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标题：Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review
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作者：Terrence C. Lee ; Neil U. Shah ; Alyssa Haack 等
期刊名称：Informatics
电子版ISSN：2227-9709
出版年度：2020
卷号：7
期号：3
页码：25-42
DOI：10.3390/informatics7030025
出版社：MDPI Publishing
摘要：Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic review of articles describing predictive models integrated into EHR systems and implemented in clinical practice. We conducted an exhaustive database search and extracted data encompassing multiple facets of implementation. We assessed study quality and level of evidence. We obtained an initial 3393 articles for screening, from which a final set of 44 articles was included for data extraction and analysis. The most common clinical domains of implemented predictive models were related to thrombotic disorders/anticoagulation (25%) and sepsis (16%). The majority of studies were conducted in inpatient academic settings. Implementation challenges included alert fatigue, lack of training, and increased work burden on the care team. Of 32 studies that reported effects on clinical outcomes, 22 (69%) demonstrated improvement after model implementation. Overall, EHR-based predictive models offer promising results for improving clinical outcomes, although several gaps in the literature remain, and most study designs were observational. Future studies using randomized controlled trials may help improve the generalizability of findings.
关键词：electronic health records; predictive models; predictive analytics; risk prediction; clinical prediction model; precision medicine; clinical informatics; clinical decision support; artificial intelligence electronic health records ; predictive models ; predictive analytics ; risk prediction ; clinical prediction model ; precision medicine ; clinical informatics ; clinical decision support ; artificial intelligence