将人工智能带到OR:将机器学习预测模型集成到EHR中 - - 关于用户友好性的试点
Sara Ben Hmido1,2, Ewout Ingwersen3,2, Houssam Abder Rahim4
1Department of Surgery, Amsterdam UMC De Boelelaan Site, Amsterdam, The Netherlands s.benhmido@amsterdamumc.nl.
BMJ health & care informatics
|February 24, 2026
概括
一个新的机器学习模型用于预测结直肠静脉漏,已成功集成到Epic电子健康记录 (EHR) 中. 该系统被发现是可靠和用户友好的,支持潜在的实时临床使用.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习在外科手术中的应用
- 临床决策支持系统 临床决策支持系统
背景情况:
- 结肠直肠静脉泄漏是一个显著的并发症.
- 术内预测模型可以帮助手术决策.
- 集成到电子健康记录 (EHR) 中对于临床采用至关重要.
研究的目的:
- 评估用于结直肠流的手术内预测机器学习模型的技术集成和可用性.
- 在一个学术中心评估Epic EHR系统中的模型性能.
- 为了确定手术室工作人员感知到的易用性和工作流的影响.
主要方法:
- 开发了一个利用28个数据元素的机器学习模型.
- 从患者记录,监测设备和每15分钟一次的OR观察中收集数据.
- 用户可用性使用系统可用性量表 (SUS) 和有针对性的问卷进行了评估.
主要成果:
- 该模型在15个程序中被集成并可靠运行.
- 九名OR工作人员参与,报告平均SUS得分为79.2.2.
- 用户发现界面易于访问和清晰,尽管预测响应率被评为较低.
结论:
- 术内预测模型在EHR环境中展示了可靠的实施和积极的用户感知.
- 该系统显示为实时临床使用的准备,等待进一步的多地点评估和监管规划.
- 未来的部署需要一个强大的质量管理框架,以随着时间的推移监测性能和安全.
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