Agentic AI is changing how decisions happen in healthcare—bringing real-time reasoning to clinical and operational workflows. Learn how leading organizations are moving from experimentation to execution, and where AI agents make the most impact.
The arrival of AI agents on the healthcare scene was a top talking point at the 2025 HIMSS Global Conference & Exhibition, a gathering of premier industry leaders and practitioners from around the world. Executives from healthcare giants as well as global enterprise AI providers all spoke about the growing role of agentic reasoning in healthcare settings—operational, clinical, and research—and what it means for the future.
The heavy spotlight on AI agents at one of the industry’s most prominent events signals a promising new opportunity: Agentic AI could be the solution to the intense resource strain many health systems face as they work to meet surging demand for access and care. At the same time, it opens the door to higher levels of innovation and more precise, informed, insightful decision-making.
Hospitals, research institutions, and life sciences organizations are all beginning to apply intelligent agentic systems to workflows where timely, context-aware reasoning can have a measurable impact. And while it’s still in its early stages, investment in agentic AI across healthcare markets is expected to multiply many times over the next five years.
Its impact is surfacing in both clinical and operational domains. For healthcare leaders, the time is now to get on board with understanding how agentic AI works, where it adds value in different scenarios, and how it can be safely integrated into real workflows.
Investment in agentic AI across healthcare markets is expected to multiply many times over the next five years.
Agentic reasoning enables artificial intelligence systems to do something healthcare has always needed: respond intelligently to change. In a field where information shifts quickly and every decision can affect outcomes, the ability to analyze context and take action—without waiting for step-by-step instructions from a human user—is quite a meaningful advance.
In healthcare, even more so than other industries, agentic AI has an important dual impact: it helps to both optimize healthcare operations and efficiencies while powering scalable high-quality care provision. In clinical settings, agents help maintain continuity by adapting to real-time patient conditions, minimizing gaps in care, and aligning actions to treatment goals. On the operational side, they support dynamic resource allocation, reduce bottlenecks, and bring coordination into daily decision-making.
Healthcare doesn’t operate in silos, and AI systems can’t either. The most useful agentic models are designed with that in mind, continuously interpreting inputs from people, systems, and situations, and helping teams act with clarity even when time and capacity are limited. Powered by cutting-edge technologies like machine learning and natural language processing (NLP), AI agents are capable of having an immense impact.
Core capabilities of healthcare agents include:
Goal orientation: Agents pursue specific objectives such as reducing patient wait times, supporting treatment adherence, or improving care coordination.
Contextual awareness: Inputs are interpreted in relation to real-world factors like acuity, comorbidities, staffing, and workflow constraints.
Autonomous decision-making: Within clear boundaries, agents take action without needing step-by-step instructions, offloading routine choices from staff.
Adaptability: Agents adjust as new inputs arrive, helping decisions stay relevant as conditions change.
Transparency and accountability: Systems provide clear rationale and escalation paths, so clinicians can interpret, trust, and intervene when needed.
Agentic reasoning streamlines the complexity that healthcare teams manage daily. It prioritizes actions, flags exceptions, and moves routine decisions forward so clinicians can focus on what truly requires human judgment. In care settings where seconds matter, the ability to act with this type of context and clarity is not a convenience—it’s a necessity.
Companies like Workday, Zoom, Epic, and IQVIA are pioneering agentic AI solutions for healthcare across operational, clinical, and research settings. The future of healthcare is going to be defined by how healthcare professionals integrate these AI-driven tools.
Healthcare presents an ideal test bed for agentic reasoning systems. It’s an environment marked by constant demand for quick decisions, limited time, and a heavy need for precision. Organizations are introducing agentic AI not as a standalone innovation, but as part of the clinical and operational fabric of healthcare, and it’s starting to show tangible results in areas where traditional systems struggle to keep pace.
Healthcare delivery today is shaped by rising expectations and mounting complexity. Patients want faster access, real-time updates, and more control over their care journeys. Stakeholders—from health plan executives to regulators—expect data-backed decisions and continuous improvement. And health systems are managing a surge of real-time inputs from electronic health records (EHRs), wearables, patient portals, and operational platforms.
This speed and scale are pushing traditional systems to their limits. That’s where AI agents are starting to make an impact—by supporting decisions that need to happen faster, more consistently, and with less manual effort. Whether streamlining operations, incorporating predictive analytics, or assisting with routine tasks like patient monitoring or appointment scheduling, these are just some of the ways agentic AI integrates with (and improves) existing workflows.
In clinical settings, agentic AI is helping teams make quicker, more context-aware decisions by continuously interpreting structured and unstructured data. For example, Google Cloud recently introduced agentic tools that act as AI doctors’ assistants, designed to support care teams with documentation and next-step planning during the visit itself—freeing up clinicians to focus on the patient, not the screen.
Agentic AI is starting to make an impact by supporting decisions that need to happen faster, more consistently, and with less manual effort.
