Control Charts for Monitoring Hospital Adverse Events: A Comparative Study

Monitoring hospital adverse events is important to certify that evidence-based procedures of risk management are put in place and maintained, and patient safety is pursued. Statistical process control methods are increasingly used by hospital management teams to monitor safety performance data and there is comprehensive literature on methods for statistical surveillance of adverse events rates. For monitoring hospital adverse events the Shewhart u-control chart is the most used methodology. It is usually set up with the three-sigma limits after performing a Phase I analysis in which the process parameters are estimated and the control limits are calculated from historical in-control data. One possible issue of the u-chart, especially when the area of opportunity (sample size) is not constant over time, is that the in-control and out-of-control run length performances of the monitoring scheme are unknown. Furthermore, in healthcare applications often the lower control limit (LCL) is conventionally set to zero since the adverse events are rare and the sample sizes are not sufficiently large to obtain LCL greater than zero. Consequently, the control chart loses any ability to signal improvements. In this work, on the basis of a real case and through an intensive simulation study we first investigate the in-control statistical properties of the u-chart. In this way we are able to assess whether the false alarm rate is acceptable for the current application. Then we set up several alternative monitoring schemes with the same in-control performances and their out-of-control properties are studied and compared. The aim is to identify the most suitable control chart considering jointly: the ability to detect unexpected changes (usually worsening), the ability to test the impact of interventions (usually improvements), the ease of use and clarity of interpretation. Results indicate that the EWMA control chart derived under the framework of weighted likelihood ratio test has the best overall performance.