一个新一代的患者报告的结果指标,使用大型语言模型
Jan Henrik Terheyden1, Maren Pielka2,3, Tobias Schneider2,3
1University Hospital Bonn, Department of Ophthalmology, Venusberg-Campus 1, 53127, Bonn, Germany. jan.terheyden@ukbonn.de.
Journal of patient-reported outcomes
|March 24, 2025
概括
大型语言模型 (LLM) 可以通过实现更具交互性的数据收集和实时解释来增强患者报告的结果指标 (PROM). 这项创新有望提高患者报告数据的有效性和临床应用.
科学领域:
- 数字健康数字健康
- 人工智能在医学中的应用
- 患者报告的结果
背景情况:
- 患者报告结果指标 (PROM) 对于以患者为中心的护理至关重要,但由于其结构化的格式,它们在现实世界的有效性和患者接受性方面存在局限性.
- 对利用PROM数据的临床兴趣日益增长,突出了对更具适应性和可访问性的方法的需求.
- 本评论探讨了大型语言模型 (LLM) 的潜力,以解决PROM数据收集和解释中的这些限制.
研究的目的:
- 展示大型语言模型 (LLM) 在患者报告结果测量 (PROM) 数据的收集和解释中的潜在应用.
- 要突出LLMs如何克服传统的,高度结构化的PROM问卷的局限性.
- 引入LLM生成PROMs (LLM-PROMs) 的概念,以提高临床效用.
主要方法:
- 建议开发新一代PROM,利用LLMs进行数字管理和基于AI的实时评分.
- 强调需要多方利益相关者的投入和严格验证LLM-PROM与既定措施的相关性.
- 建议LLM-PROM作为传统PROM的补充工具,特别是在现实世界的临床环境中.
主要成果:
- 通过不太结构化的患者输入,LLM-PROM有潜力量化患者相关的维度.
- 这种方法可以促进在数字临床应用中更好地利用患者报告的数据.
- 临床医疗系统可以促进更动态和互动的患者数据收集.
结论:
- LLM-PROMs为推进患者报告结果测量提供了一个有希望的途径.
- 这些新型PROM可以提高患者数据在数字医疗保健中的整合和实用性.
- 开发和验证LLM-PROM是实现其在临床实践中的全部潜力的关键.
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