Healthcare and technology leaders convened at the with a shared sense of urgency as the Federal health ecosystem is undergoing one of its most significant transformations in decades. Across panel sessions, discussions highlighted both the structural challenges and strategic investments shaping Government health agencies, from modernizing public health data infrastructure to addressing long-standing interoperability barriers that have fragmented care delivery.
Five critical insights emerged that define a path toward a more connected, data-driven and patient-centered Federal healthcare system.
Federal AI Policy Is Being Rebuilt Around Coordination, Not Fragmentation听
Leaders from the Department of Health and Human Services (HHS) emphasized that agency-by-agency artificial intelligence (AI) experimentation is ending. With dozens of programs across its divisions, HHS has restructured its AI strategy around three coordinated pillars: regulation, reimbursement and research/development.
Historically fragmented efforts created conflicting signals and limited cross-agency innovation. Now, the Secretary鈥檚 office serves as an alignment layer, ensuring regulatory decisions at the Food and Drug Administration (FDA), reimbursement policies at the Centers for Medicare and Medicaid Services (CMS) and research investments at the Advanced Research Projects Agency for Health (ARPA-H) are coordinated. The goal is not to expand Government roles, but to remove barriers and accelerate adoption of existing technologies.
The FDA is rethinking how AI-enabled medical technologies are regulated. After authorizing more than 1,000 AI and machine learning products, primarily in radiology but expanding into other domains, the agency recognizes the limits of a pre-market framework designed for static hardware, not continuously evolving software. Leaders described a shift toward lighter pre-market review paired with stronger post-market surveillance, focusing on real-world performance, model drift and patient outcomes. This approach requires new regulatory frameworks and enhanced data-sharing between developers, providers and regulators.
ARPA-H complements this work by funding high-risk, high-reward innovations not supported through traditional mechanisms. Notably, no generative AI (GenAI) technology capable of providing clinical care has received FDA authorization, a gap the agency aims to close. One flagship initiative supports AI systems capable of performing comprehensive physician functions, developed alongside the FDA to establish new regulatory pathways. Additionally, ARPA-H is investing in 鈥渟upervising agents,鈥 systems that monitor and control deployed AI, addressing the scalability limits of human oversight.
The VIP Sets a New National Standard for Health Data Exchange听
The Department of Veterans Affairs (VA) positioned itself as a national convener for interoperability through the , which unites leading health systems to improve care coordination for veterans regardless of where they receive care.
Grounded in the , the initiative mandates rapid adoption of national interoperability standards across care coordination, benefits, identity matching, quality measurement and public health. VA leaders outlined a layered interoperability model鈥攆rom foundational standards such as , , to data quality frameworks like and ultimately to advanced analytics and decision support. The key message: interoperability is foundational, but value is created through what is built on top of it.
Operationally, the VIP is already enabling real-world capabilities. The Veteran Confirmation Application Programming Interface (API) allows Electronic Health Records (EHRs) to verify veteran status in real time, supporting eligibility recommendations under the and the . Two workgroups are developing recommendations for identity verification and care coordination workflows, targeting submission by the end of March. A structured cadence of monthly plenaries and bi-weekly workgroups ensures continuous alignment between policy, standards and implementation.
Seamless Collaboration Requires Breaking Down Technical and Cultural Barriers听
Federal, State and Local leaders underscored that populations served by multiple programs cannot be effectively supported by siloed agencies. Both technical and cultural barriers must be addressed simultaneously.
At the Federal level, CMS, VA and the Indian Health Service (IHA) are advancing shared infrastructure and lowering redundancy. CMS is transitioning from Government-developed systems to commercial platforms, accelerating innovation and enabling AI tools that now reach approximately 80% of its workforce, saving an estimated 5.5 hours per employee weekly. The agency is also adopting a multicloud strategy for resilience and fostering talent pipelines through partnerships with institutions like the University of Maryland.
IHS is undergoing a similar transition to commercial platforms, improving AI integration and expanding access to advanced tools in rural and tribal communities. Enterprise services help ensure equitable access where local technical resources are limited. The VA is modernizing security processes to reduce delays in technology adoption and leveraging physical locations to support identity verification, improving access for veterans struggling with digital enrollment.
