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Machine Learning-Assisted Health Economics and Policy Reviews: A Comparative Assessment.

Ludovico Cavallaro1, Vittoria Ardito1, Michael Drummond1,2

  • 1Center for Research on Health and Social Care Management (CERGAS), SDA Bocconi School of Management, Via Sarfatti, 10, 20136, Milan, MI, Italy.

Applied Health Economics and Health Policy
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PubMed
Summary
This summary is machine-generated.

Machine learning tools can significantly speed up literature reviews in health economics and policy by improving title and abstract screening efficiency. This technology shows high reliability, potentially saving considerable reviewer time.

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Area of Science:

  • Health Economics
  • Policy Analysis
  • Bibliometrics

Background:

  • The exponential growth of scientific literature poses significant challenges for conducting comprehensive literature reviews in health economics and policy.
  • Efficiently screening titles and abstracts is crucial for managing the volume of research and ensuring the quality of evidence synthesis.

Purpose of the Study:

  • To evaluate the effectiveness and reliability of a machine learning (ML) tool in enhancing the title and abstract screening process for health economics and policy literature reviews.
  • To assess the potential of ML tools to streamline systematic reviews and improve reviewer efficiency.

Main Methods:

  • A machine learning tool, ASReview, was employed in 'Simulation Mode' to assess its performance in identifying relevant records (RRF) from a dataset of 10,246 unique records.
  • Performance was evaluated across scenarios with varying prior knowledge (5, 10, 15 records) using sampling and heuristic stopping criteria, with 100 simulations per scenario.

Main Results:

  • The ML tool achieved a median RRF of 97% after screening only 25% of the sample, indicating substantial time savings (approx. 32 working days).
  • While higher prior knowledge improved initial performance, the ML tool's accuracy remained robust throughout the screening process.
  • The heuristic criterion showed comparable median RRF but exhibited greater variability due to premature stopping.

Conclusions:

  • Machine learning tools offer a promising solution for enhancing the efficiency of title and abstract screening in health economics and policy research.
  • Establishing clear guidelines for ML-assisted reviews is vital to maintain rigorous evidence quality standards and ensure the reliability of systematic reviews.