Comparison of control charts for monitoring clinical performance using binary data. Issue 11 (25th September 2017)

• Record Type: Journal Article
• Authors: Neuburger, Jenny; Walker, Kate; Sherlaw-Johnson, Chris; van der Meulen, Jan; Cromwell, David A
• Is Part Of: BMJ quality & safety. Volume 26:Issue 11(2017)
• Page Start: 919
• Page End: 928
• Publication Date: 2017-09-25
• Subjects: control charts -- run charts -- statistical process control -- quality measurement; Medical care -- Quality control -- Periodicals; Health facilities -- Risk management -- Periodicals; Medical errors -- Prevention -- Periodicals; 362.106805
• DOI: 10.1136/bmjqs-2016-005526
• Languages: English
• ISSNs: 2044-5415

Abstract

Abstract : Background: Time series charts are increasingly used by clinical teams to monitor their performance, but statistical control charts are not widely used, partly due to uncertainty about which chart to use. Although there is a large literature on methods, there are few systematic comparisons of charts for detecting changes in rates of binary clinical performance data. Methods: We compared four control charts for binary data: the Shewhart p-chart; the exponentially weighted moving average (EWMA) chart; the cumulative sum (CUSUM) chart; and the g-chart. Charts were set up to have the same long-term false signal rate. Chart performance was then judged according to the expected number of patients treated until a change in rate was detected. Results: For large absolute increases in rates (>10%), the Shewhart p-chart and EWMA both had good performance, although not quite as good as the CUSUM. For small absolute increases (<10%), the CUSUM detected changes more rapidly. The g-chart is designed to efficiently detect decreases in low event rates, but it again had less good performance than the CUSUM. Implications: The Shewhart p-chart is the simplest chart to implement and interpret, and performs well for detecting large changes, which may be useful for monitoring processes of care. The g-chart is a useful complement for determining the success of initiatives to reduce low-event rates (eg, adverse events). The CUSUM may be particularly useful for faster detection of problems with patient safety leading to increases in adverse event rates.