Characteristics of risk adjustment systems. Working Paper Series 2

Published: June 6, 2001
Category: Reports
Authors: Breiner JR, Shenkman EA
Country: United States
Language: null
Types: Care Management, Finance/Budgeting
Settings: Health Plan, Hospital

Gainesville, FL, USA: Institute for Child Health Policy, University of Florida.

Institute for Child Health Policy, University of Florida, Gainesville, FL, USA

Increasing numbers of children and adolescents are enrolled in some form of managed care arrangement. For example, over 49 million persons were enrolled in health maintenance organizations (HMOs) in 1994 with approximately 33% of them under the age of 17.1 In addition, as of September 1999, there were two million enrollees in SCHIP, with most states using some form of managed care arrangement for their Title XXI Programs.2 The numbers of children with special health care needs and adolescents (ages 12 through 18) also are increasingly enrolled in managed care arrangements in both the public and private sectors.
Although most children are healthy and consume relatively few health care resources, children with special health care needs and adolescents (ages 12 through 18) have increased needs for health care services. These increased needs may place insurers and health care providers at financial risk, particularly within managed care arrangements. Reimbursement within managed care is frequently provided in the form of capitated risk payments. However, standard methods to adjust capitation payments to health plans or providers only take into consideration age, gender, geographic region, and welfare category, which typically explain less than 6% of the variation in health care expenditures.3
Various methods have been proposed to assess the likelihood, or “risk”, of future health care use by enrollees. These risk assessment methods use measures such as demographic data, self-reported health status, and diagnostic information to classify the individual in terms of his or her anticipated health care use. The results are then summarized across individuals to provide an overall measure of risk for enrollees in that risk pool. This information can be used to make adjustments in capitation rates based on the enrollees’ expected health care needs and use, thereby providing more adequate reimbursement to health plans and providers.4
            Insurers commonly use demographic data such as age and gender as risk-adjusters. However, these measures explain less than 4% to 6% of the variance in health care use.5 While there are several different approaches to risk adjustment, diagnostic-based models have gained the greatest attention from a research and a policy perspective.6 Diagnostic-based approaches, as the name implies, use diagnoses or combinations of diagnoses assigned by clinicians in outpatient and inpatient settings to predict the need for health care services both in the same year in which the diagnosis was assigned (concurrent use) and at some time in the future (prospective use).7
            These approaches are attractive because: 1) they rely on data readily available in most third party payers’ claims and encounter databases, thereby reducing the need for additional costly data collection; 2) they are an improvement over traditional demographic adjusters; and 3) some systems are either in use or are being tested with third party payers who insure large numbers of children and adolescents.
Currently, health care plans and providers face strong financial disincentives when caring for those with increased health care needs, such as adolescents. Greater precision can be achieved in predicting health care use and charges by using diagnostic-based approaches to risk adjustment and altering capitation payments accordingly. Several diagnostic-based systems are available and some are widely used.

Age,Resource Use,Financial,Practice Patterns Comparison,United States
LinkedIn Facebook Twitter

© The Johns Hopkins University, The Johns Hopkins Hospital, and Johns Hopkins Health System.
All rights reserved. Terms of Use Privacy Statement

Back to top