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The American Journal of Managed Care June 2011
Disease Management Programs in Type 2 Diabetes: Quality of Care
Heiner K. Berthold, MD, PhD; Kurt P. Bestehorn, MD; Christina Jannowitz, MD; Wilhelm Krone, MD; and Ioanna Gouni-Berthold, MD
Impact of Persistence With Infliximab on Hospitalizations in Ulcerative Colitis
Chureen T. Carter, PharmD, MS; Henry Leher, PhD; Paula Smith, MS; Daniel B. Smith, MA; and Heidi C. Waters, MBA
Costs of Asthma Among US Working Adults
Rahul Shenolikar, PhD; Xue Song, PhD; Julie A. Anderson, MPH; Bong Chul Chu, PhD; and C. Ron Cantrell, PhD
Identifying Favorable-Value Cardiovascular Health Services
R. Scott Braithwaite, MD, MS; and Sherry M. Mentor, MPH
Better Continuity of Care Reduces Costs for Diabetic Patients
Chi-Chen Chen, PhD; and Shou-Hsia Cheng, PhD
Shared Medical Appointments in a Residency Clinic: An Exploratory Study Among Hispanics With Diabetes
Natalia Gutierrez, MD; Nora E. Gimpel, MD; Florence J. Dallo, PhD, MPH; Barbara M. Foster, PhD; and Emeka J. Ohagi, PhD, MPH
Hospital Market Concentration, Pricing, and Profitability in Orthopedic Surgery and Interventional Cardiology
James C. Robinson, PhD
Currently Reading
The Structure of Risk Adjustment for Private Plans in Medicare
Joseph P. Newhouse, PhD; Jie Huang, PhD; Richard J. Brand, PhD; Vicki Fung, PhD; and John Hsu, MD, MBA, MSCE
Emergency Department Visits in Veterans Affairs Medical Facilities
S. Nicole Hastings, MD, MHSc; Valerie A. Smith, MS; Morris Weinberger, PhD; Kenneth E. Schmader, MD; Maren K. Olsen, PhD; and Eugene Z. Oddone, MD, MHSc
Shared Medical Appointments in a Residency Clinic: An Exploratory Study Among Hispanics With Diabetes
Natalia Gutierrez, MD; Nora E. Gimpel, MD; Florence J. Dallo, PhD, MPH; Barbara M. Foster, PhD; and Emeka J. Ohagi, PhD, MPH

The Structure of Risk Adjustment for Private Plans in Medicare

Joseph P. Newhouse, PhD; Jie Huang, PhD; Richard J. Brand, PhD; Vicki Fung, PhD; and John Hsu, MD, MBA, MSCE
Health plan accounting data are used to test how well the CMSHCC risk adjustment system tracks relative costs of treating various diagnoses: not very well.

Medicare bases its risk adjustment method for Medicare Advantage plan payment on the relative costs of treating various diagnoses in traditional Medicare. However, there are many reasons to doubt that the relative cost of treating different diagnoses is similar between Medicare Advantage plans and traditional Medicare, including the varying applicability of care management methods to different diagnoses and the varying degrees of market power among suppliers of services to plans. We use internal cost data from a large health plan to compare its cost of treating various diagnoses with Medicare’s reimbursement. We find substantial variability across diagnoses, implying that the current risk adjustment system creates incentives for Medicare Advantage plans to favor beneficiaries with certain diagnoses, but find no consistent relationship between the costliness of the diagnosis and the difference between reimbursement and cost.

(Am J Manag Care. 2011;17(6):e231-e240)

The Centers for Medicare and Medicaid Services–Hierarchical Condition Categories (CMSHCC) system that Medicare uses to reimburse health plans establishes relative prices for different diagnoses based on fee-for-service system data. This makes the implicit assumption that health plans reduce costs equiproportionately across diagnoses. This article tests that assumption.


  • We overwhelmingly reject that the relative cost of diagnoses in the health plans in our sample is the same as that in the CMS-HCC.


  • The magnitude of the errors is large, exceeding 100% for some HCCs.


