Utilizing Large Language Models to Enhance Patient-Reported Outcome Measures: Application to the EQ-5D-5L and Bolt-ons.

OBJECTIVES: Large language models (LLMs) may be useful tools for the development/adaptation of patient-reported outcome measures (PROMs). As a methodological proof-of-concept, we evaluated the use of LLMs to support the identification of potential EQ-5D-5L bolt-on dimensions, using patient-reported free-text data. METHODS: We used GPT-4o to analyze text data from 1,977 members of the Dutch Celiac Association, who completed the EQ-5D-5L and narratively described the impact of celiac disease on their lives. Prompts were designed to identify potential EQ-5D-5L bolt-on dimensions and produce preliminary item wordings for selected dimensions. Evaluations comprised: comparisons of dimensions identified using two alternative approaches (qualitative analysis and topic modelling) conducted on a subset of 85 text entries; text-entry level agreement (Kappa) between LLM and qualitatively identified dimensions; and suitability of LLM-generated item wordings assessed against existing criteria. RESULTS: The LLM identified 12 potential bolt-on dimensions to the EQ-5D-5L, of which 9 were also identified using qualitative analysis, and 5 using topic modelling. Text-entry level agreement between the LLM and qualitative approaches was 'moderate', 'substantial' or 'almost perfect', with two exceptions of slight/fair agreement (median Kappa=0.68, IQR=0.56-0.71). Sensitivity analyses using four other LLMs produced similar agreement results. The LLM-generated item wordings for the 4 most common dimensions scored 4.0-4.4 out of 5 when assessed against existing criteria. CONCLUSIONS: This study demonstrates the potential of LLMs to support the development/modification of PROMs based on patient-reported text data. Further research should assess the approach's transferability across disease areas and data sources, while better incorporating patient/stakeholder input throughout.