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Methods of Documentation VII: EMR01:30

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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze
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使用计算机视觉识别电子健康记录任务和活动

Liem M Nguyen1, Amrita Sinha2, Adam Dziorny3

  • 1Department of Pediatric Critical Care, Stanford University School of Medicine, Stanford, California, United States.

Applied clinical informatics
|September 10, 2025
PubMed
概括

一个新的计算机视觉模型准确地分类电子健康记录 (EHR) 任务,并从屏幕记录中量化积极使用时间. 这种方法通过区分主动工作和不活动来改善EHR分析,提供了更精确的临床活动测量方法.

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科学领域:

  • 医疗信息学 医疗信息学
  • 计算机视觉应用程序 计算机视觉应用
  • 临床工作流分析分析

背景情况:

  • 电子健康记录 (EHR) 使用指标对于评估临床活动至关重要,但来自供应商的数据可能不反映实际用户体验.
  • 量化精确的EHR活动时间是具有挑战性的,因为难以翻译离散审计日志时间和测量不活动.
  • 现有的方法缺乏足够的数据来准确测量与电子健康记录系统积极接触的时间.

研究的目的:

  • 开发和验证基于计算机视觉的模型,用于分类EHR任务和检测任务变化.
  • 从临床医生会话屏幕记录中量化活跃的EHR使用时间.
  • 为了实现定制的EHR,使用原始审计日志数据使用指标.

主要方法:

  • 利用YOLOv8,Tesseract OCR和预定义的字典来对模拟和现实世界EHR屏幕录像进行任务分类和变更检测.
  • 开发了一个框架比较算法,以区分主动使用和不活动,从而实现主动时间的量化.
  • 与临床医生的注释对任务分类,任务变更识别和活动时间测量进行验证的模型性能.

主要成果:

  • 该模型在分类高层次EHR任务时达到94%的准确性,在检测任务变化时达到90.6%的灵敏度.
  • 积极使用的量化准确性因任务而异,对于显示明显视觉变化的任务 (例如,结果审查) 的错误率较低.
  • 后期分析表明,通过调整的不活动值,积极使用量化得到了改进.

结论:

  • 计算机视觉方法可用于识别EHR任务并测量在EHR系统中花费的活跃时间.
  • 这种方法提供了一个潜在的解决方案,用于定义情境敏感的值,以量化临床相关的活跃EHR时间.
  • 未来的研究应该专注于改进特定任务的值,并在各种临床环境中验证模型.