Using statistical anomaly detection models to find clinical decision support malfunctions
Using statistical anomaly detection models to find clinical decision support malfunctions
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DOI：
10.1093/jamia/ocy041
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发表时间：
2018-07-01
影响因子：
6.4
通讯作者：
Wright, Adam
中科院分区：
文献类型：
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作者：
Ray, Soumi;McEvoy, Dustin S.;Wright, Adam
目的：临床决策支持(CDS)系统出现故障的原因是多方面的，往往被忽视，导致潜在的不良结果。我们的目标是识别CDS系统中的故障。方法：我们评估了6种异常检测模型：(1)泊松变点模型、(2)自回归综合移动平均(ARIMA)模型、(3)分层分裂变点(HDC)模型、(4)贝叶斯变点模型、(5)季节性混合极端学生化偏差(SHESD)模型和(6)E-分裂与中位数(EDM)模型，并描述了它们发现已知异常的能力。我们分析了马萨诸塞州波士顿布里格姆妇女医院的纵向医疗记录(LMR)和EPIC(R)(EPIC系统公司，威斯康星州麦迪逊，美国)中的4个已知故障的CDS警报。结果：泊松变化点、ARIMA、HDC、贝叶斯变化点和SHESD模型能够在儿童铅筛查警报和免疫低下成人肺炎球菌结合疫苗警报中发现异常。EDM能够在胺碘酮患者甲状腺功能监测警报中发现异常。结论：CDS警报系统经常发生故障/异常。能够及时发现此类异常情况非常重要。异常检测模型是辅助此类检测的有用工具。
Objective: Malfunctions in Clinical Decision Support (CDS) systems occur due to a multitude of reasons, and often go unnoticed, leading to potentially poor outcomes. Our goal was to identify malfunctions within CDS systems.Methods: We evaluated 6 anomaly detection models: (1) Poisson Changepoint Model, (2) Autoregressive Integrated Moving Average (ARIMA) Model, (3) Hierarchical Divisive Changepoint (HDC) Model, (4) Bayesian Changepoint Model, (5) Seasonal Hybrid Extreme Studentized Deviate (SHESD) Model, and (6) E-Divisive with Median (EDM) Model and characterized their ability to find known anomalies. We analyzed 4 CDS alerts with known malfunctions from the Longitudinal Medical Record (LMR) and Epic (R) (Epic Systems Corporation, Madison, WI, USA) at Brigham and Women's Hospital, Boston, MA. The 4 rules recommend lead testing in children, aspirin therapy in patients with coronary artery disease, pneumococcal vaccination in immunocompromised adults and thyroid testing in patients taking amiodarone.Results: Poisson changepoint, ARIMA, HDC, Bayesian changepoint and the SHESD model were able to detect anomalies in an alert for lead screening in children and in an alert for pneumococcal conjugate vaccine in immunocompromised adults. EDM was able to detect anomalies in an alert for monitoring thyroid function in patients on amiodarone.Conclusions: Malfunctions/anomalies occur frequently in CDS alert systems. It is important to be able to detect such anomalies promptly. Anomaly detection models are useful tools to aid such detections.