基于电子健康记录数据自动识别术后感染以进行预测和监控:范围审查

IF 3.1 3区 医学 Q2 MEDICAL INFORMATICS JMIR Medical Informatics Pub Date : 2024-09-10 DOI:10.2196/57195
Siri Lise van der Meijden, Anna M van Boekel, Harry van Goor, Rob GHH Nelissen, Jan W Schoones, Ewout W Steyerberg, Bart F Geerts, Mark GJ de Boer, M Sesmu Arbous
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引用次数: 0

摘要

背景:术后感染仍是医疗保健领域的一项重要挑战,会导致高发病率、高死亡率和高成本。准确识别和标记术后细菌感染患者对于开发预测模型、验证生物标记物以及在临床实践中实施监控系统至关重要。目的:本次范围界定综述旨在探讨使用电子健康记录(EHR)数据识别术后感染患者的方法,以超越手动病历审查的参考标准。方法:我们在 PubMed、Embase、Web of Science(核心收录)、Cochrane 图书馆和 Emcare(Ovid)中执行了一项系统性检索策略,目标是针对术后环境中各种细菌感染的预测和全自动监控(即无需人工检查)的研究。对于预测建模研究,我们评估了所使用的标记方法,将其分为手动和自动两种。我们评估了监测和标记术后感染所需的不同类型的电子病历数据,以及全自动监测系统与人工病历审查相比的性能。结果:我们在 2003 年至 2023 年间发表的研究中确定了 75 种不同的方法和定义,用于识别术后感染患者。人工标注是预测建模研究中最主要的方法,65%(49/75)的鉴定方法使用结构化数据,45%(34/75)使用自由文本和临床笔记作为数据源之一。全自动监控系统应谨慎使用,因为报告的阳性预测值介于 0.31 和 0.76 之间。结论:目前还没有证据支持仅根据结构化电子病历数据对感染患者进行全自动标记和识别。未来的研究应侧重于定义统一的定义,以及优先发展使用结构化电子病历数据进行感染检测的更具扩展性的自动化方法。
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Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review
Background: Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice. Objective: This scoping review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart review. Methods: We performed a systematic search strategy across PubMed, Embase, Web of Science (Core Collection), the Cochrane Library, and Emcare (Ovid), targeting studies addressing the prediction and fully automated surveillance (ie, without manual check) of diverse bacterial infections in the postoperative setting. For prediction modeling studies, we assessed the labeling methods used, categorizing them as either manual or automated. We evaluated the different types of EHR data needed for the surveillance and labeling of postoperative infections, as well as the performance of fully automated surveillance systems compared with manual chart review. Results: We identified 75 different methods and definitions used to identify patients with postoperative infections in studies published between 2003 and 2023. Manual labeling was the predominant method in prediction modeling research, 65% (49/75) of the identified methods use structured data, and 45% (34/75) use free text and clinical notes as one of their data sources. Fully automated surveillance systems should be used with caution because the reported positive predictive values are between 0.31 and 0.76. Conclusions: There is currently no evidence to support fully automated labeling and identification of patients with infections based solely on structured EHR data. Future research should focus on defining uniform definitions, as well as prioritizing the development of more scalable, automated methods for infection detection using structured EHR data.
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来源期刊
JMIR Medical Informatics
JMIR Medical Informatics Medicine-Health Informatics
CiteScore
7.90
自引率
3.10%
发文量
173
审稿时长
12 weeks
期刊介绍: JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
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