基于电子病历的算法开发以识别医院不良事件，用于卫生系统绩效评估和改进：研究方案。

Developing EMR-based algorithms to Identify hospital adverse events for health system performance evaluation and improvement: Study protocol.

机构信息

Centre for Health Informatics, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

Concordia Institute for Information Systems Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, Quebec, Canada.

出版信息

PLoS One. 2022 Oct 5;17(10):e0275250. doi: 10.1371/journal.pone.0275250. eCollection 2022.

DOI:10.1371/journal.pone.0275250

PMID:36197944

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9534418/

Abstract

BACKGROUND

Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative data-based adverse event (AE) detection methods are suboptimal due to lack of clinical information. Electronic medical records (EMR) have been widely implemented and contain detailed and comprehensive information regarding all aspects of patient care, offering a valuable complement to administrative data. Harnessing the rich clinical data in EMRs offers a unique opportunity to improve detection, identify possible risk factors of AE and enhance surveillance. However, the methodological tools for detection of AEs within EMR need to be developed and validated. The objectives of this study are to develop EMR-based AE algorithms from hospital EMR data and assess AE algorithm's validity in Canadian EMR data.

METHODS

Patient EMR structured and text data from acute care hospitals in Calgary, Alberta, Canada will be linked with discharge abstract data (DAD) between 2010 and 2020 (n~1.5 million). AE algorithms development. First, a comprehensive list of AEs will be generated through a systematic literature review and expert recommendations. Second, these AEs will be mapped to EMR free texts using Natural Language Processing (NLP) technologies. Finally, an expert panel will assess the clinical relevance of the developed NLP algorithms. AE algorithms validation: We will test the newly developed AE algorithms on 10,000 randomly selected EMRs between 2010 to 2020 from Calgary, Alberta. Trained reviewers will review the selected 10,000 EMR charts to identify AEs that had occurred during hospitalization. Performance indicators (e.g., sensitivity, specificity, positive predictive value, negative predictive value, F1 score, etc.) of the developed AE algorithms will be assessed using chart review data as the reference standard.

DISCUSSION

The results of this project can be widely implemented in EMR based healthcare system to accurately and timely detect in-hospital AEs.

摘要

背景

护理质量和安全的测量主要依赖于抽象的行政数据。然而，已有充分的研究表明，由于缺乏临床信息，基于行政数据的不良事件（AE）检测方法并不理想。电子病历（EMR）已广泛实施，包含了关于患者护理各个方面的详细和全面的信息，为行政数据提供了有价值的补充。利用 EMR 中的丰富临床数据提供了一个独特的机会，可以提高检测准确性，识别 AE 的可能风险因素，并加强监测。然而，需要开发和验证用于在 EMR 中检测 AE 的方法学工具。本研究的目的是从医院 EMR 数据中开发基于 EMR 的 AE 算法，并评估加拿大 EMR 数据中 AE 算法的有效性。

方法

将来自加拿大阿尔伯塔省卡尔加里的急性护理医院的患者 EMR 结构化和文本数据与 2010 年至 2020 年之间的出院摘要数据（DAD）进行链接（n~150 万）。AE 算法开发。首先，通过系统文献回顾和专家建议生成一份全面的 AE 清单。其次，使用自然语言处理（NLP）技术将这些 AE 映射到 EMR 自由文本中。最后，一个专家小组将评估开发的 NLP 算法的临床相关性。AE 算法验证：我们将在 2010 年至 2020 年间，从阿尔伯塔省卡尔加里随机选择 10000 份 EMR 对新开发的 AE 算法进行测试。经过培训的审核员将对所选的 10000 份 EMR 图表进行审核，以确定住院期间发生的 AE。使用图表审核数据作为参考标准，评估开发的 AE 算法的性能指标（例如，敏感性、特异性、阳性预测值、阴性预测值、F1 分数等）。