Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force.

Journal: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
PMID: 35779937

Abstract

Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.

Authors

  • William V Padula
    Department of Pharmaceutical and Health Economics, School of Pharmacy, University of Southern California, Los Angeles, CA, USA; The Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California, Los Angeles, CA, USA. Electronic address: padula@usc.edu.
  • Noémi Kreif
    Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK.
  • David J Vanness
    Department of Health Policy and Administration, College of Health and Human Development, Pennsylvania State University, Hershey, PA, USA.
  • Blythe Adamson
    Flatiron Health, New York, NY, USA.
  • Juan-David Rueda
    AstraZeneca, Cambridge, England, UK.
  • Federico Felizzi
    Novartis, Basel, Switzerland.
  • Pall Jonsson
    National Institute for Health and Care Excellence, London, UK.
  • Maarten J IJzerman
    Centre for Health Policy, School of Population and Global Health, University of Melbourne, Melbourne, Australia.
  • Atul Butte
    Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, United States of America.
  • William Crown
    The Heller School for Social Policy and Management, Brandeis University, Waltham, MA, USA. Electronic address: wcrown@brandeis.edu.

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