Yakusheva Olga, Khadr Lara, Lee Kathryn A, Ratliff Hannah C, Marriott Deanna J, Costa Deena Kelly
The Johns Hopkins University School of Nursing, Baltimore, MD 21205, United States.
University of Michigan School of Nursing, Ann Arbor, MI 48109, United States.
J Am Med Inform Assoc. 2025 Mar 1;32(3):426-434. doi: 10.1093/jamia/ocae275.
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.
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.
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.
Our EHR event logs-mining program has high precision, recall, and validity for identifying patient-levelshift interprofessional teams in ICUs.
Algorithmic and artificial intelligence approaches have a strong potential for informing research to optimize patient team assignments and improve ICU care and outcomes.
健康信息学的进展促使临床研究人员迅速扩大了大数据分析和电子健康记录（EHR）的使用，以优化跨专业重症监护病房（ICU）团队护理。本研究开发并验证了一个程序，用于从EHR事件日志中提取每个班次分配给每位患者的跨专业团队。
对2018年1月1日至2019年12月31日期间某学术医疗中心5个ICU中18岁及以上机械通气患者的EHR事件日志进行回顾性分析。我们将跨专业团队定义为每个班次分配给每位患者的所有医疗服务提供者（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，该程序可提取每个班次与每位患者的病历进行交互的临床医生。该算法由两名独立评审员使用消息理解会议（MUC - 6）方法，针对每个ICU随机抽取的200个患者班次的人工图表审查进行验证。
我们的样本包括4559次ICU会诊和72846个患者班次。我们的程序提取了与这些会诊相关的3288名医疗服务提供者、2702名注册护士和219名呼吸治疗师。83%的患者班次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%的班次级团队包括来自所有三个专业的临床医生。该程序的精确率为95.9%，召回率为96.2%，且具有较高的表面效度。
我们的EHR事件日志挖掘程序在识别ICU中患者层面班次的跨专业团队方面具有较高的精确率、召回率和效度。
算法和人工智能方法在为优化患者团队分配以及改善ICU护理和结局的研究提供信息方面具有强大潜力。
