Using Diagnoses to Describe Populations and Predict Costs - capitated payment system uses demographic risk adjusted to calculate payments

Health Care Financing Review, Spring, 2000 by Arlene S. Ash, Randall P. Ellis, Gregory C. Pope, John Z. Ayanian, David W. Bates, Helen Burstin, Lisa I. Iezzoni, Elizabeth MacKay, Wei Yu

INTRODUCTION

Role of Health-Based Payment Models

Since 1985, HCFA has made capitated payments to managed care organizations that enroll Medicare beneficiaries. HCFA, using a demographic risk adjuster to calculate payments equal to 95 percent of what health maintenance organization (HMO) enrollees "would have cost" had they remained in the traditional fee-for-service Medicare program, paid less-than-average dollars for the group who originally transferred into these programs. However, HCFA still appears (on average) to have overpaid, because the early switchers into Medicare managed care were healthier than comparably aged non-switchers (Brown et al., 1993). Anticipating and responding to this problem, HCFA has sponsored much research, including development of the Diagnostic Cost Group (DCG) models, with the goal of being able to better match HMO payments to the health care needs of enrollees. Since 1984, when researchers at Boston University and Brandeis initiated this work for HCFA, DCGs have evolved into a family of methods for using administrative data collected during patient encounters to calculate health-based "expected costs" for populations (Ash et al., 1986, 1989, 1998; Ellis and Ash, 1995; Ellis et al., 1996a, 1996b; Pope et al., 1998, 1999, 2000).

DCG models use age, sex, and diagnoses generated from patient encounters with the medical delivery system to infer which medical problems are present for each individual and their likely effect on health care costs for a population. Some versions of the DCG models focus on diagnoses that form the principal reason for an inpatient admission, now called "PIP diagnoses" (Ash et al., 1989; Ellis and Ash, 1995; Pope et al., 2000). Other versions, such as the DCG/HCC models of this article, utilize the full range of diagnoses generated during all face-to-face encounters with clinicians (Ellis et al., 1996a, 1996b; Ash et al., 1998; Pope et al., 1998). Whereas previous publications using DCGs have calibrated models solely for Medicare samples, in this study, we contrast the ability of DCG/HCC models to predict resources in three different samples: privately insured, Medicaid, and Medicare.

Payment methods establish incentives. For example, when payments follow a "piecework" model, as in traditional fee-for-service medicine, providers are rewarded for doing more--whether the additional utilization is valuable or not. Conversely, capitated payments encourage doing less--whether through efficiency or stinting. Further, flat-rate capitated payments introduce a new perverse incentive: to enroll healthy people and to do the very little required to keep them enrolled. Models that pay each person's expected cost eliminate the incentive to "select on risk" and make efficiency the main way for a plan to achieve a competitive advantage (Van de Ven and Ellis, 2000).

Although risk-adjusted payment solves the problem of perverse patient-selection incentives, linking payments to a risk-adjustment model may lead plans to invest unproductive effort in making their enrollees "look needier" according to that model. For example, models that pay more for health care "users" encourage both appropriate and unnecessary utilization; those that identify illness only through hospitalizations encourage admissions, and those that pay more for people with more coded illnesses encourage "diagnostic discovery." This last incentive can be good to the extent that it rewards plans that keep better track of their members' chronic illnesses (Greenwald et al., 1998). The degree of imperfection in incentive-setting is one criterion in choosing among payment models. Furthermore, how much imperfection is acceptable depends upon the nature and level of problems associated with available alternatives.

Predicting Costs in a Range of Populations

The original DCG models are prospective, that is, they use baseline, or year 1, data to infer the level of need for health care in year 2 and were developed to predict costs for Medicare beneficiaries. Medical conditions (diagnoses) detected in year 1 are used to organize people into groups with similar levels of future health care need. The distribution of all members by levels of future need characterizes an enrolled group and is used to determine a health-based payment. More recently, we have developed DCG models to calculate expected concurrent expenses, that is, expenses that occur in the same year as the diagnoses used to characterize the population (Pope et al., 1998, 1999, 2000). We have also adapted both prospective and concurrent modeling frameworks for use in Medicaid and commercially insured (private) populations under the age of 65 (Ash et al., 1998).

Concurrent models may be particularly useful for provider profiling and monitoring, because knowing all the medical problems being treated during a period of time is particularly relevant for estimating the level of resources used to treat them. However, prospective models, which predict future costs, are more appropriate for creating payments to managed care organizations that assume financial risk, because they focus on the presence of illnesses, such as cancer and heart disease, that predictably make people more expensive to treat.