4 Agentic AI Use Cases Driving Healthcare Innovation

Agentic AI marks the beginning of a new era in healthcare. It's not just about AI analyzing data. It's about systems that understand clinical context, anticipate needs, and take meaningful action without waiting for commands.

We're entering what Daniel Kraft, founder and chair of NextMed Health, calls a new phase of digital care: generative health. In this model, agentic AI tools don't just support decisions; they act upon them. Drawing from lab results, wearables, imaging, genomics, and even microbiome profiles, these intelligent agents collaborate with care teams in real-time. They speak our language, reflect our cultural norms, and tailor their approach to each patient's unique health journey.

Picture this: Instead of clinicians juggling endless dashboards or responding to non-stop alerts, they're supported by an always-on AI teammate. One that reads the situation, interprets real-time data, and takes the right decisions/actions that it is programmed for.

For healthcare leaders, it's already in motion. Agentic AI systems are being piloted at places like UCSF and the Mayo Clinic. These tools integrate seamlessly into existing workflows, lighten the cognitive load, and improve care reliability without the need for a full tech overhaul.

AI-supported mammography screening enabled radiologists to detect 20% more breast cancers than traditional double-reading methods while cutting radiologist workload by 44%.

- World Economic Forum

That's the kind of result that captures attention. Not because it's futuristic, but because it's functioning.

And at a time when health systems face shrinking margins, stricter compliance demands, and soaring staff burnout, agentic AI delivers something more powerful than automation: resilience.

Here's what's at stake:

• Fewer preventable readmissions
• Lower administrative waste
• Higher quality scores
• Safer, more personalized care at scale

Still, like with any technological leap, the healthcare industry is approaching agentic AI with warranted caution. Questions around accountability, reliability, and integration into clinical workflows are real and shared across institutions.

That’s exactly why this article doesn’t speculate. It showcases.

We’ve identified four real-world use cases where leading institutions have already begun to implement agentic AI as a present-day solution. These are the early adopters who’ve moved beyond pilot hype and into measurable outcomes. The ones showing us what’s possible when healthcare systems dare to let intelligent agents share the clinical load.

Because the future of care won't be AI-assisted, it will be agent-led.

“AI agents can take many forms, but one thing is clear: they're going to be a key part of future healthcare. And because they're digital, they can be democratized. Anyone with a smartphone could soon have a personal health agent capable of connecting the dots, crowdsourcing insights, and delivering deeply personalized care. ”

Daniel Kraft

Founder and chair of NextMed Health

4 High-Impact Use Cases for Agentic AI in Healthcare

In the coming years, we won't just see AI supporting healthcare; we'll see it participating in it.

“The health-care industry is conservative. But in the next two to five years, we expect AI to enable a gradual increase in efficiency and quality of service, while making life easier for doctors, nurses, and administrative staff. ”

Alexander Podgornyy

Managing Director, IT Medical LLC

As Alexander Podgornyy, Managing Director at IT Medical LLC, observes:

That gradual shift is already underway. Across a range of clinical and operational domains, we're seeing the rise of agents that are context-aware, adaptive, and capable of independent action.

We'll now explore four such use cases, each grounded in real clinical workflows, each offering a distinct advantage in care delivery, coordination, or oversight.

1. AI Agents for Post-Surgical Recovery Pathways

Post-surgical recovery is often the most vulnerable phase of a patient's care journey. From pain management and infection risks to mobility and medication compliance, the margin for error is narrow, and the pressure on care teams is high. That's where agentic AI is beginning to redefine the rules.

At the University of Florida Health Center, a lung transplant patient is at the center of a quiet technological revolution. Inside his smart ICU room, sensors and cameras continuously capture vital signs, physical activity, and even facial expressions. Over 350 GB of data per patient is collected and analyzed in real-time by an AI system trained to detect subtle behavioral shifts before symptoms escalate.

