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

• Published in: Value in health Vol. 25; no. 7; pp. 1063 - 1080
• Main Authors: Padula, William V., Kreif, Noemi, Vanness, David J., Adamson, Blythe, Rueda, Juan-David, Felizzi, Federico, Jonsson, Pall, IJzerman, Maarten J., Butte, Atul, Crown, William
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2022
• Subjects: artificial intelligence; Internal Medicine; machine learning; Public Health; machine learning; artificial intelligence
• ISSN: 1098-3015; 1524-4733; 1524-4733
• DOI: 10.1016/j.jval.2022.03.022

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. •ISPOR convened a task force to establish emerging good practices in the use of machine learning (ML) methods for applications in health economics and outcomes research.•The task force identified methods that would appear suitable to support health economics and outcomes research endeavors in the domains of cohort selection, feature selection, predictive analytics, causal inference, and economic evaluation. Along with methods for these approaches are example case studies on how to conduct ML in each of these domains.•The task force examined the impact of ML on explainability and transparency of findings to providers and patients who may be the recipients of curated data. The PALISADE Checklist offers a series of considerations that researchers can use to explore whether ML adds value to traditional approaches to research.