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Use of Large Language Models to Extract Cost-Effectiveness Analysis Data: A Case Study.

Xujun Gu1, Hanwen Zhang2, Divya Patil3

  • 1Department of Practice, Sciences, and Health Outcomes Research, University of Maryland Baltimore, Baltimore, MD, USA.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|June 6, 2025
PubMed
Summary

Large language models (LLMs) show promise for automating cost-effectiveness analysis (CEA) data extraction, achieving accuracy comparable to established registries. Human oversight remains crucial for complex data points.

Keywords:
artificial intelligence (AI)cost-effectiveness analysis (CEA)data extractionlarge language models (LLMs)

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Area of Science:

  • Health economics
  • Artificial intelligence in healthcare
  • Medical informatics

Background:

  • Cost-effectiveness analysis (CEA) is vital for health economic research but relies on manual data collection, which is labor-intensive and error-prone.
  • Advancements in artificial intelligence (AI) and large language models (LLMs) present opportunities to automate data extraction for CEA.

Purpose of the Study:

  • To evaluate the accuracy of LLM-based data extraction for CEA.
  • To assess the feasibility of using LLMs to support CEA data collection.

Main Methods:

  • A custom ChatGPT model (GPT) was compared against the Tufts CEA Registry (TCRD) and researcher-validated data (RVE).
  • The models extracted 36 predefined variables from 34 structured articles.
  • Concordance rates and accuracy differences were calculated and statistically analyzed.

Main Results:

  • GPT demonstrated accuracy comparable to TCRD (GPT vs. RVE: mean 0.88, TCRD vs. RVE: mean 0.90).
  • GPT excelled in extracting 'Population and Intervention Details' but faced challenges with complex variables like 'Utility.'

Conclusions:

  • LLMs like GPT show potential for automating CEA data extraction, offering accuracy similar to existing methods.
  • Human supervision is essential to manage complexities and ensure accuracy, especially for intricate variables.