Roles for AI in Implementation of Medicaid Work Requirements
By January 2027, 44 state Medicaid programs must redetermine the eligibility of millions of beneficiaries under criteria in the 2025 budget reconciliation law (HR 1). Among the changes are “community engagement” (work) requirements and increased redetermination cadence (from annual to at least semiannual). In preparation, state Medicaid agencies have been exploring technological solutions to automate redeterminations at scale, including artificial intelligence (AI). Six agencies have decided to use AI tools so far and 21 states are considering use of them. Strategic deployment of AI can help states tackle the redetermination juggernaut, but overly broad or hasty deployment could compound the harms of HR 1.
Medicaid eligibility determinations are an increasing source of administrative strain. A faint silver lining to the new requirements of HR 1 may be the technological innovation it inspires. Investment by state programs in new tools could offer a practical, lasting solution if they deploy AI where it can help, not harm.
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The Healthcare Policies of the November 2026 Candidates
Recent polls show that healthcare costs are a top affordability concern for American adults and that a large majority of adults, 90% of Democrats and 75% of Republicans, support a fundamental redesign or major changes to our healthcare system. Across states, political parties, and different types of office, politicians and constituents hold different views on healthcare policies and support different proposals. Various stakeholders such as patients, physicians, nurses, veterans, employers, and industry groups could potentially benefit from access to data on how their representatives’ views align with their own. There is no systematic, openly available database and analysis of these views. We sought to create the first systematic, openly available database and analysis of the healthcare policies and proposals of the leading candidates in the November 2026 elections and beyond.
Recent polls show that healthcare costs are a top affordability concern for American adults and that a large majority of adults, 90% of Democrats and 75% of Republicans, support a fundamental redesign or major changes to our healthcare system. Across states, political parties, and different types of office, politicians and constituents hold different views on healthcare policies and support different proposals. Various stakeholders such as patients, physicians, nurses, veterans, employers, and industry groups could potentially benefit from access to data on how their representatives’ views align with their own. There is no systematic, openly available database and analysis of these views. We sought to create the first systematic, openly available database and analysis of the healthcare policies and proposals of the leading candidates in the November 2026 elections and beyond.
Addressing Medical Affordability Without Compromising Care
The US spends 25 percent of all health care costs on administration, a big part of which involves billing for medical services. To realize savings requires standardizing and digitizing billing.
The US spends 25 percent of all health care costs on administration, a big part of which involves billing for medical services. To realize savings requires standardizing and digitizing billing.
In January 2026, Utah announced a first-of-its kind pilot program allowing an autonomous artificial intelligence (AI) agent to renew prescriptions for consumers who request it. The state agreed not to enforce its unprofessional conduct laws against the developer, Doctronic, if the company adheres to a contract that includes safety and privacy protections. The pilot program includes 192 drugs for chronic conditions. Although physicians will initially validate the AI’s actions, the pilot program will swiftly become one of the first deployments at scale of an autonomous, agentic system in medicine. The announcement prompted concern from associations of physicians and pharmacists who opined that AI “should NOT be making care decisions.”
In January 2026, Utah announced a first-of-its kind pilot program allowing an autonomous artificial intelligence (AI) agent to renew prescriptions for consumers who request it. The state agreed not to enforce its unprofessional conduct laws against the developer, Doctronic, if the company adheres to a contract that includes safety and privacy protections. The pilot program includes 192 drugs for chronic conditions. Although physicians will initially validate the AI’s actions, the pilot program will swiftly become one of the first deployments at scale of an autonomous, agentic system in medicine. The announcement prompted concern from associations of physicians and pharmacists who opined that AI “should NOT be making care decisions.”
The AI Arms Race In Health Insurance Utilization Review: Promises Of Efficiency And Risks Of Supercharged Flaws
Health insurers and health care provider organizations are increasingly using artificial intelligence (AI) tools in prior authorization and claims processes. AI offers many potential benefits, but its adoption has raised concerns about the role of the “humans in the loop,” users’ understanding of AI, opacity of algorithmic determinations, underperformance in certain tasks, automation bias, and unintended social consequences. To date, institutional governance by insurers and providers has not fully met the challenge of ensuring responsible use. However, several steps could be taken to help realize the benefits of AI use while minimizing risks. Drawing on empirical work on AI use and our own ethical assessments of provider-facing tools as part of the AI governance process at Stanford Health Care, we examine why utilization review has attracted so much AI innovation and why it is challenging to ensure responsible use of AI. We conclude with several steps that could be taken to help realize the benefits of AI use while minimizing risks.
Health insurers and health care provider organizations are increasingly using artificial intelligence (AI) tools in prior authorization and claims processes. AI offers many potential benefits, but its adoption has raised concerns about the role of the “humans in the loop,” users’ understanding of AI, opacity of algorithmic determinations, underperformance in certain tasks, automation bias, and unintended social consequences. To date, institutional governance by insurers and providers has not fully met the challenge of ensuring responsible use. However, several steps could be taken to help realize the benefits of AI use while minimizing risks. Drawing on empirical work on AI use and our own ethical assessments of provider-facing tools as part of the AI governance process at Stanford Health Care, we examine why utilization review has attracted so much AI innovation and why it is challenging to ensure responsible use of AI. We conclude with several steps that could be taken to help realize the benefits of AI use while minimizing risks.