Is risk-adjustor selection more important than statistical approach for provider profiling? Asthma as an example

I. Chan Huang, Francesca Dominici, Constantine Frangakis, Gregory B. Diette, Cheryl L. Damberg, Albert W. Wu

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives. To examine how the selections of different risk adjustors and statistical approaches affect the profiles of physician groups on patient satisfaction. Data sources. Mailed patient surveys. Patients with asthma were selected randomly from each of 20 California physician groups between July 1998 and February 1999. A total of 2515 patients responded. Research design. A cross-sectional study. Patient satisfaction with asthma care was the performance indicator for physician group profiling. Candidate variables for risk-adjustment model development included sociodemographic, clinical characteristics, and self-reported health status. Statistical strategies were the ratio of observed-to-expected rate (OE), fixed effects (FE), and the random effects (RE) approaches. Model performance was evaluated using indicators of discrimination (C-statistic) and calibration (Hosmer-Lemeshow χ2). Ranking impact of using different risk adjustors and statistical approaches was based on the changes in absolute ranking (AR) and quintile ranking (QR) of physician group performance and the weighted kappa for quintile ranking. Results. Variables that added significantly to the discriminative power of risk-adjustment models included sociodemographic (age, sex, prescription drug coverage), clinical (asthma severity), and health status (SF-36 PCS and MCS). Based on an acceptable goodness-of-fit (P > 0.1) and higher C-statistics, models adjusting for sociodemographic, clinical, and health status variables (Model S-C-H) using either the FE or RE approach were more favorable. However, the C-statistic (=0.68) was only fair for both models. The influence of risk-adjustor selection on change of performance ranking was more salient than choice of statistical strategy (AR: 50%-80% v. 20%-55%; QR: 10%-30% v. 0%-10%). Compared to the model adjusting for sociodemographic and clinical variables only and using OE approach, the Model S-C-H using RE approach resulted in 70% of groups changing in AR and 25% changing in QR (weighted kappa: 0.88). Compared to the Consumer Assessment of Health Plans model. the Model S-C-H using RE approach resulted in 65% of groups changing in AR and 20% changing in QR (weighted kappa: 0.88). Conclusions. In comparing the performance of physician groups on patient satisfaction with asthma care, the use of sociodemographic, clinical, and health status variables maximized risk-adjustment model performance. Selection of risk adjustors had more influence on ranking profiles than choice of statistical strategies. Stakeholders employing provider profiling should pay careful attention to the selection of both variables and statistical approach used in risk-adjustment.

Original languageEnglish (US)
Pages (from-to)20-34
Number of pages15
JournalMedical Decision Making
Volume25
Issue number1
DOIs
StatePublished - Jan 2005

Keywords

  • Fixed effects model
  • Physician group
  • Random effects model
  • Report cards
  • Risk adjustment

ASJC Scopus subject areas

  • Health Policy

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