Introduction: A New Era in Care Delivery
Artificial intelligence, digital health tools, and automation are no longer future concepts in healthcare — they are active forces reshaping how care is delivered today. Together, these technologies drive predictive insights, power virtual care, and improve operational efficiency across health systems of all sizes.
Moreover, leading health organizations are not simply adding new technologies. They are also consolidating redundant systems, eliminating inefficiencies, and building more connected, patient-centered care ecosystems. The goal is clear: improve patient outcomes while reducing friction across the care continuum.
These insights emerged from a session at Becker’s 16th Annual Meeting, sponsored by CSL. The panel included senior health leaders representing diverse health systems across the United States. Their discussion covered three major themes: ambient AI adoption, agentic AI potential, and the shift from task digitization to full experience design.
Ambient AI Reduces Clinician Cognitive Load
AI Scribes in Clinical Documentation
One of the most visible AI shifts in healthcare today is the rise of ambient AI scribes. Clinicians widely use these tools to automate clinical notetaking, freeing physicians from the burden of typing while they engage directly with patients. This reduces cognitive load and restores focus to the patient encounter itself.
Furthermore, the next frontier for ambient AI goes beyond simple notetaking. Health leaders now expect AI to pull data from lengthy patient medical records and produce accurate, concise summaries. As Dr. K. Nadeem Ahmed, Chief Medical Information Officer at Valley Health System, explained: those are records a physician no longer has to read manually. This capability could save significant time at the point of care.
Governing AI Trustworthiness
Yet adoption alone is not enough. Trustworthiness is equally important. Valley Health System addresses this by maintaining an internal AI task force that vets new technologies before deployment. The task force also informs end users about the data used to train each tool. Additionally, it evaluates potential risks including model drift, bias, and hallucinations.
Dr. Ahmed was direct about the policy boundaries: AI cannot make clinical or operational decisions at Valley Health. Accountability rests with the human clinician — not the algorithm. This stance reflects broader caution across the industry. As Matt Hare, Vice President of Network Growth at CoverMyMeds, noted, organizations are being thoughtful about AI risks while also feeling pressure to keep pace with the market.
Agentic AI Transforms Hospital Operations
From Contact Centers to Revenue Cycles
Beyond ambient tools, agentic AI carries even broader transformative potential. Unlike passive AI that responds to prompts, agentic AI can initiate actions, make decisions within defined parameters, and complete multi-step workflows autonomously. In healthcare, this means significant efficiency gains across contact centers, hospital pharmacies, patient journey orchestration, and revenue cycle management.
Consequently, health systems that deploy agentic AI can expect faster workflows, better alignment across departments, and improved continuity of care. The speed and coordination gains are particularly valuable for high-volume institutions managing complex patient populations.
Boston Medical Center’s AI Journey
Boston Medical Center Health System offers a compelling case study. As a large safety-net hospital in New England, it serves a diverse and often medically complex population. The system is actively exploring how to embed agentic AI into existing infrastructure — including its electronic health record platform — to improve scheduling, patient engagement, and post-encounter follow-up.
Shahidul Mannan, System Chief Data Officer at Boston Medical Center, described the ambition: the organization is looking at the full continuum of care and how AI and data-driven capabilities can support real-time decision-making throughout that journey. This holistic view signals a maturation in how health systems think about AI — not as isolated tools but as integrated intelligence layers.
Designing Experiences, Not Just Digitizing Tasks
The Risk of Tool Proliferation
Perhaps the most forward-looking insight from the session came from Hillery Shay, Chief Marketing and Experience Officer at Children’s Minnesota. She challenged health leaders to think bigger. Healthcare needs more systems thinking — not just technology adoption. The difference lies between creating truly designed patient experiences and simply executing digitized versions of old tasks.
Shay emphasized that today’s patients and caregivers need systems with less friction and more predictive intelligence. Too often, organizations deploy AI without considering how new tools interact with existing ones. This creates layered complexity rather than streamlined care.
Matt Hare reinforced this point directly. One of the real risks, he said, is continuing to layer on additional tools without governance. Without clear systems thinking in place, that approach spirals out of control. Health leaders must therefore build frameworks that evaluate AI tools not in isolation but in the context of their entire technology ecosystem.
What Health Leaders Must Do Next
Three clear priorities emerge from this discussion. First, health systems should establish internal AI governance structures — task forces, review boards, or ethics committees — to ensure responsible adoption. Second, organizations must shift their mindset from tool acquisition to experience architecture, asking not just what each tool does but how it connects to others. Third, leaders should accelerate exploration of agentic AI across high-volume operational areas such as scheduling, pharmacy, and revenue cycle management.
Additionally, workforce readiness matters. Clinicians and staff need clear communication about how AI tools work, what data trains them, and where human judgment remains essential. Transparency builds trust — both internally and with patients.
Conclusion
Digital health and AI are redefining care delivery at every level — clinical, operational, and strategic. Health organizations that approach AI thoughtfully, govern it rigorously, and design with systems thinking rather than tool stacking will lead this transformation. The shift is already underway. The leaders who act with intention today will build the connected, patient-centered healthcare ecosystem of tomorrow.
