Presented by Carahsoft
Federal health IT is entering a pivotal moment as electronic health records, artificial intelligence, interoperability standards, and cybersecurity converge to reshape how care is delivered across government health systems. In this episode of Innovation in Government, host Francis Rose brings together senior leaders from across government and industry to examine how federal health IT is evolving beyond digital record-keeping into more intelligent, patient-centered, and outcome-driven systems.
Throughout the program, panelists explore how EHRs are becoming the backbone of AI-enabled care, supporting clinicians while reducing administrative burden and burnout. Government officials discuss the role of standards, certification, and transparency in building trust and ensuring secure data exchange, while industry leaders share real-world examples of innovation improving access to care, particularly in rural and underserved communities. Cybersecurity, privacy, and responsible AI governance emerge as recurring themes, underscoring the need to balance rapid innovation with patient safety and system resilience.
Together, the conversations point toward a future where technology fades into the background, empowering clinicians, patients, and communities through better data, smarter systems, and stronger collaboration across the federal health ecosystem.
Boltz emphasizes that technology must never interfere with the patient-provider relationship. He reflects on how earlier generations of EHRs often pulled clinicians away from direct patient interaction, contributing to frustration and burnout. Emerging capabilities such as ambient listening and automated clinical documentation are reversing that dynamic by capturing patient encounters in real time and translating them into structured data without manual input. This allows clinicians to focus on conversation and care while preserving accuracy and continuity.
He also stresses that AI adoption must move forward alongside strong cybersecurity, privacy, and governance practices. As healthcare organizations expand data environments to support AI, zero trust architectures, clearly defined access controls, and consistent governance are essential. Boltz underscores that AI is only as effective as the data it consumes, making data quality, interoperability, and security non-negotiable requirements.
Key Takeaways
Electronic health records are becoming the data backbone for AI-driven and personalized care.
Ambient documentation tools can reduce clinician burden and restore patient-focused interactions.
Cybersecurity, governance, and data quality are critical to responsible AI adoption.
Prieto outlines how ONC’s voluntary certification program, reinforced through CMS payment incentives, encourages providers to adopt certified, standards-based technology. Regular updates to certification criteria help ensure systems remain aligned with modern standards and emerging technologies. Transparency, especially as AI becomes more prevalent, is essential to building trust among clinicians and ensuring confidence in system outputs.
Key Takeaways
Standards-based certification is foundational to interoperability and secure data exchange.
Transparency in AI-enabled systems supports trust and responsible clinical use.
Collaborative ecosystems help translate standards into improved patient experiences.
A central theme of Dr. Ondra’s remarks is responsible AI governance. Unlike traditional medical devices, AI systems continuously evolve as they ingest new data, requiring lifecycle oversight to ensure safety, reliability, and trust. He also highlights emerging cybersecurity risks unique to AI, including data poisoning and cascading system failures that can disrupt healthcare delivery at scale.
Key Takeaways
AI enables new models for clinical research and operational efficiency.
Responsible AI requires continuous oversight and lifecycle governance.
Integrating healthcare and social data supports whole-person care and better outcomes.
Sriram emphasizes NIST’s role in building trust through testing, benchmarking, and measurement. As AI becomes more prevalent, new metrics are needed to assess reliability, uncertainty, and trustworthiness. Ongoing research into these areas will be essential to ensuring AI-enabled health systems perform safely and effectively.
Rinderer adds a vendor perspective, stressing that transparency is essential when applying AI in healthcare. Protecting intellectual property must not come at the expense of patient safety, privacy, or trust. He argues that healthy tension between regulation and innovation is necessary to
Key Takeaways
Future health IT systems will be intelligent, patient-centric, and data-driven.
Trust in AI depends on rigorous testing, benchmarking, and clear metrics.
Responsible innovation requires balancing speed, transparency, and patient safety.