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Requires the Department of Health and Human Services (through HRSA) to build and maintain a real-time online dashboard tracking graduate medical education (GME) residency applications, match and fulfillment rates, training completion, and practice locations. The dashboard must aggregate and de-identify individual data, comply with federal privacy laws, integrate with existing systems, and be developed in partnership with other federal agencies and data holders. The Secretary must report to Congress starting two years after enactment and annually thereafter. The Act authorizes a one-time $1.5 million appropriation for FY2026 to implement the dashboard.
The bill promises better, more transparent GME and residency data to target clinician training and improve access in underserved areas, but it creates administrative costs, privacy and data-completeness risks, and relies on modest, one-year funding that may delay or limit sustained impact.
Rural and underserved communities, and patients who rely on Medicaid or have chronic conditions, will likely see improved access to clinicians over time because policymakers and health planners get real-time, integrated GME/residency data to better target training slots and recruitment to shortage areas.
Hospitals, residency programs, and medical educators will have clearer, timely information to plan GME positions, justify expansions, and align training capacity with local needs, improving the match between trainees and available residency slots.
Communities, researchers, and policymakers gain greater transparency through public aggregate, de-identified dashboards and regular evidence-based reporting, supporting better federal coordination and oversight of workforce investments.
Hospitals, training programs, and federal agencies will face increased administrative and implementation costs (and smaller/rural programs may face disproportionate compliance burdens) to collect, integrate, and maintain real-time GME data and reporting, potentially diverting funds and staff from direct services.
Applicants, residents, and medical students face heightened privacy and re-identification risks because collecting and integrating sensitive applicant-level data increases the chance of data breaches despite de-identification safeguards.
Reliance on external data sources, potential limits on access to non-public data, and use of a single federal metric risk incomplete or delayed dashboards and could exclude areas that local authorities view as underserved, limiting the usefulness of the tool for local workforce planning.
Introduced October 23, 2025 by Marsha Blackburn · Last progress October 23, 2025