专题燃烧:一种人工智能驱动的策略,用于管理患者的消息负载,并减少燃烧
Stephon N Proctor1,2, Greg Lawton3, Shikha Sinha1
1Behavioral Health, The Children's Hospital of Philadelphia, Philadelphia, United States.
Applied clinical informatics
|April 8, 2025
概括
电子健康记录 (EHR) 中的大型语言模型 (LLM) 减少了患者消息响应时间. 然而,人工智能草案的响应效率因角色和专业而异,表明需要个性化人工智能实施.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床工作流的优化 临床工作流优化
背景情况:
- 电子健康记录 (EHR) 中的患者信息对临床医生和工作人员的工作负担作出了重大贡献.
- 有效管理患者通信对于提供优质医疗保健至关重要.
- 人工智能 (AI) 的整合为简化通信任务提供了潜在的解决方案.
研究的目的:
- 评估大型语言模型 (LLM) 对临床医生和辅助人员在电子健康记录系统中生成患者信息响应草案时的工作量和效率的影响.
- 评估与人工智能生成的答案草案相关的利用率和时间节省.
主要方法:
- 与Epic Systems合作进行了一项试点研究,实施OpenAI的ChatGPT 4.0以响应患者消息.
- 该研究从2023年8月到2024年7月,涉及13个门诊专业的323名参与者.
- 收集的数据包括草稿利用率和消息响应时间,使用统计方法进行分析.
主要成果:
- 总体平均生成草案利用率为38%,临床医生 (43%) 的使用率高于安排人员 (33%).
- 人工智能生成的草稿显著减少了13秒的平均消息响应时间,支持人员比临床医生 (3秒) 更受益 (23秒).
- 使用率和时间节省在不同角色和专业之间显示出显著的变化.
结论:
- 实施LLM来起草患者消息回复可以有效地减少响应时间和减轻消息负担.
- 人工智能产生的反应的有效性取决于环境,因临床作用和专业而异,需要量身定制的实施策略.
- 建议进一步开发和个性化人工智能工具,以优化实用性,并确保在各种临床环境中安全有效地应用.
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