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.
| Original language | English |
|---|---|
| Pages (from-to) | 1063-1080 |
| Number of pages | 18 |
| Journal | Value in Health |
| Volume | 25 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - Jul 2022 |
| Externally published | Yes |
Bibliographical note
Funding Information:Funding/Support: Dr Padula declares support by an unrestricted grant during the conduct of this task force from the US National Institutes of Health ( KL2 TR001854 ). There are no other funding sources to declare.
Funding Information:
Conflict of Interest Disclosures: Dr Padula reported receiving grants from the National Institutes of Health/Office of Extramural Research during the conduct of the study and reported receiving personal fees from Monument Analytics outside the submitted work. Dr Adamson reported receiving personal fees from Flatiron Health and Infectious Economics and reported stock ownership in Roche. Dr Rueda is employed by AstraZeneca. Dr Felizzi reported personal fees from Novartis and Roche and reported stock ownership in Roche. Dr IJzerman reported receiving grants and speaker fees from Illumina outside the submitted work. Dr Butte reported receiving personal fees from Samsung, Mango Tree Corporation, 10x Genomics, Helix, Pathway Genomics, Verinata (Illumina), Personalis NuMedii, Geisinger Health, Regenstrief Institute, Gerson Lehman Group, AlphaSights, Covance, Novartis, Genentech, Merck, Roche, Johnson and Johnson, Pfizer, Lilly, Takeda, Varian, Mars, Siemens, Optum, Abbott, Celgene, AstraZeneca, AbbVie, and Westat; reported stock ownership in Personalis, NuMedii, Apple, Facebook, Alphabet (Google), Microsoft, Amazon, Snap, 10x Genomics, Illumina, CVS, Nuna Health, Assay Depot, Vet24seven, Regeneron, Sanofi, Royalty Pharma, AstraZeneca, Moderna, Biogen, Paraxel, and Sutro; and reported royalties and stock from Stanford University for several patents and other disclosures licensed to NuMedii and Personalis outside the submitted work. Drs Padula, Vanness, and IJzerman are editors for Value in Health and had no role in the peer-review process of this article. No other disclosures were reported.
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© 2022