与临床医生共同设计结构化专家启发式以增强运动肿瘤学的医疗保健决策。

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Medical Decision Making Pub Date : 2025-07-01 Epub Date: 2025-04-25 DOI:10.1177/0272989X251332967
Yufan Wang, Alexandra L McCarthy, Haitham Tuffaha
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引用次数: 0

摘要

虽然结构化专家启发(SEE)在数据匮乏的情况下在卫生技术评估中越来越受到关注,但其在实践中的应用仍然有限。与专家共同设计实用、符合目的的SEE,可提高其在临床研究中的可接受性和可行性。目的:与临床医生共同设计SEE,对运动肿瘤学决策分析模型中的3个不确定兴趣量(qoi)征求专家意见。方法召开一系列共同设计会议,设计6个启发阶段。个体启发采用可变间隔法(VIM),通过视频会议进行。采用线性池化方法产生群体估计。在启发过程后进行了半结构化访谈,以收集专家对启发过程的第一手经验,并确定需要改进的领域。对定性资料进行转录和内容分析。结果12位专家参与了共同设计的SEE。从专家的回答中得出并估计了三个beta分布:运动对早期子宫内膜癌存活妇女心血管事件的相对风险降低(平均值:0.362,SD: 0.15),临床医生将患者转介到运动项目的概率(平均值:0.457,SD: 0.218),以及癌症患者在转诊时使用此类健康服务的概率(平均值:0.446,SD: 0.203)。大多数专家对合作设计的SEE的第一手经验都是积极的。定性反馈强调了启发过程的关键方面,当针对没有SEE经验的临床医生时,应该谨慎设计和执行。结论首次在运动肿瘤学领域开展专家启发。事实证明,通过共同设计会议和纳入定性反馈,让不同利益相关者参与进来,在将专家启发引入临床研究方面是有效和实用的。最近的SEE指南旨在促进在基于模型的经济评估中进行专家启发,但其在实践中的应用仍然有限。让专家参与SEE的设计可以提高其在临床研究中的可接受性和可行性。这是第一个涉及运动肿瘤学领域临床医生的共同设计的专家启发。这种实施SEE的实用方法可以促进更广泛的采用,以便在缺乏证据或不确定的情况下为卫生保健政策决策提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Co-designing a Structured Expert Elicitation with Clinicians to Enhance Health Care Decision Making in Exercise Oncology.

BackgroundWhile structured expert elicitation (SEE) is gaining traction in health technology assessment in situations in which data are scarce, its application in practice remains limited. Co-designing a practical and fit-for-purpose SEE with experts could enhance its acceptability and feasibility in clinical research.ObjectivesAn SEE was co-designed with clinicians to elicit expert opinions on 3 uncertain quantities of interest (QoIs) for a decision-analytic model in exercise oncology.MethodsA series of co-design meetings was convened to design 6 elicitation stages. Individual elicitation was conducted using the variable interval method (VIM), via videoconferencing. Linear pooling was adopted to generate group estimates. Semi-structured interviews were conducted after the elicitation exercise to gather the experts' first-hand experience of the elicitation process and to identify areas for improvement. Qualitative data were transcribed and content analyzed.ResultsTwelve experts participated in the co-designed SEE. Three beta distributions were derived and estimated from the experts' responses: the relative risk reduction of cardiovascular events of exercise for women who survived early-stage endometrial cancer (Mean: 0.362, SD: 0.15), the probability that a clinician would refer a patient to the exercise program (Mean: 0.457, SD: 0.218), and the probability that a cancer patient would use such a health service upon referral (Mean: 0.446, SD: 0.203). Most of the experts' first-hand experience of the co-designed SEE was positive. The qualitative feedback highlighted critical aspects of the elicitation process that should be designed and executed with caution when targeting clinicians with no prior experience of SEE.ConclusionsThis is the first expert elicitation conducted in exercise oncology. Engaging diverse stakeholders through co-design meetings and incorporating qualitative feedback proved effective and practical in introducing expert elicitation into clinical research.HighlightsRecent SEE guidelines aim to facilitate the conduct of expert elicitation in model-based economic evaluation, but its application in practice remains limited.Engaging experts in the design of SEE could enhance its acceptability and feasibility in clinical research.This is the first co-designed expert elicitation involving clinicians in the field of exercise oncology.This practical approach to conducting SEE could promote a wider adoption to inform health care policy decisions when the evidence is lacking or uncertain.

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来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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