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World J Clin Cases. Jul 16, 2015; 3(7): 625-634
Published online Jul 16, 2015. doi: 10.12998/wjcc.v3.i7.625
Bayesian methods in reporting and managing Australian clinical indicators
Peter P Howley, Stephen J Hancock, Robert W Gibberd, Sheuwen Chuang, Frank A Tuyl
Peter P Howley, Frank A Tuyl, School of Mathematical and Physical Sciences\Statistics, The University of Newcastle, Callaghan 2308, Australia
Stephen J Hancock, Robert W Gibberd, Health Services Research Group, Faculty of Health, The University of Newcastle, Callaghan 2308, Australia
Sheuwen Chuang, School of Health Care Administration, Health Policy and Care Research Center, Taipei Medical University, Taipei 11031, Taiwan
Author contributions: Howley PP co-developed and implemented the described methods and designed and principally constructed the article; Hancock SJ co-developed and implemented the described methods and edited the article; Gibberd RW developed and implemented the described methods and edited the article; Chuang S provided valuable contribution to the international perspective of clinical indicator use; Tuyl FA provided valuable contribution to the discussion of Bayesian concepts and edited the article.
Conflict-of-interest statement: The Health Services Research Group at The University of Newcastle has received research funding from the Australian Council on Healthcare Standards to produce annual clinical indicator reports. Professor Robert W Gibberd is the Director and Mr Stephen J Hancock and Dr. Peter P Howley are members of the Group.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Dr. Peter P Howley, School of Mathematical and Physical Sciences\Statistics, The University of Newcastle, c/- Room v123, Mathematics Building, Callaghan 2308, Australia. peter.howley@newcastle.edu.au
Telephone: +61-2-49215518 Fax: +61-2-49216898
Received: August 26, 2014
Peer-review started: August 27, 2014
First decision: October 14, 2015
Revised: April 2, 2015
Accepted: May 16, 2015
Article in press: May 18, 2015
Published online: July 16, 2015
Processing time: 335 Days and 0.6 Hours
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 20th 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 20th 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.