Comparing the value of three main diagnostic-based risk-adjustment systems (DBRAS)

Published: March 1, 2005
Category: Bibliography > Reports
Authors: Berlinguet M, Dean S, Preyra C
Countries: Canada
Language: null
Types: Care Management
Settings: Academic, Hospital

Ottawa, ON, Canada: Canadian Health Services Research Foundation.

Canadian Health Services Research Foundation, Ottawa, ON, Canada

Diagnostic-Based Risk-Adjustment Systems (DBRAS) are now widely used in the United States by healthcare payers and providers to identify the health status of individuals and predict their expenditures for the same year or the next year. That requires linking all diagnoses over a period of a year for an individual (from the same administrative databases input files as diagnosis related groups (DRG)) and generating one (for the categorical systems) or many groups (for the so-called dichotomous variables groupers) for each individual. These systems can be used for funding under a capitation arrangement, identifying high-cost patients for case management, monitoring health status of groups of enrolees, and planning and evaluating the health services.

The lead researchers secured access to large development and validation samples from Ontario, Quebec, and Alberta. Evaluation licenses from three most relevant DBRAS were obtained, the ADG/ACG system from Johns Hopkins University, the HCC/ DCG system from DxCG Inc., and ACRG2/ CRG from 3M Inc. Data were processed successfully. All diagnoses coming from fee-forservice and hospital discharge summaries were used and pooled for each patient.

The design involved measuring an expected cost and an observed cost for each individual of a validation sample for the same year (concurrent model) and for the following year (prospective model). Retrieving all expenditures from fee-for-service medical billings and/or acute hospital expenditures for inpatient services or ambulatory day surgeries is needed to calculate weights. Evaluation was done initially in all three provinces using socio-economic adjustments in addition to age and gender, and the three DBRAS systems were much better predictors of costs. Then our core comparative evaluation between DBRAS showed that the HCC/DCG system slightly outperformed the ACRG2/CRG model and more so, outperforms the ADG/ACG for cost prediction power for medical fee-for-service expenditures, hospital inpatient and ambulatory expenditures, and total cost. Some results varied much between provinces for same groupers.

These systems are never used to predict individual expenses but rather to estimate expenses for groups of people with similar conditions. Predictive ratios (expected over observed costs) pool expenditures for many individuals. Hence, the prediction is much greater with groups of people. Still, we observe that these systems over-predict costs for the groups (here deciles: meaning all population sampled divided in 10 equal bins) in the lower-cost deciles, and under-predict for higher-cost deciles.

Three main evaluation criteria were developed in January 2004 and used to rate each DBRAS grouper: 1) clinical and administrative value of categories; 2) discrimination and predictive value of categories; and 3) transparency, ease of use, and simplicity of resource weight calculation (see table 15 in the full report). All groupers are good and sound but decision makers shall select the one that fits their needs. Since then, clinical risk groups (CRGs) have been proposed in 2004 by the Quebec Ministry of Health for severity adjusting capitation payment of GPs; and the Calgary Health region has since acquired an operational license of CRGs.

Predictive Risk Modeling,Cost Burden Evaluation,Diagnostic Certainty,Canada

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