A reimbursement framework for artificial intelligence in healthcare
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-06-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-022-00621-w |
| _version_ | 1797428352277020672 |
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| author | Michael D. Abràmoff Cybil Roehrenbeck Sylvia Trujillo Juli Goldstein Anitra S. Graves Michael X. Repka Ezequiel “Zeke” Silva III |
| author_facet | Michael D. Abràmoff Cybil Roehrenbeck Sylvia Trujillo Juli Goldstein Anitra S. Graves Michael X. Repka Ezequiel “Zeke” Silva III |
| author_sort | Michael D. Abràmoff |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T08:56:43Z |
| format | Article |
| id | doaj.art-f43164895a1a46d5b08f31a3460a184b |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-09T08:56:43Z |
| publishDate | 2022-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-f43164895a1a46d5b08f31a3460a184b2023-12-02T12:56:34ZengNature Portfolionpj Digital Medicine2398-63522022-06-01511610.1038/s41746-022-00621-wA reimbursement framework for artificial intelligence in healthcareMichael D. Abràmoff0Cybil Roehrenbeck1Sylvia Trujillo2Juli Goldstein3Anitra S. Graves4Michael X. Repka5Ezequiel “Zeke” Silva III6Department of Ophthalmology and Visual Sciences, University of IowaAI Healthcare CoalitionOCHINDigital DiagnosticsIndiana University School of MedicineWilmer Eye Institute, Johns Hopkins UniversitySouth Texas RadiologyResponsible 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.https://doi.org/10.1038/s41746-022-00621-w |
| spellingShingle | Michael D. Abràmoff Cybil Roehrenbeck Sylvia Trujillo Juli Goldstein Anitra S. Graves Michael X. Repka Ezequiel “Zeke” Silva III A reimbursement framework for artificial intelligence in healthcare npj Digital Medicine |
| title | A reimbursement framework for artificial intelligence in healthcare |
| title_full | A reimbursement framework for artificial intelligence in healthcare |
| title_fullStr | A reimbursement framework for artificial intelligence in healthcare |
| title_full_unstemmed | A reimbursement framework for artificial intelligence in healthcare |
| title_short | A reimbursement framework for artificial intelligence in healthcare |
| title_sort | reimbursement framework for artificial intelligence in healthcare |
| url | https://doi.org/10.1038/s41746-022-00621-w |
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