ELEVATE-GenAI: Reporting Guidelines for the Use of Large Language Models in Health Economics and Outcomes Research: An ISPOR Working Group Report.

Abstract

Objectives: Generative artificial intelligence (AI), particularly large language models (LLMs), holds significant promise for health economics and outcomes research (HEOR). However, standardized reporting guidance for LLM-assisted research is lacking. This article introduces the ELEVATE-GenAI framework and checklist-reporting guidelines specifically designed for HEOR studies involving LLMs.

Methods: The framework was developed through a targeted literature review of existing reporting guidelines, AI evaluation frameworks, and expert input from the ISPOR Working Group on Generative AI. It comprises 10 domains-including model characteristics, accuracy, reproducibility, and fairness and bias. The accompanying checklist translates the framework into actionable reporting items. To illustrate its use, the framework was applied to 2 published HEOR studies: one focused on a systematic literature review tasks and the other on economic modeling.

Results: The ELEVATE-GenAI framework offers a comprehensive structure for reporting LLM-assisted HEOR research, while the checklist facilitates practical implementation. Its application to the 2 case studies demonstrates its relevance and usability across different HEOR contexts.

Conclusions: Although the framework provides robust reporting guidance, further empirical testing is needed to assess its validity, completeness, usability, and generalizability across diverse HEOR use cases. The ELEVATE-GenAI framework and checklist address a critical gap by offering structured guidance for transparent, accurate, and reproducible reporting of LLM-assisted HEOR research. Future work will focus on extensive testing and validation to support broader adoption and refinement.