Can benchmarking Australian hospitals for quality identify and improve high and low performers? Disseminating research findings for hospitals

Abstract

This paper examines the principles of benchmarking in healthcare and how benchmarking can contribute to practice improvement and improved health outcomes for patients. It uses the Deepening our Understanding of Quality in Australia (DUQuA) study published in this Supplement and DUQuA’s predecessor in Europe, the Deepening our Understanding of Quality improvement in Europe (DUQuE) study, as models. Benchmarking is where the performances of institutions or individuals are compared using agreed indicators or standards. The rationale for benchmarking is that institutions will respond positively to being identified as a low outlier or desire to be or stay as a high performer, or both, and patients will be empowered to make choices to seek care at institutions that are high performers. Benchmarking often begins with a conceptual framework that is based on a logic model. Such a framework can drive the selection of indicators to measure performance, rather than their selection being based on what is easy to measure. A Donabedian range of indicators can be chosen, including structure, process and outcomes, created around multiple domains or specialties. Indicators based on continuous variables allow organizations to understand where their performance is within a population, and their interdependencies and associations can be understood. Benchmarking should optimally target providers, in order to drive them towards improvement. The DUQuA and DUQuE studies both incorporated some of these principles into their design, thereby creating a model of how to incorporate robust benchmarking into large-scale health services research.

The effect of electronic audits and feedback in primary care and factors that contribute to their effectiveness: a systematic review

“…This emphasizes the difficulties in designing complex healthcare interventions and indicates the need for a framework and a well-defined research agenda when developing electronic A&F trials so that interventions can be reproduced and compared. [60][61][62] Designing a methodology for developing generalizable automated A&F interventions in primary care could be useful since automated quality assessment based on EHRs offers promising prospects if the challenges are answered. [15] Large data repositories, such as those of the Dutch NIVEL, the British Royal College of General Practitioners (RGCP) Research and Surveillance Centre (RSC) network, and the Belgian INTEGO database, have already been available for many years in primary care [17,63,64].…”

Managerial features and outcome in neonatal intensive care units: results from a cluster analysis

“…Dimensions typically measured are quality of care and costs. Managers are responsible for carrying out benchmarking in order to trigger continuous improvement processes [23,24].…”

Managerial features and outcome in neonatal intensive care units: results from a cluster analysis

“…Dimensions typically measured are quality of care and costs. Managers are responsible for carrying out benchmarking in order to trigger continuous improvement processes [23,24]. Area 3.…”