Bridging the digital divide also requires workforce and literacy solutions. Baltimore City panelists highlighted the need to translate Federal data into local action, particularly around social determinants of health, including housing and economic mobility. Community health workers were cited as essential connectors and should be integrated into digital strategies from the outset.
Public Health Data Infrastructure Must Shift from Detection to Prediction听
The Center for Disease Control (CDC) acknowledged that current public health infrastructure is designed for detection, not prediction. While improvements have been made since COVID-19, a broader transformation is still underway.
The serves as a central hub, enabling flexible data exchange, reusable capabilities and advanced analytics. Its purpose is to shift focus from manual data processing to proactive analysis and decision making. Leaders envision disease forecasting becoming as routine as weather forecasting, with real-time modeling to guide early intervention.
State-level examples illustrate this shift. Illinois is consolidating siloed systems into a unified cloud platform, while addressing cultural resistance to data sharing. Louisiana is focusing on targeted, use-case-driven improvements tied to Medicaid and public health outcomes. Mississippi is prioritizing foundational infrastructure and workforce readiness before scaling analytics. Across all three states, the consensus is clear that interoperability only delivers value when tied to actionable outcomes.
The VA鈥檚 NextGen CCN Redesigns Care Delivery at National Scale听
Community care is one of the fastest-growing components of the VA healthcare system. Of the 17 million veterans served, roughly 6.3 million use VA healthcare annually, with 2-3 million accessing community providers. Programs introduced through the expanded access but created operational and financial complexity.
The addresses these challenges through a comprehensive redesign of how the VA manages external care. Expected to launch in early 2027, the program introduces a more competitive ecosystem involving insurers, providers and technology partners.
Key capabilities include improved care coordination, real-time data exchange, standardized quality benchmarks and outcomes-based reimbursement. Interoperability is foundational to these goals, enabling performance measurement and accountability. The program also prioritizes transparency and trust across stakeholders, ensuring a shared understanding of care delivery. Together, these efforts are designed to position the VA to deliver high-quality, fiscally responsible care while continuing to expand access for a veteran population whose demographics and care needs are rapidly evolving.
Charting the Course for Federal Health IT Modernization听
HIMSS 2026 reinforced that progress in Federal healthcare requires aligned investment across AI governance, interoperability, cross-agency collaboration, data infrastructure and care delivery redesign. Government health agencies are not simply adding new technologies onto existing systems; they are rethinking how they organize, share data and operate as an integrated ecosystem. Sustained success will depend on aligned standards, cultural transformation and technologies that translate strategy into measurable outcomes.
As 探花视频, The Trusted Government IT Solutions Provider鈩, continues supporting Federal health IT modernization, these insights inform how industry can partner with Government to deliver a more connected, data-driven and patient-centered healthcare system.
Explore 探花视频鈥檚 Healthcare Technology portfolio of leading solutions that support Federal healthcare modernization priorities including AI, interoperability, cloud infrastructure and advanced analytics.
Contact the Health IT Team at Healthcare@探花视频.com or (571) 591-6080 to learn more.
While bad actors have utilized the capabilities of AI, the healthcare industry can also use it to improve cybersecurity. AI does not need breaks, and therefore can run all day reducing the time needed to identify a security breach by analyzing large amounts of data in real time. On a similar note, AI can identify multiple devices and manage network endpoint detection for large networks. AI has been used to predict Domain Name System (DNS) attacks before occurrence, preventing and mitigating these attacks. It can implement Secure Access Service Edge (SASE), analyze identities and manage risk. With its strength of detecting patterns, AI can distinguish subtle patterns of attack that would otherwise go unnoticed by people.
AI can significantly reduce the administrative burden for medical providers by automating routine tasks and increasing bandwidth for front line staff to complete other medical duties. A hallmark capability for AI is analyzing data which it can aggregate from wide pools of information to suggest electronic health record (EHR)-based interventions, predict possible future patient ailments and offer a more unified, comprehensive picture. In a post-COVID-19 world, AI healthcare data applications offer the extremely relevant and desired ability of anticipating future public health crises through research and analytics. These AI forecasts can accelerate understanding for policy creation, reinforce healthcare resources and provide precision public health.