Epic, one of the largest EHR vendors, has also begun integrating agentic logic into its systems. Its agents are being developed to help clinicians prepare for patient visits by synthesizing relevant history and surfacing key data points ahead of time. The goal is not to replace clinical judgment but to sharpen it—especially when physicians are managing high caseloads and operating with limited time.
Teams are piloting other agentic models in areas like care plan adaptation, where real-time patient data can prompt recommendations for treatment changes, medical imaging, where agents can analyze scans such as X-rays and MRIs to aid diagnostic confidence, and medication safety workflows that reconcile prescriptions during transitions of care. These agents operate continuously and can surface risks that would otherwise require extensive manual review.
On the operations side, the complexity is just as high—and the margin for error just as narrow. Staffing, scheduling, compliance, throughput—each decision has cascading effects. Agentic AI can stabilize this complexity, enabling systems to respond in real time to shifts in workforce demand, resource constraints, and compliance triggers.
Zoom, for instance, is building agentic AI into frontline communication tools, allowing care team members to escalate issues, coordinate handoffs, and surface information using voice-based mobile agents. These agents act as context-aware assistants, helping staff adjust quickly as priorities shift.
Workday is also advancing agentic AI capabilities tailored to healthcare operations through its Agent System of Record. In settings where staffing, scheduling, and financial planning are tightly connected, the Agent System of Record enables agents to act on real-time data from across HR and finance systems, supporting key decisions like adjusting shift coverage based on patient volume, labor costs, or credentialing requirements.
Credentialing is another space where agents are showing value. Instead of relying on spreadsheet tracking and periodic audits, health systems are deploying agents to monitor license renewals, training completions, and policy compliance in real time—reducing risk and administrative burden.
Technology is also reimagining audit readiness and quality reporting. Agents can categorize and tag documentation as it's generated, reducing the manual prep that traditionally consumes weeks of staff time.
In the research and life sciences space, agentic AI is being used to improve synthesis, accelerate experimentation, and drive faster insights from growing data pipelines. IQVIA, for example, is developing agent-based systems that automate tasks like literature review, trial protocol refinement, and results validation.
These agents draw from regulatory standards, historical study data, and real-time lab inputs to suggest next steps or flag issues. Because they operate continuously, they can keep pace with evolving conditions—helping research teams adapt without restarting workflows.
In labs, scientists are using agentic systems to sequence tasks and manage resource bottlenecks. As experiments generate data in real time, agents help orchestrate lab operations so scientists can stay focused on discovery rather than administration.
From early-stage discovery to protocol monitoring, agentic models are reducing time-to-insight and enabling more agile research operations. As AI infrastructure matures, more organizations are beginning to see agentic AI as foundational—not just experimental.
As agentic systems begin executing decisions within clinical and operational workflows, trust is no longer theoretical—it’s a daily requirement. The shift from static automation to autonomous reasoning raises real stakes: not just whether a system functions, but whether its actions can be understood, validated, and governed in real time.
In healthcare, where the margin for error is narrow and demand for speed is growing, agentic AI governance is essential.
Healthcare environments are uniquely sensitive to this challenge. Agents making recommendations about staffing, scheduling appointments, treatment adjustments, or research actions must operate within clearly defined scopes—and with full transparency into how and why decisions are made. This is particularly important when agent outputs intersect with regulated activities, clinical standards, or financial accountability.
To support safe deployment, governance must evolve in parallel with capability. That means:
Traceability baked into workflows, so every agent decision can be linked to source data, logic, and context.
Escalation protocols that trigger human review when ambiguity, risk, or outlier scenarios arise—particularly in clinical care, credentialing, or protocol deviation.
Operational observability that enables healthcare administrators to continuously monitor agent activity, flag drift, and maintain audit readiness.
Multi-stakeholder oversight, where clinical, operational, compliance, and technology teams share responsibility for evaluating agent performance and updating deployment boundaries.
Trust in agentic AI is about creating the conditions for reliable autonomy to exist. That requires tools and processes designed not only for what agents can do, but for how they’re supervised, understood, and improved over time. In healthcare systems, where the margin for error is narrow and the demand for speed is growing, that kind of governance is essential infrastructure.
As healthcare organizations scale agentic AI, systems must be built to act with clarity, but also with empathy: supporting decisions in a way that reflects clinical judgment, respects patients, and strengthens trust in the process of care.
Agentic AI is becoming embedded into the operational and clinical systems that healthcare teams use every day. Across key healthcare AI applications like scheduling, documentation, credentialing, treatment planning, and research workflows, early deployments are showing where agentic AI can reduce friction and support faster and more informed decisions that drive outcomes.
But deployment is only part of the challenge. Real impact depends on how well AI-powered systems are governed, how clearly their purpose is defined, and how effectively they’re integrated into the environments they’re meant to support. That includes aligning with the realities of care delivery, data infrastructure, and oversight requirements.
For organizations exploring agentic AI in healthcare, the focus now should be on execution: identifying viable use cases, establishing strong guardrails, and investing in the data and systems that make reasoning at scale possible. The opportunity is real—but so is the responsibility to get it right.
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