在HEOR中将生成AI与人类专业知识相结合:混合智能框架
Tushar Srivastava1, Hanan Irfan2, Vladimir Babiy3
1ConnectHEOR Limited, 5 St John's Lane, London, EC1 M4BH, UK. tushar.srivastava@connectheor.com.
Advances in therapy
|June 25, 2025
概括
本研究介绍了一种混合智能框架,将生成人工智能 (Gen-AI) 与人类专业知识相结合,以增强健康经济学和结果研究 (HEOR) 工作流程. 它为改善医疗保健决策和患者护理提供了一条实用的途径.
科学领域:
- 卫生经济学和结果研究 (HEOR)
- 医疗保健中的人工智能 (AI)
背景情况:
- 当前的HEOR工作流程面临着数据复杂性,资源限制和实时分析需求的挑战.
- 生成型人工智能 (Gen-AI) 通过增强人类能力,为解决这些HEOR挑战提供了重大机会.
- 将Gen-AI集成到HEOR中仍未得到充分探索,这给专业人士带来了不确定性.
研究的目的:
- 引入一种新的混合智能框架,将Gen-AI与HEOR的人类投入相结合.
- 加强关键的HEOR任务,包括健康经济模型概念化,证据综合和患者报告结果 (PRO) 评估.
主要方法:
- 该框架以技术采用理论 (TAM,UTAUT) 为基础,重点关注感知到的有用性,易用性,组织准备以及社会影响.
- 提出了两种可实施的方法:循环中的人类 (HITL) 和循环中的AI (AITL).
- 像检索增强生成 (RAG),图形RAG和提示工程等先进工具被用于可靠和上下文接地输出.
主要成果:
- 混合框架有效地将计算效率与人类洞察力相结合,用于HEOR任务.
- 它为将Gen-AI集成到现有的HEOR工作流程中提供了一个结构化的方法.
- 拟议的方法确保人工智能产生的输出可靠,与上下文相关,并与HEOR要求保持一致.
结论:
- 这项研究为Gen-AI在HEOR集成奠定了实际基础.
- 混合智能方法促进创新,并推动医疗保健研究的可操作结果.
- 该研究提供了一条改变HEOR工作流程,改善医疗保健决策和增强患者护理的途径.
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