EVALUATION
---------------------------------------------------- Appendix III:5.18
HCFA is correct. However, although the HMO can choose its Medicare
risk contract service area, it may be impractical from a business
standpoint for an HMO to define its service area too narrowly. In
addition, it is nonetheless true that an HMO can be paid very
different sums for the care of similar individuals. Our interviews
with participating HMOs and the results of several research
studies\53 led us to conclude that variation in adjacent county rates
may make HMOs more reluctant to participate in the risk contract
program.
--------------------
\53 See Mathematica Policy Research, Inc., What Makes HMOs Drop Their
Medicare Risk Contracts?, report to the HCFA (May 1992); Frank W.
Porell and Christopher Tompkins, "Medicare Risk Contracting:
Identifying Factors Associated with Market Exit," Inquiry (Summer
1993), 30 (2), 157-169; and Cynthia Polich with L. Iversen and C.
Oberg, "Risky Business: An Examination of TEFRA Risk HMOs and Their
Risk Contracting Experience," InterStudy Center for Aging and
Long-Term Care, June 1987.
HCFA COMMENT
---------------------------------------------------- Appendix III:5.19
With respect to the discussion of rate stability and accuracy on p.
38, HCFA pointed out that "a HCFA sponsored study by Frank Porell
examined alternatives to the 5-year moving average and found no
alternative that was clearly superior in terms of stability and
accuracy."
GAO EVALUATION
---------------------------------------------------- Appendix III:5.20
GAO examined the Porell study in the course of our review.\54
Given the trade-off between stability and accuracy in rate setting,
we agreed that no one forecasting method was unquestionably superior
to all others. In our discussion, we simply point out a trade-off
between stability and accuracy, without recommending a change in
HCFA's forecasting method.
--------------------
\54 See Frank W. Porell, Christopher P. Tompkins, and Winston M.
Turner, "Alternative Geographic Configurations for Medicare Payments
to Health Maintenance Organizations." Health Care Financing Review,
Spring 1990, 11 (3), pp. 17-30.
HCFA COMMENT
---------------------------------------------------- Appendix III:5.21
Referring to our discussion on p. 39, HCFA commented: "The second
paragraph states: `... some researchers and industry analysts have
suggested that favorable selection may lead to the opposite
effect--that is, rising HMO market penetration may increase, not
decrease, average fee-for-service costs.' We're unfamiliar with this
argument."
GAO EVALUATION
---------------------------------------------------- Appendix III:5.22
This argument is drawn from the mathematics of the HMO rate setting
formula. Because HMO rates are based on fee-for-service costs, as
healthy individuals leave the fee-for-service sector for HMOs,
average fee-for-service costs must increase. If successive HMOs are
able to attract the healthiest candidates in the remaining
fee-for-service pool, then increasing HMO market penetration will
result in higher average fee-for-service costs. A more extensive
discussion can be found in Mathematica Policy Research, Inc., Biased
Selection in the TEFRA HMO/CMP Program, report to HCFA (Sept. 21,
1990).
HCFA COMMENT
---------------------------------------------------- Appendix III:5.23
HCFA suggested that "a discussion of why competitive bidding is
successful in the private sector (but would not be for Medicare)
would have been helpful."
GAO EVALUATION
---------------------------------------------------- Appendix III:5.24
In our discussion, we highlighted some of the practical difficulties
with competitive bidding in the Medicare program. However, we do not
believe that sufficient evidence exists to conclude definitively that
competitive bidding would not be successful for Medicare under any
circumstances.
ACKNOWLEDGMENTS
=========================================================== Appendix V
We would like to acknowledge the assistance of the following
individuals and organizations. Representatives of the institutions
cited and the individuals noted provided valuable assistance or
insights on the issues discussed in this report. However, these
organizations and individuals do not necessarily endorse the
positions taken in the report.
Aetna Health Plan of Southern California, Inc., San Bernardino,
California
American Managed Care and Review Association, Washington, D.C.
Blue Cross/Blue Shield Association, Center for Health Economics and
Policy Research, Chicago, Illinois
Capitol Health Care/Health Maintenance of Oregon, Salem, Oregon
Department of Health and Human Services, Office of the Assistant
Secretary for Policy and Evaluation, Washington, D.C.
