Building health plan databases to risk adjust outcomes and payments

Published: December 1, 1998
Category: Bibliography > Papers
Authors: Bachman DJ, Fishman PA, Goodman MJ, Hornbrook MC, Meenan RT, O'Keeffe-Rosetti M
Countries: United States
Language: null
Types: Care Management
Settings: Health Plan

Int J Qual Health Care 10:531-538.

Center for Health Research, Kaiser Permanente, Northwest Division, Portland, OR, USA

OBJECTIVES: To highlight the types and sources of data on medical risk and outcomes routinely collected by managed care organizations over time; to summarize the quality and consistency of these data; and to describe some of the difficulties that arise in collecting, pooling, and using these data.

DESIGN: Synthesis of the experiences of two risk-adjustment modeling projects in assembling large volumes of demographic, diagnostic, and expense data from several health maintenance organizations (HMOs) over multiple years.

SETTING: Six large HMOs from the Northwest, North Central, and Northeast regions of the USA.

INTERVENTIONS: Health plans were approached to participate in a risk-adjustment study, presented with an extensive variable-by-variable data request, and, if willing to participate, asked to specify a desired process for extracting, copying, and transferring selected variables to the study site for purposes of research. Depending on local circumstances, three different approaches were used: (i) health plan staff obtained the data and organized them into the requested study format; (ii) study staff were provided access to health plan data systems to perform the extractions directly; and (iii) health plans hired contract programmers to perform the extractions under the direction of the study team. Key measures of risk and cost were extracted and merged into analysis files.

MAIN OUTCOME MEASURES: Complete and consistent eligibility maps, demographic information, inpatient and outpatient diagnoses, and total health plan expense for each enrollee.

RESULTS: We have been successful in collecting and integrating complete utilization, morbidity, demographic, and cost data on total memberships of five large HMOs as well as a subset from a sixth HMO, all for multiple years.

CONCLUSION: While HMOs vary greatly in the quality and comprehensiveness of their data systems, these attributes have been improving across the board over time. Automated health plan data systems represent potentially valuable sources of data on health risks and outcomes and can be used to benchmark disease management programs and risk adjust capitation payments and medical outcomes.

PMID: 9928592

Predictive Risk Modeling,Capitation,Payment,Outcome Measures,United States,Managed Care Programs/economics,Databases,Factual

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