An electronic health record metadata-mining approach to identifying patient-level interprofessional clinician teams in the intensive care unit.

IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Olga Yakusheva, Lara Khadr, Kathryn A Lee, Hannah C Ratliff, Deanna J Marriott, Deena Kelly Costa
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引用次数: 0

Abstract

Objectives: Advances in health informatics rapidly expanded use of big-data analytics and electronic health records (EHR) by clinical researchers seeking to optimize interprofessional ICU team care. This study developed and validated a program for extracting interprofessional teams assigned to each patient each shift from EHR event logs.

Materials and methods: A retrospective analysis of EHR event logs for mechanically-ventilated patients 18 and older from 5 ICUs in an academic medical center during 1/1/2018-12/31/2019. We defined interprofessional teams as all medical providers (physicians, physician assistants, and nurse practitioners), registered nurses, and respiratory therapists assigned to each patient each shift. We created an EHR event logs-mining program that extracts clinicians who interact with each patient's medical record each shift. The algorithm was validated using the Message Understanding Conference (MUC-6) method against manual chart review of a random sample of 200 patient-shifts from each ICU by two independent reviewers.

Results: Our sample included 4559 ICU encounters and 72 846 patient-shifts. Our program extracted 3288 medical providers, 2702 registered nurses, and 219 respiratory therapists linked to these encounters. Eighty-three percent of patient-shift teams included medical providers, 99.3% included registered nurses, and 74.1% included respiratory therapists; 63.4% of shift-level teams included clinicians from all three professions. The program demonstrated 95.9% precision, 96.2% recall, and high face validity.

Discussion: Our EHR event logs-mining program has high precision, recall, and validity for identifying patient-levelshift interprofessional teams in ICUs.

Conclusions: Algorithmic and artificial intelligence approaches have a strong potential for informing research to optimize patient team assignments and improve ICU care and outcomes.

电子健康记录元数据挖掘方法,用于识别重症监护室中患者级别的跨专业临床医生团队。
目的:健康信息学的进步迅速扩大了临床研究人员对大数据分析和电子健康记录(EHR)的使用,以优化ICU团队的跨专业护理。本研究开发并验证了一个程序,用于从EHR事件日志中提取分配给每个患者每个班次的跨专业团队。材料与方法:回顾性分析2018年1月1日至2019年12月31日某学术医疗中心5个icu中18岁及以上机械通气患者的电子病历事件日志。我们将跨专业团队定义为每班分配给每位患者的所有医疗提供者(医生、医师助理和执业护士)、注册护士和呼吸治疗师。我们创建了一个EHR事件日志挖掘程序,该程序可以提取每班与每位患者医疗记录交互的临床医生。该算法使用消息理解会议(MUC-6)方法进行验证,并由两名独立审稿人对每个ICU的200个患者班次的随机样本进行手动图表审查。结果:我们的样本包括4559次ICU就诊和72 846次患者轮班。我们的程序提取了3288名医疗服务提供者、2702名注册护士和219名呼吸治疗师。83%的患者轮班团队包括医疗服务提供者,99.3%包括注册护士,74.1%包括呼吸治疗师;63.4%的轮班级别团队包括来自所有三个专业的临床医生。该程序具有95.9%的准确率、96.2%的召回率和较高的面孔效度。讨论:我们的EHR事件日志挖掘程序在识别icu患者级别的跨专业团队方面具有很高的精度、召回率和有效性。结论:算法和人工智能方法具有强大的潜力,可以为研究提供信息,优化患者团队分配,改善ICU护理和预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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