  • This is particularly true for uncommon conditions for which certain providers have substantial market power but Medicare uses lower administered prices.
For a quarter of a century, Medicare has risk adjusted its payments to private plans that accept at-risk contracts and participate in Part C of the Medicare program, now known as Medicare Advantage (MA). Risk adjustment means Medicare pays plans more for enrollees who are expected to use more services and less for enrollees who are expected to use fewer services, thereby better matching enrollee reimbursement to expected use and, most important, minimizing plan incentives to select against the sick. For example, Medicare pays more for a beneficiary with cancer than for a beneficiary with no chronic disease.

Medicare’s initial risk adjustment system, introduced in 1985, accounted for only the enrollee’s age, sex, county of residence, institutional status, Medicaid eligibility (for the noninstitutionalized), and whether a working beneficiary had employment-based insurance that was primary. Although Medicare paid plans more for enrolling older beneficiaries than younger beneficiaries, reflecting their greater medical spending, the risk adjustment system at that time included no direct measures of health status.

Without adequate risk adjustment, a plan could make money by attracting low-cost healthy beneficiaries with a tightly managed low-premium product, whereas a plan enrolling high-cost beneficiaries would not be paid commensurately more and could lose money.1 In particular, if expected profitability varies with the health status of the beneficiary, plans have incentives to manage care for different services more or less aggressively. Services that are predictable by the beneficiary (and can be used as a device for selection), that are predictive of total medical costs, and that are less profitable are the services a plan would cut back on or manage aggressively to deter enrollment by enrollees who want those services, and conversely.2 Ellis and McGuire2 provide evidence that service-level selection is operative in Medicare.

Because plan reimbursement was set by Congress in 1985 at 95% (later 100%) of predicted spending in traditional Medicare (TM) in the beneficiary’s county, the enrollment of those whose utilization was less than this amount resulted in increased overall spending by Medicare.3-7 For example, the Congressional Budget Office4 estimated in 1994 that Medicare was paying 8% more to private plans than it would have paid had the same beneficiaries instead been enrolled in TM. In 2006, Medicare changed from paying plans a take-it-or-leave-it price to a bidding system, but the bids were for an average risk mix, and the actual monies paid to the plans continued to be adjusted for the risk mix of the plan’s actual enrollees. Therefore, to the degree the risk adjustment system remains inadequate, the MA program continues to be vulnerable to selection problems.

In response to recommendations of the Prospective Payment Assessment Commission and the Physician Payment Review Commission (the predecessors of the Medicare Payment Advisory Commission), the Balanced Budget Act8 mandated that Medicare introduce a risk adjustment method that would account for the beneficiary’s health status in addition to demographic variables, such as age and sex, that it already used. In response, the Centers for Medicare and Medicaid Services (CMS [née Health Care Financing Administration]) contracted for the development of a risk adjustment method that became known as CMS–Hierarchical Condition Categories (CMS-HCCs). In addition to most of the demographic factors that the prior risk adjustment system had used, this method also adjusted plan reimbursement on the basis of diagnoses recorded on claims from the prior year.

In 2000, an initial version of this method, based solely on diagnoses from inpatient claims, was implemented.9 This initial version was used through 2003, but to avoid an incentive to hospitalize a beneficiary simply to record a diagnosis and obtain higher reimbursement, the method initially applied only to 10% of the reimbursement to plans; the remaining 90% was based on the prior system that did not use diagnostic data. Starting in 2004, diagnostic data from outpatient claims were considered reliable enough to use to reimburse, and a transition began such that the new system of CMS-HCCs, which used diagnoses recorded in both the inpatient and outpatient settings, by 2007 applied to 100% of the Part C payment.10 Although the developers initially distinguished 189 categories of conditions, the final set of CMS-HCCs that CMS implemented had only 70 categories, which balanced concerns about coding feasibility, adequate sample size, and predictive accuracy.

The principal task of the new risk adjustment method was to distinguish how much more or less expensive beneficiaries with a given diagnosis or diagnoses were to treat (in terms of total Medicare-covered services) relative to beneficiaries with other diagnoses. A key problem that CMS and its contractor faced in answering this question was that the data to determine the relative costliness of those with various diagnoses were available only from TM. Using data on relative costs in TM to pay for treatment in MA presumes that the relative cost of diagnoses in the managed system has the same structure as TM (ie, any reduction in cost from care management is equiproportionate across all diagnoses or CMS-HCCs).

