  Background: Quality indicators are frequently used to assess the performance
of healthcare 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 (MQI) for both healthcare providers and geographical regions.
  Research Design: We formally derived MQI from a statistical multilevel model,
which may include characteristics of patients, providers, and regions. We used
Monte Carlo simulation to assess the performance of MQI 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 MQI are 1) standardized hospital outcome rate (SHOR),
2) regional SHOR (RSHOR), and 3) regional standardized patient outcome rate
(RSPOR). Monte Carlo simulations indicated that the SHOR provides substantially
better estimates of provider performance than the SMR and RSMR in almost all
scenarios. RSPOR 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: MQI methodology facilitates adequate and efficient estimation of
quality indicators for both healthcare providers and geographical regions.