"We're not just asking if the patient is in bed," a clinician explains. "We're asking: are they grimacing more than usual? Is the room too bright? Are they moving enough?" The AI doesn't just flag data, it interprets it. It anticipates issues like discomfort, disorientation, or early signs of infection and suggests clinical action before the patient even reports a problem.

This is agentic AI in action: not waiting for input but continuously observing, assessing, and proactively supporting recovery.

And it's not isolated to a single institution.

New trends in postoperative care:

Emerging trends in postoperative care are aligning around the same principle of context-aware intelligence that lightens the clinical load and accelerates recovery. Telemedicine, smart bandages, and wearable sensors are gaining traction across health systems globally. But what makes them truly transformative is when agentic AI ties it all together.

• Smart Bandages with built-in sensors can detect changes in wound temperature or moisture, signaling early-stage infections.
• Wearables track heart rate, oxygen saturation, and movement, feeding data into remote monitoring platforms that trigger alerts when recovery milestones are missed.
• AI-Enhanced ERAS Protocols (Enhanced Recovery After Surgery) allow intelligent agents to remind patients to mobilize, guide nutrition choices, and provide just-in-time education, all while flagging deviations to care teams.

When combined with predictive analytics, these systems go beyond monitoring. They anticipate deterioration, suggest interventions, and, crucially, keep patients engaged in their own healing process.

"Will the AI know I'm having a problem before I do?" the Florida patient asked.

"Yes, 100%," replied his clinician.

That is the promise of agentic AI: a recovery environment where patients are not just watched but actively supported by intelligent systems that act as a second set of clinical eyes and sometimes the first to detect risk.

The outcome? Faster recovery. Lower readmission risk. And more time for clinicians to focus on care, not paperwork.

Agentic AI doesn't replace the human touch in recovery. It protects it by removing the overload, anticipating what matters, and stepping in before it's too late.

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2. AI for Clinician Burnout Mitigation

Burnout isn’t just a workforce issue; it’s a patient safety crisis. And for most clinicians, it’s not the complexity of care that wears them down. It’s the crushing load of low-value tasks that pulls them away from the very reasons they entered medicine.

In 2024, U.S. physicians reported spending nearly 28 hours a week on administrative work, including 8.8 hours on documentation and more than 10 hours on communication workflows. That’s over half the workweek devoted to clerical tasks instead of clinical care.

This is where agentic AI begins to shift the equation, not by replacing clinicians, but by restoring them to meaningful work.

At UC San Diego Health, AI-generated message drafts are now embedded into the electronic health record, helping physicians respond to patient messages more effectively. A study by the UC San Diego School of Medicine found that while these drafts didn’t reduce response time, they did ease cognitive burden by starting empathetic replies that physicians could then personalize. In a separate evaluation of 200 patient queries, expert reviewers preferred the AI-generated responses in 78.6% of cases, citing greater clarity, empathy, and completeness.

This isn’t about automation for its own sake. It’s about intelligent augmentation, where agentic systems handle the repetitive, nonclinical work so clinicians can refocus on what only humans can do.

A 2024 AMA survey underscores this shift:

• 66% of physicians now report using AI in their daily practice (up from 38% in 2023)
• 57% identified administrative automation as AI’s top value
• 54% said burnout reduction was their primary reason for adoption

“Everyone is talking about AI as a technology. To me, AI isn’t about technology; it’s about empowerment. It’s about giving doctors and nurses back the time and focus for the very reasons they became clinicians in the first place. ”

Tom Lawry

Managing Director, Second Century Tech LLC

This is the deeper promise of agentic AI. As Eric Topol calls it, keyboard liberation, freeing clinicians from inboxes, form-filling, and documentation so they can return to working at the top of their license.

Imagine reclaiming a third of your week, not by pushing harder, but by letting intelligent systems take on what doesn’t require your expertise. That’s not just a workflow improvement. It’s a renewal of purpose.

