Lee Terrence C, Shah Neil U, Haack Alyssa, Baxter Sally L
Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA 92093, USA.
Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
Informatics (MDPI). 2020 Sep;7(3). doi: 10.3390/informatics7030025. Epub 2020 Jul 25.
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.
在过去十年中，利用电子健康记录（EHR）数据进行的预测分析发展迅速。虽然模型性能指标有了显著改善，但将预测模型应用于临床环境以进行即时护理风险分层的最佳实践仍在不断发展。在此，我们对描述整合到EHR系统并在临床实践中实施的预测模型的文章进行了系统综述。我们进行了详尽的数据库搜索，并提取了涵盖实施多个方面的数据。我们评估了研究质量和证据水平。我们初步获得了3393篇文章用于筛选，最终纳入了44篇文章进行数据提取和分析。已实施预测模型最常见的临床领域与血栓性疾病/抗凝（25%）和脓毒症（16%）相关。大多数研究是在住院学术环境中进行的。实施挑战包括警报疲劳、缺乏培训以及护理团队工作负担增加。在32项报告对临床结果有影响的研究中，22项（69%）在模型实施后显示出改善。总体而言，基于EHR的预测模型在改善临床结果方面提供了有前景的结果，尽管文献中仍存在一些差距，且大多数研究设计为观察性研究。未来使用随机对照试验的研究可能有助于提高研究结果的可推广性。
