使用大型语言模型进行主动的多药房管理:增强老年护理的机会
Arya Rao1,2,3, John Kim1,2,3, Winston Lie1,2,3
1Harvard Medical School, Boston, MA, USA.
Journal of medical systems
|April 17, 2024
概括
像ChatGPT这样的大型语言模型 (LLM) 显示了通过提供减压推来帮助多药房管理的潜力. 他们的决定与临床因素保持一致,这表明未来对初级保健医生的支持.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 老年人药理疗的药理疗方法
背景情况:
- 多药在管理复杂患者方面面临重大挑战,特别是随着人口老龄化和初级保健短缺.
- 有效的多药管理策略对于减少医疗保健负担至关重要.
- 人工智能 (AI),特别是大型语言模型 (LLM) 在多药房管理中的作用仍然在很大程度上未被评估.
研究的目的:
- 评估ChatGPT,一个大型语言模型,在为多种药物治疗的患者做出抑郁症决定时的性能.
- 评估ChatGPT在复杂患者的药物管理中帮助临床决策的能力.
主要方法:
- 最初用于研究全科医生下药决定的标准化临床细节被输入了ChatGPT 3.5.5.
- 基于二进制的是/否下药建议和基于列表的药物选择提示,评估了ChatGPT的性能.
- 记录和分析了关于停止处方决定的反应,包括药物的数量和类型.
主要成果:
- 在没有心血管疾病 (CVD) 病史的患者中,ChatGPT普遍建议在不考虑日常生活活动 (ADL) 状态的情况下减轻处方.
- 在患有心血管疾病史的患者中,ChatGPT的建议在技术复制品中各不相同.
- 推放弃处方的药物数量 (共7种药物中的2.67-3.67) 随着ADL损伤严重程度的增加而增加,但不受心血管疾病状态的影响.
- 聊天GPT首选推降低处方的止痛药.
结论:
- 根据ADL状态,心血管疾病史和药物类型,ChatGPT的抑郁决定显示出变化,这表明与临床推理有一定的一致性.
- 这些发现表明,经过专门培训的LLM可以为初级保健医生提供有价值的临床支持,帮助他们管理多药房.
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