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Considering chance in quality and safety performance measures: an analysis of performance reports by boards in English NHS trusts
  1. Kelly Ann Schmidtke1,
  2. Alan J Poots2,
  3. Juan Carpio1,
  4. Ivo Vlaev1,
  5. Ngianga-Bakwin Kandala3,4,
  6. Richard J Lilford5
  1. 1Warwick Business School, University of Warwick, Coventry, UK
  2. 2National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) North West London (NWL), Imperial College London, London, UK
  3. 3Department of Mathematics and Information Sciences, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, UK
  4. 4Health Economics and Evidence Synthesis Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
  5. 5Department of Public Health, University of Warwick, Coventry, UK
  1. Correspondence to Dr Kelly Ann Schmidtke, Warwick Business School, University of Warwick, Coventry CV4 7AL, UK; Kelly.Schmidtke{at}


Objectives Hospital board members are asked to consider large amounts of quality and safety data with a duty to act on signals of poor performance. However, in order to do so it is necessary to distinguish signals from noise (chance). This article investigates whether data in English National Health Service (NHS) acute care hospital board papers are presented in a way that helps board members consider the role of chance in their decisions.

Methods Thirty English NHS trusts were selected at random and their board papers retrieved. Charts depicting quality and safety were identified. Categorical discriminations were then performed to document the methods used to present quality and safety data in board papers, with particular attention given to whether and how the charts depicted the role of chance, that is, by including control lines or error bars.

Results Thirty board papers, containing a total of 1488 charts, were sampled. Only 88 (6%) of these charts depicted the role of chance, and only 17 of the 30 board papers included any charts depicting the role of chance. Of the 88 charts that attempted to represent the role of chance, 16 included error bars and 72 included control lines. Only 6 (8%) of the 72 control charts indicated where the control lines had been set (eg, 2 vs 3 SDs).

Conclusions Hospital board members are expected to consider large amounts of information. Control charts can help board members distinguish signals from noise, but often boards are not using them. We discuss demand-side and supply-side barriers that could be overcome to increase use of control charts in healthcare.

  • Control charts, run charts
  • Decision making
  • Statistical process control
  • Governance

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