Background: Systematic, automated methods for monitoring physician performance are necessary if outlying behavior is to be detected promptly and acted on. In the Michigan Urological Surgery Improvement Collaborative (MUSIC), we evaluated several statistical process control (SPC) methods to determine the sensitivity and ease of interpretation for assessing adherence to imaging guidelines for patients with newly diagnosed prostate cancer.
Methods: Following dissemination of imaging guidelines within the Michigan Urological Surgery Improvement Collaborative (MUSIC) for men with newly diagnosed prostate cancer, MUSIC set a target of imaging < 10% of patients for which bone scan is not indicated. We compared four SPC methods using Monte Carlo simulation: p-chart, weighted binomial CUSUM, Bernoulli cumulative sum (CUSUM), and exponentially weighted moving average (EWMA). We simulated non-indicated bone scan rates ranging from 5.9% (within target) to 11.4% (above target) for a representative MUSIC practice. Sensitivity was determined using the average run length (ARL), the time taken to signal a change. We then plotted actual non-indicated bone scan rates for a representative MUSIC practice using each SPC method to qualitatively assess graphical interpretation.
Results: EWMA had the lowest ARL and was able to detect changes significantly earlier than the other SPC methodologies (p < 0.001). The p-chart had the highest ARL and thus detected changes slowest (p < 0.001). EWMA and p-charts were easier to interpret graphically than CUSUM methods due to their ability to display historical imaging rates.
Conclusions: SPC methods can be used to provide informative and timely feedback regarding adherence to healthcare performance target rates in quality improvement collaboratives. We found the EWMA method most suited for detecting changes in imaging utilization.
Keywords: Imaging; Monitoring; Prostate cancer; Quality control.