December ended with health IT developments that highlight many of the industry’s priorities – and challenges – heading into 2026. Artificial intelligence (AI) governance continued to evolve, interoperability efforts advanced (slowly), and life sciences saw positive results from AI-driven discovery methods. Below are seven health IT stories from December 2025 that stood out and why they matter as we move into 2026.
47 states move to regulate AI
In 2025, lawmakers in nearly every U.S. state introduced legislation focused on the use of AI in healthcare. Out of the 250 bills proposed, 33 have been signed into law across 21 states, according to a Manatt Health analysis from December. Most of these bills center on transparency, patient protection, and disclosure requirements. Moving forward, healthcare organizations will need to pay closer attention to how AI is governed – not just how it performs.
CMS introduces new coverage model for GLP-1 drugs
The Centers for Medicare & Medicaid Services (CMS) introduced a new drug pricing model to expand access to GLP-1 medications through Medicare and Medicaid. The program, called the Better Approaches to Lifestyle and Nutrition for Comprehensive hEalth, enables CMS to negotiate directly with GLP-1 manufacturers to lower the cost of these drugs. Beyond drug access, this new model could impact providers and payers managing drug use, prior authorizations, and outcomes.
FDA approves AI application to support liver disease trials
The FDA approved its first AI-based tool designed to help evaluate liver disease severity in clinical trials. Whereas traditional biopsy reviews often create bottlenecks in research, an AI-powered approach enables faster drug development. This is a prime example of AI being successfully integrated into a regulatory setting to aid experts and standardize time-consuming, data-intensive work.
AI-designed psoriasis drug shows strong trial results
On Dec. 18, 2025, the Japan-based pharma company Takeda reported positive late-stage clinical trial results for a psoriasis drug identified using AI-driven discovery methods. The results indicate that these approaches are starting to yield tangible, verifiable outcomes. For life sciences teams, it reinforces the importance of leveraging reliable clinical and research data throughout the development process.
ONC shares updates on TEFCA participation
The Office of the National Coordinator for Health IT (ONC) published updates in December on TEFCA adoption and CMS-aligned interoperability efforts. TEFCA, or the Trusted Exchange Framework and Common Agreement, is a framework designed to simplify the exchange of digital health information across various electronic networks. While participation continues to grow, challenges persist regarding trust, data consistency, and governance. This update indicates that nationwide data exchange is progressing in the right direction – albeit at a slower pace than some might prefer.
Obamacare enrollment declines
With federal subsidies expiring on Dec. 31, 2025, enrollment for health insurance under the Affordable Care Act (ACA) – commonly known as Obamacare – decreased for 2026, according to CMS. Monthly premiums for subsidized ACA plans are projected to more than double in 2026, impacting payer strategy and patient access. The shift could also touch health tech teams and revenue cycle leaders, as coverage instability often ripples into other areas, such as revenue cycle management (RCM) and downstream coding and compliance.
AI adoption increasing, but not scaling
A December KLAS report found that healthcare organizations are increasingly adopting AI to improve efficiency and reduce administrative burden. Health centers are primarily using ambient clinical intelligence and generative AI for lower-risk functions with clearer ROI, such as denials management, EHR documentation, and coding automation. The report also states that while most organizations are experimenting with or using AI, few have scaled it. Some of the barriers to widespread adoption include a lack of regulation and challenges in integrating AI tools into existing workflows.
