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

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.