Bayesian methods in reporting and managing Australian clinical indicators

Abstract

Sustained clinical improvement is unlikely without appropriate measuring and reporting techniques. Clinical indicators are tools to help assess whether a standard of care is being met. They are used to evaluate the potential to improve the care provided by healthcare organisations (HCOs). The analysis and reporting of these indicators for the Australian Council on Healthcare Standards have used a methodology which estimates, for each of the 338 clinical indicators, the gains in the system that would result from shifting the mean proportion to the 20^th centile. The results are used to provide a relative measure to help prioritise quality improvement activity within clinical areas, rather than simply focus on “poorer performing” HCOs. The method draws attention to clinical areas exhibiting larger between-HCO variation and affecting larger numbers of patients. HCOs report data in six-month periods, resulting in estimated clinical indicator proportions which may be affected by small samples and sampling variation. Failing to address such issues would result in HCOs exhibiting extremely small and large estimated proportions and inflated estimates of the potential gains in the system. This paper describes the 20^th centile method of calculating potential gains for the healthcare system by using Bayesian hierarchical models and shrinkage estimators to correct for the effects of sampling variation, and provides an example case in Emergency Medicine as well as example expert commentary from colleges based upon the reports. The application of these Bayesian methods enables all collated data to be used, irrespective of an HCO’s size, and facilitates more realistic estimates of potential system gains.

Keywords: Clinical indicators, Improvement, System gains, Bayesian, Statistical models

Core tip: The article’s purpose is to bring attention to the increasing use of Bayesian methods in the clinical field to overcome shortcomings of previous analyses, and provide an application of how such methods are used in clinical management in Australia; in particular, on how to best report and use clinical indicator data for system improvement. The paper identifies flaws associated with traditional clinical indicator reporting techniques which are still often-used; describes part of current Australian clinical indicator reporting methods; and demonstrates how and why Bayesian methods are fundamental to the improved methods overcoming issues that would otherwise arise with such data.