Of course, implementation isn’t without challenges. Workflow integration, trust-building, and ethical oversight remain critical. But as systems like UC San Diego have shown, these tools are already working, safely, effectively, and with measurable impact.

Agentic AI doesn’t just reduce burnout. It helps clinicians reconnect with what makes healthcare worth doing. And in doing so, it protects both providers' well-being and patient safety at scale.

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3. Autonomous Bedside Discharge Agents

Discharge planning remains one of the most delay-prone and resource-intensive stages in the care journey. From arranging transport and medications to coordinating follow-up appointments and documentation, the process is highly manual and often fragmented. These delays do not just affect patient experience; they disrupt hospital flow, prolong length of stay, and elevate readmission risks.

Agentic AI is beginning to address this complexity with systems that proactively assist rather than passively monitor.

At Lyell McEwin Hospital in Australia, a novel AI-driven tool known as the Adelaide Score was trialed in 2024 to enhance discharge readiness. Developed by researchers at the University of Adelaide, the system continuously analyzes vital signs and lab results from patient records to identify those likely nearing clinical discharge.

What sets it apart is its agentic behavior. Each day, it autonomously ranks patients by probability of discharge and shares these insights with the Supportive Weekend Interprofessional Flow Team (SWIFT), a nurse-pharmacist duo focused on optimizing weekend discharges.

Rather than replacing decision-making, the AI streamlines it by surfacing actionable insights that help clinical teams prioritize patients who can safely transition out of the hospital within 24 hours.

Early outcomes were encouraging. Compared to the same period in the previous year:

• 7-day readmission rates dropped from 7.1% to 5%
• Median length of stay fell from 3.1 to 2.9 days
• Cost savings were estimated at over $735,000 in just 28 days

As Associate Professor Stephen Bacchi noted, “While health is a 24/7 issue, hospital staffing is inherently influenced by factors like the time of day and day of week. AI tools like the Adelaide Score make the discharge process more efficient, especially when staff are limited.”

The system also helped close a critical operational gap: weekend discharge slowdowns, which are often limited by reduced staffing and information delays. By proactively identifying longer-stay or complex patients ready for discharge, the AI helped SWIFT teams accelerate coordination and maintain throughput even on weekends.

Looking ahead, the potential for agentic AI in discharge planning is substantial. As these tools evolve, they could support a broader set of care transitions by automatically generating discharge summaries, scheduling follow-ups, and initiating communication with primary care providers or post-acute facilities.

By transforming a traditionally reactive process into a predictive and workflow-integrated function, agentic AI allows clinicians to focus on what matters most: safe, timely transitions and, ultimately, better patient outcomes.

4. Multi-Agent System (Data-sharing across care teams)

Despite major strides in EHR adoption and HIE infrastructure, healthcare interoperability remains uneven and often reactive. Clinical data may exist, but it's frequently:

• Unavailable at the moment of care
• Misaligned with workflow contexts
• Trapped in incompatible formats

This creates delays, blind spots, and inefficiencies that directly affect patient outcomes and clinician decision-making.

Agentic AI offers a fundamentally different approach.

Rather than just connecting systems, intelligent agents actively monitor clinical states, reconcile conflicting data, and route critical updates across care settings before a query is made.

Why the Current System Falls Short

A 2024 ONC report noted that:

• The share of U.S. hospitals engaging in all four key domains of interoperable exchange (send, receive, find, integrate) rose from 28% in 2018 to 43% in 2023
• Yet most hospitals still operate below "routine" exchange levels, particularly when it comes to integrating data from long-term, behavioral health, or post-acute providers
• Larger urban systems lead adoption, while rural and independent hospitals lag behind, widening disparities in care continuity

This leaves many clinical teams navigating incomplete, delayed, or siloed information, especially during transitions of care or emergencies.

Real-World Model: Portugal's BMaPI System

In Portugal, the BMaPI multi-agent platform offers a compelling model. Deployed across Hospital da Luz and other networks, it showcases how autonomous interoperability agents can deliver real-time coordination across hospital systems.

