Housing insecurity, an important determinant of mental health, worsened during the COVID-19 pandemic in the US. The federal Emergency Rental Assistance (ERA) program sought to reduce housing insecurity among low-income renters. Using 2021-23 Household Pulse Survey data, we employed a quasi-experimental design to assess the effects of ERA on anxiety and depression symptoms and on mental health care use. We conducted causal mediation analyses to determine whether and how ERA affected these outcomes through indirect effects-by alleviating housing insecurity-or through direct effects, which freed up resources to seek care. ERA significantly reduced anxiety and depression symptoms through both indirect and direct effects. Among renters with anxiety or depression symptoms, it increased psychotherapy use through direct effects. Future rental assistance programs could strengthen these dual impacts by including features to improve both housing and health status-for example, by streamlining applications and expediting benefit delivery to provide resources that recipients can use to address urgent mental health needs even before full housing stability is achieved.

The rapid growth of clinical artificial intelligence (AI) applications poses a unique challenge to Medicare's prospective payment systems, with two issues at the forefront. First, the productivity gains from AI may dwarf those of previous non-AI innovations, but they are uncertain and will take time to realize. Second, AI forces the Centers for Medicare and Medicaid Services (CMS) to think very differently about software. For many clinical applications of AI, the software itself is the service. It does not make sense to treat this expense as overhead, but direct reimbursement will likely exceed the true cost of using AI. Finally, although CMS currently pays for only a small fraction of all AI-enabled medical devices approved by the Food and Drug Administration, Medicare may already be spending more on AI as a result of additional AI-generated findings and follow-up care.

Drug overdose deaths involving prescription opioids rose by more than 400percent from 1999 to 2022, partly driven by overprescribing. Following 2016 Centers for Disease Control and Prevention guidelines, thirty-nine states instituted opioid prescribing limits (OPLs), with twelve adopting comprehensive OPLs combining maximum duration limits and dosage caps. However, the relationship between comprehensive OPLs and health outcomes remains unclear. Using data from the State Inpatient Databases and State Emergency Department Databases with a difference-in-differences design, we evaluated the association between state OPLs and opioid-related inpatient stays and emergency department (ED) visits during the period 2016-21. We found that the states' adoption of comprehensive OPLs was associated with a 6.2percent decline in opioid-related hospitalizations and an 11.9percent decline in opioid-related ED visits per 100,000 population per quarter. Stratified analyses showed that these associations were driven by adults ages25-44 and people residing in lower income quartiles of ZIP codes. In addition, the reductions were most pronounced in states with comprehensive OPLs that included dosage caps below 90 morphine milligram equivalents per day, whereas states with only duration limits or only dosage caps did not experience similar reductions. Our findings underscore the potential importance of comprehensive OPLs in mitigating opioid-related adverse health events.

After emergency brain surgery, a college student learned that her health care sharing plan would cover none of the costs.

The Medicaid Drug Rebate Program and the 340B Drug Pricing Program are generally understood as separate efforts to promote drug affordability-one by directly subsidizing low-income patients through insurance coverage (Medicaid) and the other by indirectly subsidizing safety-net clinics and hospitals (340B). Yet they interact in ways that can unintentionally raise costs for Medicaid. This Policy Insight examines how this interaction occurs, introduces two policy strategies to mitigate the interaction, and summarizes the use of these strategies across states as of 2024. We conclude with recommendations for how policy makers can weigh the costs and benefits of states' efforts to preserve Medicaid savings against lost revenue for 340B-participating organizations.

In 2019, pursuant to a Section 1115 waiver, Florida launched a Statewide Medicaid Managed Care housing assistance pilot to foster housing stability and reduce preventable health care use in adults with serious mental illness (SMI) or substance use disorder (SUD). We conducted a retrospective cohort study to examine the relationship between four housing support services provided in the pilot (transitional housing support, tenancy support, peer support, and crisis management) and health care use and health outcomes among 1,300 pilot enrollees during the period December2017-June2024. Transitional housing support services were associated with a 15percent increase in emergency department (ED) visits. Tenancy support services were associated with 51percent lower odds of all-cause mortality, and peer support was associated with a 20percent reduction in ED use. Crisis management was associated with a 45percent increase in ED visits, a 41percent increase in outpatient visits, and a 90percent increase in psychiatric hospitalizations. Findings highlight the value of tailored Medicaid housing interventions for people with SMI or SUD. Medicaid policy should prioritize high-value, data-driven housing interventions and protect them from budget cuts. Embedding such services within managed care contracts and aligning them with broader care coordination strategies offer a viable path for sustainability.

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.

The Inflation Reduction Act of 2022 authorized Medicare to negotiate prices for selected drugs, with implications for innovation. Using ClinicalTrials.gov data on biopharmaceutical trials initiated during 2015-24, we found that overall trial activity remained stable, returning to prepandemic levels, but manufacturers with drugs subject to negotiation initiated fewer trials, particularly in certain cancers.

National health expenditure growth between 2009 and 2019 slowed to less than half the historical rate of growth seen between 1970 and 2008. To identify why, we gathered actuarial projections of the fiscal effects of policies implemented between 2009 and 2019, netted these out from the 2009 Centers for Medicare and Medicaid Services baseline projections of national health expenditures, and decomposed the residual differences by payer and service to shed light on the spending slowdown. We identified four trends that contributed to spending growth below the baseline projections: declining utilization and substitution of lower-cost alternatives across hospitals, physicians, and pharmaceuticals; slow private hospital and physician price growth and the expanding scope of practice of nonphysicians in office-based settings; declining home health use among the oldest Medicaid beneficiaries; and slow growth in private insurers' administrative spending. Our results raise questions about several of the assumptions that underlay previous forecasts of future health care spending.

The proliferation of artificial intelligence (AI) clinical decision support technologies requires careful consideration of payment policies under the Medicare Physician Fee Schedule. In this Policy Insight, we identify key challenges faced by policy makers and the Centers for Medicare and Medicaid Services (CMS), and we discuss the interplay between the Medicare Physician Fee Schedule and the adoption of AI technologies in care delivery. The resource-based relative value scale methodology, which determines Medicare Physician Fee Schedule rates, was not designed for software-based technologies and requires both comprehensive adaptation and implementation reform. We recommend developing a policy framework for coverage determinations for AI technologies, reforming the resource-based relative value scale, creating clear reimbursement pathways for AI technologies, and implementing cost-effectiveness analysis to ensure that marginal Medicare expenditures yield proportional improvements in health care delivery and outcomes.