出院Sim:一个模拟基准,用于教育医生与患者在出院时的沟通
Zonghai Yao1,2, Michael Sun2, Won Seok Jang1,3
1Center for Healthcare Organization and Implementation Research, VA Bedford Health Care.
DischargeSim对医院访问后个性化患者教育的大型语言模型 (LLM) 进行了基准测试. 目前的LLM在为各种患者提供公平,有效的退院支持方面存在重大差距.
科学领域:
- 医疗保健中的人工智能
- 临床信息学 临床信息学
- 患者教育 患者教育
背景情况:
- 出院沟通对于患者护理过渡至关重要,将重点转向教育.
- 现有的大型语言模型 (LLM) 的基准主要评估诊断推理,忽视了访问后的患者支持.
- 退院后有效的患者教育仍然是LLM评估中未被充分探索的领域.
研究的目的:
- 引入 DischargeSim,这是一个新的基准来评估LLM作为个性化解雇教育者的能力.
- 评估LLM在模拟现实,多轮患者与医生的解诊后对话方面的表现.
- 通过对话质量,个性化文档生成和患者理解来衡量LLM的有效性.
主要方法:
- DischargeSim模拟了在LLM驱动的DoctorAgents和具有不同心理社会背景的PatientAgents之间的离院后对话.
- 谈话涵盖了六个临床出院主题,评估对话质量,文件生成 (摘要,检查清单) 和通过考试了解患者.
- 实验涉及18名LLM与各种模拟患者配置文件进行交互.
主要成果:
- 在LLM's免费教育能力方面观察到显著的绩效差距.
- 根据患者的心理社会资料,LLM的表现有很大差异.
- 较大的模型大小并不总是与改善的教育成果相关,这表明了战略性权衡.
结论:
- DischargeSim提供了一个基本的基准,用于评估LLMs在后访问临床教育.
- 目前的LLM在提供公平和个性化的患者出院支持方面存在局限性.
- 需要进一步发展,以提高LLM能力,以便有效的患者教育和释放后的沟通.
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