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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...

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Autores principales: Abràmoff, Michael D., Roehrenbeck, Cybil, Trujillo, Sylvia, Goldstein, Juli, Graves, Anitra S., Repka, Michael X., Silva III, Ezequiel “Zeke”
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184542/
https://www.ncbi.nlm.nih.gov/pubmed/35681002
http://dx.doi.org/10.1038/s41746-022-00621-w
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author Abràmoff, Michael D.
Roehrenbeck, Cybil
Trujillo, Sylvia
Goldstein, Juli
Graves, Anitra S.
Repka, Michael X.
Silva III, Ezequiel “Zeke”
author_facet Abràmoff, Michael D.
Roehrenbeck, Cybil
Trujillo, Sylvia
Goldstein, Juli
Graves, Anitra S.
Repka, Michael X.
Silva III, Ezequiel “Zeke”
author_sort Abràmoff, Michael D.
collection PubMed
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.
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spelling pubmed-91845422022-06-11 A reimbursement framework for artificial intelligence in healthcare Abràmoff, Michael D. Roehrenbeck, Cybil Trujillo, Sylvia Goldstein, Juli Graves, Anitra S. Repka, Michael X. Silva III, Ezequiel “Zeke” NPJ Digit Med Comment 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. Nature Publishing Group UK 2022-06-09 /pmc/articles/PMC9184542/ /pubmed/35681002 http://dx.doi.org/10.1038/s41746-022-00621-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Comment
Abràmoff, Michael D.
Roehrenbeck, Cybil
Trujillo, Sylvia
Goldstein, Juli
Graves, Anitra S.
Repka, Michael X.
Silva III, Ezequiel “Zeke”
A reimbursement framework for artificial intelligence in healthcare
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
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184542/
https://www.ncbi.nlm.nih.gov/pubmed/35681002
http://dx.doi.org/10.1038/s41746-022-00621-w
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