Randall P. Ellis, Ph.D., Associate Professor, Department of
Economics, Boston University, Boston, Massachusetts
Fallon Community Health Plan, West Boylston, Massachusetts
Family Health Plan, Inc., Miami Lakes, Florida
Dana Gelb-Safran, Sc.D., Senior Policy Analyst and Political Liaison,
The Health Institute, New England Medical Center, Boston,
Massachusetts
Group Health Association of America, Washington, D.C.
Group Health Cooperative of Puget Sound, Seattle, Washington
Group Health, Inc., Minneapolis, Minnesota
Harvard Community Health Plan, Wellesley, Massachusetts
Health Care Financing Administration:
Boston Regional Office
Bureau of Data Management and Strategy
Office of the Actuary
Office of Coordinated Care Policy and Planning
Office of Managed Care
Office of Prepaid Health Care Operations and Oversight
Office of Research and Demonstrations
Seattle Regional Office
Health Chex, Inc., Fairport, New York
Kaiser Foundation Health Plan, Inc., Oakland, California
Harold Luft, Ph.D., Professor of Health Economics, Institute of
Health Policy Studies, University of California, San Francisco, San
Francisco, California
The Marshfield Clinic, Marshfield, Wisconsin
Mathematica Policy Research, Inc., Princeton, New Jersey
Thomas McGuire, Ph.D., Associate Professor, Department of Economics,
Boston University, Boston, Massachusetts
Mid-Atlantic Medical Services, Inc., Rockville, Maryland
Medicare Advocacy Project, Los Angeles, California
PacifiCare Health Systems, Inc., Cypress, California
Charles Phelps, Ph.D., Chair, Department of Community and Preventive
Medicine, University of Rochester, Rochester, New York
Rochester Community Independent Practice Association, Rochester, New
York
William Rogers, Ph.D., Senior Policy Analyst, The Health Institute,
New England Medical Center, Boston, Massachusetts
United HealthCare Corporation, Minnetonka, Minnesota
Watts Health Foundation, Inc./United Health Plan, Inglewood,
California
MAJOR CONTRIBUTORS TO THIS REPORT
========================================================== Appendix VI
Jonathan Ratner, Assistant Director
Scott L. Smith, Assistant Director, (202) 512-7119
Sarah L. Glavin, Project Manager, (202) 512-7180
Julie C. Cantor-Weinberg, Evaluator
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Ways and Means, U.S. House of Representatives, May 8, 1990.
Whitney, Earl L., and Joseph N. Romano. "Medicare's Risky
Business." Research Report, Milliman and Robertson, Inc., Consulting
Actuaries, 1987.
Why Do the Medicare Risk Plans of HMOs Lose Money? Mathematica
Policy Research, Inc., report to HCFA, May 1992.
Why Do So Few HMOs Offer Medicare Risk Plans in Rural Areas?
Mathematica Policy Research, Inc., report to HCFA, June 2, 1992.
Wilensky, Gail R., and Louis F. Rossiter. "Coordinated Care and
Public Programs." Health Affairs, Winter 1991, 62-77.
Zarabozo, C. "Medicare Risk Contracting," in The Managed Care
Handbook, Peter R. Kongstvedt, ed., Gaithersburg, Maryland: Aspen
Publishers, 1989, 249-262.
RELATED GAO PRODUCTS
============================================================ Chapter 2
Managed Health Care: Effect on Employers' Costs Difficult to Measure
(GAO/HRD-94-3, Oct. 19, 1993).
Medicare: Increase in HMO Reimbursement Would Eliminate Potential
Savings (GAO/HRD-90-38, Nov. 1, 1989).
Medicare: Reasonableness of Health Maintenance Organization Payments
Not Assured (GAO/HRD-89-41, Mar. 7, 1989).
Medicare: Health Maintenance Organization Rate Setting Issues
(GAO/HRD-89-46, Jan. 31, 1989).
Medicare: Physician Incentive Payments by Prepaid Health Plans Could
Lower Quality of Care (GAO/HRD-89-29, Dec. 12, 1988).
Medicare: Experience Shows Ways to Improve Oversight of Health
Maintenance Organizations (GAO/HRD-88-73, Aug. 17, 1988).
Medicare: Uncertainties Surround Proposal to Expand Prepaid Health
Plan Contracting (GAO/HRD-88-14, Nov. 2, 1987).
Medicare: Issues Raised by Florida Health Maintenance Organization
Demonstrations (GAO/HRD-86-97, July 16, 1986).