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.