Paying for artificial intelligence in medicine
Open accessAbstract
Over the past 7 years, regulatory agencies have approved hundreds of artificial intelligence (AI) devices for clinical use. In late 2020, payers began reimbursing clinicians and health systems for each use of select image-based AI devices. The experience with traditional medical devices has shown that per-use reimbursement may result in the overuse use of AI. We review current models of paying for AI in medicine and describe five alternative and complementary reimbursement approaches, including incentivizing outcomes instead of volume, utilizing advance market commitments and time-limited reimbursements for new AI applications, and rewarding interoperability and bias mitigation. As AI rapidly integrates into routine healthcare, careful design of payment for AI is essential for improving patient outcomes while maximizing cost-effectiveness and equity.
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Monetization and liability questions can hinder widespread adoption of AI, and the vast majority of commercially available AI devices are not currently reimbursed.
“Even with effective infrastructure and AI outputs, monetization and liability questions can hinder widespread adoption, where the vast majority of commercially available AI devices are not currently reimbursed (210).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [210]
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References 13 · Cited by 132
Research works printed but not linked: 2
- [16]Ellimoottil, C. Understanding the case for telehealth payment parity. https://www.healthaffairs.org/do/10.1377/forefront.20210503.625394/full/ (2021). · DOI
- [19]Verma, S., Coster, J. & Guram, J. Value-based purchasing rule for medicaid Rx drugs: continuing to shift from FFS towards accountability. https://www.healthaffairs.org/do/10.1377/forefront.20210119.109892/full/ (2021). · DOI
- Authors
- Parikh, R. B., & Helmchen, L. A.
- Affiliations
- Leonard Davis Institute of Health Economics (US)
- George Washington University (US)
- Corresponding author
- Parikh, Ravi B.
- Venue
- npj Digital Medicine
- Published
- 2022-05-20
- Volume
- 5
- Issue
- 1
- Pages
- 63-63
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC9123184
- OpenAlex
- W4280516220
- Cited by
- 132
- References
- 13
- Citations by year
- 2026: 30 · 2025: 38 · 2024: 34 · 2023: 22 · 2022: 7 · 2020: 1
- FWCI
- 7.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (K08CA263541)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Keywords
- artificial intelligence in healthcare, payment models, advance market commitments, interoperability, algorithmic bias mitigation, health technology assessment, cost-effectiveness, health equity
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