Grand rounds in methodology: four critical decision points in statistical process control evaluations of quality improvement initiatives

BMJ Quality & Safety, vol. 32, pp. 47–54

Abstract

Quality improvement (QI) projects often employ statistical process control (SPC) charts to monitor process or outcome measures as part of ongoing feedback, to inform successive Plan-Do-Study-Act cycles and refine the intervention (formative evaluation). SPC charts can also be used to draw inferences on effectiveness and generalisability of improvement efforts (summative evaluation), but only if appropriately designed and meeting specific methodological requirements for generalisability. Inadequate design decreases the validity of results, which not only reduces the chance of publication but could also result in patient harm and wasted resources if incorrect conclusions are drawn. This paper aims to bring together much of what has been written in various tutorials, to suggest a process for using SPC in QI projects. We highlight four critical decision points that are often missed, how these are inter-related and how they affect the inferences that can be drawn regarding effectiveness of the intervention: (1) the need for a stable baseline to enable drawing inferences on effectiveness; (2) choice of outcome measures to assess effectiveness, safety and intervention fidelity; (3) design features to improve the quality of QI projects; (4) choice of SPC analysis aligned with the type of outcome, and reporting on the potential influence of other interventions or secular trends.These decision points should be explicitly reported for readers to interpret and judge the results, and can be seen as supplementing the Standards for Quality Improvement Reporting Excellence guidelines. Thinking in advance about both formative and summative evaluation will inform more deliberate choices and strengthen the evidence produced by QI projects.

Authors 2

1. Perla J. Marang‐van de Mheen corresponding Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, Medical Decision Making, J10-S, Leiden University Medical Center, Leiden, The Netherlands

   Department of Biomedical Data Sciences, Medical Decision Making, J10-S

   Leiden University Medical Center

2. Thomas M. Woodcock

   Imperial College London · National Institute for Health and Care Research

   Affiliation as printed

   National Institute for Health Research Applied Research Collaboration Northwest London, Imperial College London, London, UK

   Imperial College London

   National Institute for Health Research Applied Research Collaboration Northwest London