前瞻性监测外科协作中医生对影像学指南的遵循情况：用于检测异常表现的统计过程控制方法的比较。
Prospective monitoring of imaging guideline adherence by physicians in a surgical collaborative: comparison of statistical process control methods for detecting outlying performance.
机构信息
Department of Urology, University of Michigan, NCRC Bldg. 16, 1st Floor, Room 114W, 2800 Plymouth Road, Ann Arbor, MI, 48109-2900, USA.
Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, 48109, USA.
出版信息
BMC Med Inform Decis Mak. 2020 May 13;20(1):89. doi: 10.1186/s12911-020-1126-z.
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.
背景
如果要及时发现异常行为并采取行动，就有必要系统地、自动地监测医生的绩效。在密歇根泌尿外科手术改进协作组（MUSIC）中，我们评估了几种统计过程控制（SPC）方法，以确定评估新诊断前列腺癌患者是否遵守成像指南的敏感性和易于解释性。
方法
在密歇根泌尿外科手术改进协作组（MUSIC）中发布了成像指南后，MUSIC 将成像目标设定为<10%的不建议进行骨扫描的新诊断前列腺癌患者。我们使用蒙特卡罗模拟比较了四种 SPC 方法：p 图、加权二项式累积和（CUSUM）、伯努利累积和（CUSUM）和指数加权移动平均（EWMA）。我们模拟了非指示性骨扫描率，范围从 5.9%（在目标范围内）到 11.4%（超出目标），用于代表 MUSIC 实践。敏感性是通过平均运行长度（ARL）确定的，即信号发生变化所需的时间。然后，我们使用每种 SPC 方法为代表的 MUSIC 实践绘制实际的非指示性骨扫描率，以定性评估图形解释。
结果
EWMA 的 ARL 最低，能够比其他 SPC 方法更早地检测到变化（p<0.001）。p 图的 ARL 最高，因此检测到变化最慢（p<0.001）。由于能够显示历史成像率，EWMA 和 p 图比 CUSUM 方法更容易在图形上进行解释。
结论
SPC 方法可用于为质量改进协作中遵守医疗保健绩效目标率提供信息和及时反馈。我们发现 EWMA 方法最适合检测成像利用率的变化。
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