A reimbursement framework for artificial intelligence in healthcare
Open accessAbstract
Responsible adoption of healthcare artificial intelligence (AI) requires that AI systems which benefit patients and populations, including autonomous AI systems, are incentivized financially at a consistent and sustainable level. We present a framework for analytically determining value and cost of each unique AI service. The framework’s processes involve affected stakeholders, including patients, providers, legislators, payors, and AI creators, in order to find an optimum balance among ethics, workflow, cost, and value as identified by each of these stakeholders. We use a real world, completed, an example of a specific autonomous AI service, to show how multiple “guardrails” for the AI system implementation enforce ethical principles. It can guide the development of sustainable reimbursement for future AI services, ensuring the quality of care, healthcare equity, and mitigation of potential bias, and thereby contribute to realize the potential of AI to improve clinical outcomes for patients and populations, improve access, remove disparities, and reduce cost.
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Ethics committees play a crucial role in stressing the importance of adherence to regulation and scrutinizing potential ethical challenges, including privacy concerns, fairness, and transparency.
“Though not martinet, the ethics committees play a crucial role in stressing the importance of adherence to regulation and scrutinizing potential ethical challenges, including privacy concerns, fairness, and transparency [57].”
From Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education (2024) · cites this paper as [57]
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References 27 · Cited by 117
Research works printed but not linked: 4
- [13]American Medical Association (A. M. A.) Board of Trustees Policy Summary. Augmented intelligence in healthcare. Updated 4 Nov. 2019. https://www.ama-assn.org/system/files/2019-08/ai-2018-board-policy-summary.pdf (2019).
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- Authors
- Abràmoff, M. D., Roehrenbeck, C., Trujillo, S., Goldstein, J., Graves, A. S., Repka, M. X., & Silva III, E. “.
- Affiliations
- University of Iowa (US)
- Hogan Lovells (United States) (US)
- Ochin (US)
- Indiana University School of Medicine (US)
- Johns Hopkins University (US)
- Corresponding author
- Abràmoff, Michael D.
- Venue
- npj Digital Medicine
- Published
- 2022-06-09
- Volume
- 5
- Issue
- 1
- Pages
- 72-72
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC9184542
- OpenAlex
- W4281706229
- Cited by
- 117
- References
- 27
- Citations by year
- 2026: 29 · 2025: 27 · 2024: 27 · 2023: 32 · 2022: 2
- FWCI
- 6.63
- 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)
- IUScholarWorks (Indiana University) (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Keywords
- artificial intelligence in healthcare, reimbursement, autonomous AI systems, stakeholder analysis, AI ethics, health care equity, algorithmic bias, payers, clinical workflow
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