{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T19:41:41Z","timestamp":1790192501112,"version":"4.1.0"},"reference-count":19,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR AI"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The United States health insurance system is at a critical crossroads. Inflating costs, fragmented care, and administrative inefficiencies have revealed the limitations of the Fee-for-Service (FFS) model. This long-standing structure, while once effective in expanding access, now struggles to deliver efficiency and value. Value-based care (VBC) aims to realign incentives toward outcomes, quality, and efficiency. This article explores how artificial intelligence (AI) can serve as the digital backbone to accelerate the transition from FFS to VBC. The article reviews evidence from bundled payment programs and Accountable Care Organizations (ACOs), examines AI-driven frameworks for cost prediction, outcome measurement, and risk adjustment, and discusses associated challenges and future considerations using an illustrative case. Bundled payment models, such as the Comprehensive Care for Joint Replacement program, have shown average savings of approximately $1012 per episode; whereas, the ACO REACH model achieved average savings of roughly $930 per beneficiary, relative to FFS benchmarks. AI applications provide scalable solutions for forecasting costs, optimizing care coordination, and supporting preventive interventions. A case vignette in congestive heart failure illustrates how AI-enabled VBC can reduce episode costs by approximately 20% under favorable implementation conditions. AI has the potential to accelerate the scaling of VBC by enhancing its efficiency, equity, and sustainability. However, realizing this promise requires safeguards for data quality, interoperability, fairness, and transparency. In the AI era, the defining measure of health insurance will shift from the number of claims processed to the number of lives improved.<\/jats:p>","DOI":"10.2196\/84698","type":"journal-article","created":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T11:30:06Z","timestamp":1771759806000},"page":"e84698-e84698","source":"Crossref","is-referenced-by-count":5,"title":["Artificial Intelligence as a Catalyst for Value-Based Health Insurance in the United States: Narrative Review and Policy Perspective"],"prefix":"10.2196","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4823-6594","authenticated-orcid":false,"given":"Amol","family":"Kodan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"issue":"15","key":"R1","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1001\/jama.2019.13978","article-title":"Waste in the US health care system: estimated costs and potential for savings","volume":"322","author":"Shrank","journal-title":"JAMA"},{"key":"R2","unstructured":"High U.S. health care spending: where is it all going? The Commonwealth Fund. URL: https:\/\/www.commonwealthfund.org\/publications\/issue-briefs\/2023\/oct\/high-us-health-care-spending-where-is-it-all-going [Accessed 21-09-2025]"},{"key":"R3","unstructured":"Value-based payment as a tool to address excess US health spending. Health Affairs Brief. URL: https:\/\/www.healthaffairs.org\/do\/10.1377\/hpb20221014.526546\/full\/ [Accessed 21-09-2025]"},{"issue":"1","key":"R4","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1377\/hlthaff.2024.00842","article-title":"Current use and evaluation of artificial intelligence and predictive models in US hospitals","volume":"44","author":"Nong","journal-title":"Health Aff (Millwood)"},{"key":"R5","unstructured":"Comprehensive care for joint replacement (CJR) model: performance year 6 annual report\u2014executive summary. Centers for Medicare & Medicaid Services. 2024. URL: https:\/\/www.cms.gov\/priorities\/innovation\/data-and-reports\/2024\/cjr-py6-ar-exec-sum [Accessed 15-12-2025]"},{"issue":"Spec. No. 13","key":"R6","doi-asserted-by":"publisher","first-page":"SP1050","DOI":"10.37765\/ajmc.2024.89653","article-title":"Mandatory Medicare bundled payment and the future of hospital reimbursement","volume":"30","author":"Mechanic","journal-title":"Am J Manag Care"},{"issue":"9","key":"R7","first-page":"109","volume":"23","journal-title":"Hosp Case Manag"},{"key":"R8","unstructured":"ACO REACH financial and quality results data. CMS Data. URL: https:\/\/data.cms.gov\/cms-innovation-center-programs\/accountable-care-models\/aco-reach-financial-and-quality-results [Accessed 14-12-2025]"},{"issue":"24","key":"R9","doi-asserted-by":"publisher","first-page":"2357","DOI":"10.1056\/NEJMsa1600142","article-title":"Early performance of accountable care organizations in medicare","volume":"374","author":"McWilliams","journal-title":"N Engl J Med"},{"key":"R10","unstructured":"Comprehensive care for joint replacement (CJR) model: performance year 6 findings at a glance. CMS.gov. 2024. URL: https:\/\/www.cms.gov\/priorities\/innovation\/data-and-reports\/2024\/cjr-py6-ar-findings-aag [Accessed 15-12-2025]"},{"key":"R11","unstructured":"TEAM (transforming episode accountability model). CMS.gov. URL: https:\/\/www.cms.gov\/priorities\/innovation\/innovation-models\/team-model [Accessed 14-12-2025]"},{"issue":"1","key":"R12","doi-asserted-by":"publisher","first-page":"101","DOI":"10.36469\/jheor.2022.33951","article-title":"The potential cost and cost-effectiveness impact of using a machine learning algorithm for early detection of sepsis in intensive care units in Sweden","volume":"9","author":"Ericson","journal-title":"J Health Econ Outcomes Res"},{"issue":"14","key":"R13","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","journal-title":"N Engl J Med"},{"issue":"5","key":"R14","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/JBHI.2017.2767063","article-title":"Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis","volume":"22","author":"Shickel","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"R15","doi-asserted-by":"publisher","first-page":"142","DOI":"10.37765\/ajmc.2025.89697","article-title":"Reducing readmissions in the safety net through AI and automation","volume":"31","author":"Bennet","journal-title":"Am J Manag Care"},{"issue":"1","key":"R16","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2019-100109","article-title":"Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals","volume":"27","author":"Burdick","journal-title":"BMJ Health Care Inform"},{"issue":"7","key":"R17","doi-asserted-by":"publisher","DOI":"10.3390\/medicina59071276","article-title":"Machine learning for early prediction of sepsis in intensive care unit (ICU) patients","volume":"59","author":"Alanazi","journal-title":"Medicina (Kaunas)"},{"issue":"2","key":"R18","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3390\/hospitals1020015","article-title":"Revolutionizing patient safety: the economic and clinical impact of artificial intelligence in hospitals","volume":"1","author":"Epelde","journal-title":"Hospitals"},{"issue":"1","key":"R19","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","journal-title":"Nat Med"}],"container-title":["JMIR AI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ai.jmir.org\/2026\/1\/e84698","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:18:49Z","timestamp":1782843529000},"score":1,"resource":{"primary":{"URL":"https:\/\/ai.jmir.org\/2026\/1\/e84698"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,20]]},"references-count":19,"URL":"https:\/\/doi.org\/10.2196\/84698","relation":{},"ISSN":["2817-1705"],"issn-type":[{"value":"2817-1705","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,20]]},"article-number":"v5i15e84698"}}