Symptom burden in multiple long-term conditions: An AI-supported, mixed-methods concept elicitation study.

IF 0.7
JRSM Open Pub Date : 2026-07-15 eCollection Date: 2026-07-01 DOI:10.1177/20542704261459717
Sarah E Hughes, Benjamin M A Hughes, Shamil Haroon, Eleanor Hathaway, Nicola Anderson, Christel McMullan, Michael Newnham, Clare Taylor, Alastair Denniston, Steven Backhouse, Andrew Filer, Karim Raza, Julia Scarisbrick, Clare Anderson, Matthew Lee, Lee Watts, Victoria Hodgetts Morton, Sarah Hillman, Faraz Mughal, Ed Day, Tariq Sami, Aliza Jesani, Thomas Jackson, Philip Collis, John Devin Peipert, Melanie Calvert
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引用次数: 0

Abstract

Objective: To develop the conceptual framework for an MLTC-specific patient-reported outcome measure (PROM), the Symptom Burden Questionnaire™ for MLTC (SBQ™-MLTC).

Design: Mixed-methods study: (1) symptom list generation; (2) assessment of list face validity; and (3) construction of a conceptual framework.

Setting: Concept elicitation and conceptual framework development using existing PROMs identified through the Mapi Research Trust PROQOLID eCOA database and AI-generated symptom lists.

Participants: Fifty-one condition-specific PROMs with evidence of patient involvement during concept elicitation were included for symptom extraction. ChatGPT-4 generated symptom lists for 24 conditions prevalent in MLTC. Seventeen healthcare practitioners reviewed symptom relevance and contributed to refinement of the conceptual framework.

Main outcome measures: Identification, refinement, and organisation of relevant MLTC symptoms into body system and functional domains, and development of the SBQ™-MLTC's conceptual framework.

Results: ePROVIDE searches in July and August 2023 identified 51 condition-specific PROMs for 24 conditions prevalent in MLTC. ChatGPT-4 was prompted to generate a list of 75 symptoms for each condition. A merged list of 2202 symptoms was iteratively reduced to 190 symptoms for healthcare practitioner review. The final conceptual framework included 151 symptoms spanning 18 body system and functional domains.

Conclusions: This study represents the first phase in the development of an MLTC-specific PROM of symptom burden. Generative AI output triangulated with content from existing PROMs and healthcare practitioner review proved a feasible approach to concept elicitation. Planned cognitive debriefing will confirm content validity of the SBQ™-MLTC for people with lived experience. In the future, the SBQ™-MLTC could support integrated, symptom-led approaches for clinical management of MLTC.

多种长期条件下的症状负担:一项人工智能支持的混合方法概念启发研究。
目的:开发MLTC特异性患者报告结果测量(PROM)的概念框架,MLTC症状负担问卷™(SBQ™-MLTC)。设计:混合方法研究:(1)症状表生成;(2)清单面效度评估;(3)概念框架的构建。设置:使用通过Mapi研究信托PROQOLID eCOA数据库和人工智能生成的症状列表确定的现有prom进行概念启发和概念框架开发。参与者:51个有证据表明患者参与概念激发的特定条件的prom被纳入症状提取。ChatGPT-4生成了MLTC中常见的24种病症的症状列表。17名医疗保健从业人员回顾了症状相关性,并对概念框架的改进做出了贡献。主要结果测量:识别、细化和组织相关MLTC症状到身体系统和功能领域,以及SBQ™-MLTC概念框架的发展。结果:在2023年7月和8月的e提供搜索中,确定了MLTC中常见的24种疾病的51种特定条件的prom。ChatGPT-4被提示为每种病症生成一个包含75种症状的列表。2202个症状的合并列表迭代减少到190个症状供保健医生审查。最终的概念框架包括跨越18个身体系统和功能领域的151种症状。结论:这项研究代表了mltc特异性早PROM症状负担发展的第一阶段。生成式人工智能输出与现有prom和医疗保健从业者审查的内容进行三角测量,证明是一种可行的概念启发方法。有计划的认知汇报将确认SBQ™-MLTC对有生活经验的人的内容有效性。在未来,SBQ™-MLTC可以支持综合的、以症状为主导的MLTC临床管理方法。
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来源期刊
自引率
0.00%
发文量
16
审稿时长
12 weeks
期刊介绍: JRSM Open is a peer reviewed online-only journal that follows the open-access publishing model. It is a companion journal to the Journal of the Royal Society of Medicine. The journal publishes research papers, research letters, clinical and methodological reviews, and case reports. Our aim is to inform practice and policy making in clinical medicine. The journal has an international and multispecialty readership that includes primary care and public health professionals.
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