关于在医疗保健决策中使用结构化专家诱导协议的建议:ISPOR工作组的良好实践报告
Marta Soares1, Abigail Colson2, Laura Bojke1
1Centre for Health Economics, University of York, York, England, UK.
概括
结构化专家诱导 (SEE) 用有限的证据量化医疗保健决策的专家知识. 本报告审查了五个SEE协议,以提高不确定性评估的透明度和一致性.
科学领域:
- 卫生经济学和结果研究研究.
- 决策分析 决策分析
- 证据综合研究
背景情况:
- 医疗保健决策依赖于价值评估,这些评估往往受到不确定的,不完整的或缺失的证据的阻碍.
- 这种不确定性在先进疗法,精准医学和罕见疾病方面尤为明显.
研究的目的:
- 引入和审查五个共同的结构化专家诱导 (SEE) 协议.
- 讨论SEE在医疗保健决策环境中的应用,优点和弱点.
- 为这个新兴领域的未来研究提供建议.
主要方法:
- 五个结构化专家诱导 (SEE) 协议的定性审查和描述:谢菲尔德诱导框架,修改的德尔菲方法,库克的经典方法,调查,讨论,估计,汇总协议和医学研究委员会参考协议.
- 分析通用元素和不同协议的不同实现.
- 讨论在医疗保健决策中实施SEE的限制和考虑.
主要成果:
- 详细介绍了五个不同的SEE协议,突出了它们在引起和汇总专家判断方面的共同点和差异.
- 该报告概述了SEE在从专家知识中生成概率分布的实用性,特别是在经验证据稀缺的情况下.
- 在医疗保健决策背景下考虑实际约束以及每个协议的比较优缺点.
结论:
- 结构化专家诱导 (SEE) 是在不确定性下为医疗保健决策提供信息的宝贵工具.
- 选择SEE协议应与特定的决策环境和可用的资源保持一致.
- 建议进行进一步的研究,以完善SEE实践并加强其在医疗保健中的应用.
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