Principal Inpatient Diagnostic Cost Group Model for Medicare Risk Adjustment

Health Care Financing Review, Spring, 2000 by Gregory C. Pope, Randall P. Ellis, Arlene S. Ash, Chuan-Fen Liu, John Z. Ayanian, David W. Bates, Helen Burstin, Lisa I. Iezzoni, Melvin J. Ingber

INTRODUCTION

The goal of implementing health-status-based risk adjustment for Medicare capitation payments is to fairly compensate health plans for the expected costs associated with the disease burden of their enrollees. In support of this BBA mandate, HCFA has been collecting inpatient encounter data from health plans with discharges occurring since July 1997. These data include diagnoses and other information that can be used for risk adjustment. Risk adjustment will initially be based only on inpatient diagnoses.

The current PIPDCG model is the culmination of more than a decade of research supported by HCFA (Ash et al., 1989; Ellis and Ash, 1995; Ellis et al., 1996). Previous publications describe analyses of many methodological issues and alternative models. Here, we describe the specific model developed for year 2000 implementation and assess its performance. More details on development of the PIPDCG payment model are available in Pope et al. (1999). The physician co-authors have discussed clinical classification and other issues elsewhere (Iezzoni et al., 1998).

In this article, we first describe and briefly review the role of risk adjustment in Medicare payments to managed care plans and how the PIPDCG model determines a beneficiary's relative risk factor. Second, we comment on the strengths and limitations of using inpatient encounter data to adjust capitation payments for health stares. This section puts the PIPDCG model in broader context and presents some concerns that helped shape model development. Third, we describe model development: our data, sample, and variable definitions, the PIPDCG diagnostic classification system, and how diagnoses are sorted into Diagnostic Cost Groups (DCGs). The analysis and calibration of demographic factors for the PIPDCG model is reported next. Excluding diagnoses from short hospital stays is then considered. Finally, we examine the predictive accuracy and stability of the model and draw some conclusions.

MEDICARE RISK ADJUSTMENT

Medicare pays health maintenance organizations (HMOs) a monthly capitated amount for the medical care of each enrolled Medicare beneficiary. In the year 2000, 10 percent of payment for most beneficiaries is based on the PIPDCG risk-adjustment model, with the percentage scheduled to rise in following years. (The other 90 percent of payment is based on Medicare's historical adjusted average per capita cost [AAPCC] payment methodology as modified by the BBA. Enrollees who have been entitled to Medicare for less than 18 months will be paid for using a demographic model for new entitlees.) This capitated payment is the product of a county rate, determined by the beneficiary's residence, and a PIPDCG risk factor for that beneficiary. That is:

Payment = (Beneficiary relative risk factor) * (county rate)

For example, if a beneficiary living in a county with a monthly rate of $500 has a relative risk factor of 1.10, Medicare will pay a managed care plan 1.10*$500 = $550 per month for that beneficiary's medical care. The relative risk factor reflects the expected relative costliness of providing medical services to beneficiaries in different health states. By paying more for sicker beneficiaries, managed care plans are encouraged to enroll and work to satisfy the needs of such people. In this article, we explain how the PIPDCG model calculates an individual's risk factor. The risk-adjustment model is also used in calculating the county rate, as explained by Ingber (2000).

PIPDCG RELATIVE RISK FACTORS

The central feature of the PIPDCG model is calculating each beneficiary's relative risk factor. A beneficiary whose Medicare expenditures are predicted to equal the national average has a relative risk factor of 1.00. Risk factors greater than 1.00 indicate above average expected costliness; factors below 1.00 indicate lower-than-average expected costs. Tables 1 and 2 can be used to construct an individual's relative risk factor, starting with a base year (year 1) of demographic and medical information:

* Step 1. Compute a demographic factor (Table 1) by adding up to three individual factors: (1) age and sex; (2) originally disabled status (for a person who is now over age 65 but was previously entitled to Medicare because of disability); (3) Medicaid status (for a person who was entitled to Medicaid at any time during the base year).

* Step 2. Select the PIPDCG factor (Table 2) by: (1) assigning each hospital stay of at least 2 days to a PIPDCG category based on the principal medical problem that led to the admission; then (2) identifying the relative risk factor associated with the highest numbered of these PIPDCG categories. Note that beneficiaries with no hospital stays of at least 2 days are assigned to PIPDCG 4, along with those whose only hospitalizations fall into the lowest numbered PIPDCG, that is, 4; both groups receive PIPDCG 4's factor of zero.

* Step 3: Add the demographic and PIPDCG factors to achieve a relative risk score. If Medicare is not this person's primary payer, multiply this score by 0.21 to represent the expected part of total health care costs for which HCFA is responsible.