The equiproportionality assumption is unlikely to hold for several reasons. We have known for many years that between-area variation in rates of procedures is related to the degree of physician discretion in treating the condition.11-15 For diagnoses with less discretion in choice of treatment, there is less variation across areas. This finding seems likely to carry over into managed care as well, meaning that diagnoses with little discretion will be treated similarly to TM and those with more discretion may be treated in a less costly fashion. For example  plans could selectively contract with those physicians and hospitals that are more conservative in their management of conditions with more physician discretion or could place them in a favorable tier such that patients would pay less to use them.

In addition, health plan interventions in the care process, such as integration of care, utilization management, and prior authorization, almost certainly affect different conditions differently. For example, in the case of chronic problems, long-term compliance with a treatment regimen is often important, and health plans can influence compliance with prescribed medication for a chronic disease, such as diabetes mellitus, through benefit design and disease management.16 Such interventions are unlikely to be appropriate or used for emergency treatment, such as that of an aortic dissection. In general, acute problems tend to be less amenable to care management than chronic problems because rapid action by the physician on the scene may be important in treating an acute problem and no intervention by a third party may be feasible.

Furthermore, the number and effectiveness of potential treatment options vary across medical conditions, limiting the ability of any plan to achieve gains through more efficient care or better contracting. At one extreme, if there is only a single treatment option available nationally from a single manufacturer, the cost of this option may not vary substantially. At the other extreme, when there are multiple treatment options with comparable effectiveness (eg, diuretic prescription drugs), plans could obtain favorable prices through negotiation and encourage greater use of the highest-value option through care management programs. Even if there is only 1 standard treatment option, its cost may vary among local providers (eg, procedures performed at teaching vs nonteaching hospitals). Furthermore, if the degree of improvement in compliance that managed care can achieve and the resulting reduction in cost varies across chronic conditions, as is surely the case, the equiproportionate assumption would also fail.

One dramatic, but dated, demonstration of differences in treatment patterns between managed and “unmanaged” care comes from the RAND Health Insurance Experiment,17 which randomized families of beneficiaries younger than 65 years to a staff model health maintenance organization (HMO) in the 1970s and others to an indemnity insurance plan like TM that had no utilization management features (but had cost sharing similar to the staff model HMO). Those assigned to the HMO had similar ambulatory visit rates but 40% lower hospitalization rates and, using the same set of price weights for specific services to impute spending, 28% less spending.17,18 Therefore, for diagnoses that do not customarily result in hospitalization, the CMS-HCC weights that one would have estimated using data on the cost incurred by the staff model HMO probably would have differed little from the weights estimated using data from those with indemnity insurance, whereas for many of the diagnoses with nontrivial proportions hospitalized, the weights would have differed substantially simply from the lower frequency of hospitalization.

Finally, health plans contract with providers who have varying degrees of market power. Particularly for rare or unusual diagnoses, there may be only 1 local or even only 1 regional provider. That provider can probably obtain higher reimbursement from managed care plans relative to Medicare’s administratively set reimbursement compared with other diagnoses for which there are more numerous local providers, thus giving plans a stronger negotiating position.

In short, the relative price structure that Medicare uses to reimburse at-risk MA plans almost certainly contains errors, because the cost structure of health plans across persons with various diagnoses likely differs substantially from that of TM. The aim of this study was to determine how different the structure of the risk adjustment scheme would be if it were based on health plan costs rather than data from TM and whether the scheme creates incentives to select the healthy and avoid the sick. We do not examine actual selection by plans or beneficiaries.


We studied more than 300,000 Medicare beneficiaries enrolled in risk contracts in a large MA-HMO plan during 2006 and 2007. This MA-HMO insurer offered multiple types of benefit arrangements to its beneficiaries. The MA risk adjustment system does not adjust for differences in benefit arrangements between MA and TM, and neither do we. Data from 2005 provided prior year diagnoses for 2006. We included beneficiaries who had aged into Medicare eligibility by the beginning of the study year; we excluded the institutionalized because of small numbers. The 2006 cohort consisted of 322,237 persons, and the 2007 cohort comprised 336,507 persons, as the plan gained enrollment over this period.

Per our protocol, we have masked the identity of the plan. As is generally the case, the hospitals and physicians in the MA-HMO we studied treat both Medicare and commercially insured enrollees; therefore, the mix of providers and capital equipment are configured to treat both types of enrollees. In other words, Medicare reimbursement is not the only influence on the choices of inputs and in turn on the cost structure by disease.

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