Accuracy of Physician Electronic Health Record Usage Analytics using Clinical Test Cases

被引:1
作者
Lo, Brian [1 ,2 ,3 ]
Sequeira, Lydia [1 ,2 ,3 ]
Strudwick, Gillian [1 ,2 ,3 ]
Jankowicz, Damian [1 ]
Almilaji, Khaled [1 ]
Karunaithas, Anjchuca [1 ,4 ]
Hang, Dennis [1 ,5 ]
Tajirian, Tania [1 ,6 ]
机构
[1] Ctr Addict & Mental Hlth, Informat Management Grp, Toronto, ON, Canada
[2] Ctr Addict & Mental Hlth, Campbell Family Mental Hlth Res Inst, Ctr Complex Intervent, Digital Intervent Unit, Toronto, ON, Canada
[3] Univ Toronto, Inst Hlth Policy Management & Evaluat, Toronto, ON, Canada
[4] Univ Toronto Scarborough, Dept Hlth & Soc, Scarborough, ON, Canada
[5] Univ Victoria, Hlth Informat Sci, Victoria, BC, Canada
[6] Univ Toronto, Temerty Fac Med, Dept Family & Community Med, Toronto, ON, Canada
关键词
analytics; documentation burden; EHR system; clinical informatics; LOG DATA; MEDICINE; BURNOUT;
D O I
10.1055/s-0042-1756424
中图分类号
R-058 [];
学科分类号
摘要
Usage log data are an important data source for characterizing the potential burden related to use of the electronic health record (EHR) system. However, the utility of this data source has been hindered by concerns related to the real-world validity and accuracy of the data. While time-motion studies have historically been used to address this concern, the restrictions caused by the pandemic have made it difficult to carry out these studies in-person. In this regard, we introduce a practical approach for conducting validation studies for usage log data in a controlled environment. By developing test runs based on clinical workflows and conducting them within a test EHR environment, it allows for both comparison of the recorded timings and retrospective investigation of any discrepancies. In this case report, we describe the utility of this approach for validating our physician EHR usage logs at a large academic teaching mental health hospital in Canada. A total of 10 test runs were conducted across 3 days to validate 8 EHR usage log metrics, finding differences between recorded measurements and the usage analytics platform ranging from 9 to 60%.
引用
收藏
页码:928 / 934
页数:7
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