Multilevel Quality Indicators: Methodology and Monte Carlo Evidence.

Background: Quality indicators are frequently used to assess the performance of health care providers, in particular hospitals. Established approaches to the design of such indicators are subject to distortions due to indirect standardization and high variance of estimators. Indicators for geographical regions are rarely considered.

Objectives: To develop and evaluate a methodology of multilevel quality indicators (MQIs) for both health care providers and geographical regions.

Research design: We formally derived MQIs from a statistical multilevel model, which may include characteristics of patients, providers, and regions. We used Monte Carlo simulation to assess the performance of MQIs relative to established approaches based on the standardized mortality/morbidity ratio (SMR) and the risk-standardized mortality rate (RSMR).

Measures: Rank correlation between true provider/region effects and quality indicator estimates; shares of the 10% best and 10% worst providers identified by the quality indicators.

Results: The proposed MQIs are: (1) standardized hospital outcome rate (SHOR); (2) regional SHOR; and (3) regional standardized patient outcome rate. Monte Carlo simulations indicated that the SHOR provides substantially better estimates of provider performance than the SMR and risk-standardized mortality rate in almost all scenarios. The regional standardized patient outcome rate was slightly more stable than the regional SMR. We also found that modeling of regional characteristics generally improves the adequacy of provider-level estimates.

Conclusions: MQIs methodology facilitates adequate and efficient estimation of quality indicators for both health care providers and geographical regions.