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Using routine comparative data to assess the quality of health care: understanding and avoiding common pitfalls
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  1. A E Powell1,
  2. H T O Davies1,
  3. R G Thomson2
  1. 1Centre for Public Policy & Management, Department of Management, University of St Andrews, Fife, UK
  2. 2Department of Epidemiology and Public Health, School of Health Sciences, Medical School, University of Newcastle, Newcastle upon Tyne, UK
  1. Correspondence to:
 Professor H T O Davies, Department of Management, University of St Andrews, St Andrews, Fife KY16 9AL, UK;
 hd{at}st-and.ac.uk

Abstract

Measuring the quality of health care has become a major concern for funders and providers of health services in recent decades. One of the ways in which quality of care is currently assessed is by taking routinely collected data and analysing them quantitatively. The use of routine data has many advantages but there are also some important pitfalls. Collating numerical data in this way means that comparisons can be made—whether over time, with benchmarks, or with other healthcare providers (at individual or institutional levels of aggregation). Inevitably, such comparisons reveal variations. The natural inclination is then to assume that such variations imply rankings: that the measures reflect quality and that variations in the measures reflect variations in quality. This paper identifies reasons why these assumptions need to be applied with care, and illustrates the pitfalls with examples from recent empirical work. It is intended to guide not only those who wish to interpret comparative quality data, but also those who wish to develop systems for such analyses themselves.

  • quality improvement research
  • comparative analysis
  • league tables

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Footnotes

  • Potential conflict of interest: Richard Thomson is the Director of the UK Quality Indicator Project, a branch of the International Quality Indicator Project, which provides anonymised feedback of comparative indicator data to UK hospitals.