J William Thomas1. 1. Institute for Health Policy, Edmund S. Muskie School of Public Service, University of Southern Maine, ME 04101-9300, USA.
Abstract
OBJECTIVE: To determine whether additional risk adjustment is necessary in economic profiling of physicians when claims data are already grouped into episodes of care, and to measure effects of risk adjustment on cost efficiency rankings of physicians. DATA SOURCES: Four years of inpatient, outpatient, professional, and pharmacy claims data from a mixed model HMO. STUDY DESIGN: Claims data were processed through Symmetry Health Data Systems' episode treatment group (ETG) grouper to define episodes of care and Symmetry's episode risk group (ERG) software to define measures of patients' health risk scores. For each episode type (ETG), ETG-mean expected costs were calculated as the mean costs of all episodes of that type, and risk-adjusted expected costs were calculated using three alternative risk model formulations. DATA COLLECTION: Within specialties, physicians were ranked from most cost efficient to least cost efficient, based on standardized difference between actual and expected costs. ETG-mean based rankings were compared with risk-adjusted rankings. Analyses were performed for cardiologists, family practitioners, general surgeons, and neurologists. PRINCIPAL FINDINGS: With all three risk models, risk scores were essentially unrelated to episode costs in approximately three-fourths of episode categories (ETGs). In a sample of ETGs for which risks-costs relationships appeared to exist, split sample validation showed the relationships to be unstable or spurious in all except one ETG. Within specialties, risk-adjusted cost efficiency rankings differ little from ETG-mean adjusted rankings. CONCLUSIONS: Depending upon the purpose for which economic profiling is performed, additional risk adjustment, beyond that already provided by episode grouping, may be unnecessary. Additional research may be needed to identify and validate ETG-level relationships between patient risks and episode costs.
OBJECTIVE: To determine whether additional risk adjustment is necessary in economic profiling of physicians when claims data are already grouped into episodes of care, and to measure effects of risk adjustment on cost efficiency rankings of physicians. DATA SOURCES: Four years of inpatient, outpatient, professional, and pharmacy claims data from a mixed model HMO. STUDY DESIGN: Claims data were processed through Symmetry Health Data Systems' episode treatment group (ETG) grouper to define episodes of care and Symmetry's episode risk group (ERG) software to define measures of patients' health risk scores. For each episode type (ETG), ETG-mean expected costs were calculated as the mean costs of all episodes of that type, and risk-adjusted expected costs were calculated using three alternative risk model formulations. DATA COLLECTION: Within specialties, physicians were ranked from most cost efficient to least cost efficient, based on standardized difference between actual and expected costs. ETG-mean based rankings were compared with risk-adjusted rankings. Analyses were performed for cardiologists, family practitioners, general surgeons, and neurologists. PRINCIPAL FINDINGS: With all three risk models, risk scores were essentially unrelated to episode costs in approximately three-fourths of episode categories (ETGs). In a sample of ETGs for which risks-costs relationships appeared to exist, split sample validation showed the relationships to be unstable or spurious in all except one ETG. Within specialties, risk-adjusted cost efficiency rankings differ little from ETG-mean adjusted rankings. CONCLUSIONS: Depending upon the purpose for which economic profiling is performed, additional risk adjustment, beyond that already provided by episode grouping, may be unnecessary. Additional research may be needed to identify and validate ETG-level relationships between patient risks and episode costs.
Authors: Peter S Hussey; Han de Vries; John Romley; Margaret C Wang; Susan S Chen; Paul G Shekelle; Elizabeth A McGlynn Journal: Health Serv Res Date: 2009-01-28 Impact factor: 3.402