These Agents:

• Track patient events autonomously
• Resolve terminology and data conflicts across disparate EHRs
• Continuously monitor state changes and redirect data queries to the freshest source

Supported by the AIDA agent, the platform doesn't wait to be asked; it senses, interprets, and initiates action.

The Result:

• Faster, cleaner data synchronization across hospital networks
• Real-time delivery of critical updates, discharge summaries, allergy alerts, and imaging reports across departments and institutions
• Dramatically reduced need for manual data reconciliation

What This Means for Medical Leaders

As FHIR APIs and TEFCA lay a solid foundation for national health data exchange, agentic interoperability systems make it clinically meaningful by:

• Improving Decisions: Surfacing the right data at the point of care
• Reducing Errors: Automatically reconciling outdated or conflicting records
• Increasing Efficiency: Minimizing time spent navigating multiple systems
• Resilient Coordination: Especially valuable during handovers, weekend care, or cross-institution transitions

Agentic AI doesn't just enable interoperability; it operationalizes it.

By embedding intelligent agents into the clinical fabric, health systems move from data access to data action, giving clinicians a full, trusted, and timely picture of every patient they care for.

Why These Use Cases Deserve Attention in 2025

2025 marks a quiet but pivotal shift in healthcare AI, not because agents are everywhere, but because the conditions for them to succeed are finally in place. While the first wave of AI brought prediction and documentation support, the second wave introduced something more powerful: intelligent agents that act with autonomy, adapt in real-time, and remain accountable. The four use cases explored in this report signal the arrival of this shift.

From AI Assistance to Agentic Autonomy

Most AI tools today support agentic AI systems to decide and act. They analyze patient context, simulate scenarios, and initiate action within defined conditions. Whether it’s a consent agent tailoring explanations to a patient’s comprehension or a discharge agent adapting timelines based on care team availability, the key difference is agency.

This shift matters now because healthcare’s challenges, including burnout, fragmentation, and care complexity, can’t be solved with more dashboards. They require systems that step in and carry clinical load intelligently, not just inform it.

Hospitals Need Plug-and-Play, Not Rip-and-Replace

Nearly 8 0% of hospitals still face integration challenges with new technologies. That’s why agentic AI is built for co-existence. These agents don’t demand infrastructure overhauls.

• They operate within existing EHRs, scheduling tools, and care platforms.
• A bedside discharge agent doesn’t replace the discharge system; it makes it responsive.
• A cross-system interoperability agent doesn’t rebuild your data layer. It reconciles it securely and in real-time.

Solving Gaps That Traditional AI Leaves Open

Agentic AI doesn’t compete with current AI; it complements it.

Take post-surgical recovery as an example. Here, remote monitoring collects data, but who interprets it in real-time?

The agents in these use cases fill that operational vacuum. They sense what matters, act where appropriate, and escalate only when necessary with full traceability and clinician oversight built in.

Trust Requires Oversight, Not Just Insight

The AMA’s 2025 data is clear: while 68% of physicians believe AI can improve care, 47% rank increased oversight as one of the top requirements for adoption. Agentic systems address this head-on:

• They operate within a scoped authority, not unchecked automation.
• They keep a human in the loop by design.
• They maintain logs, explain decisions, and adapt based on clinician feedback.

This makes them not just operationally useful but ethically deployable.

It’s Not Just About Efficiency, It’s About Safety

The cost of inaction in healthcare isn’t theoretical; it’s measurable and growing:

• Preventable readmissions led to Medicare penalties for 42% of hospitals in FY2023, amounting to over half a billion dollars in collective payment reductions.
• Wasteful administrative spending accounts for 7.5% to 15% of total healthcare expenditure, an estimated $285 to $570 billion every year.
• The healthcare industry suffers the highest breach costs of any sector, with an average of $10.93 million per incident.

Agentic AI reduces exposure on all fronts, not by automating blindly but by acting meaningfully. Burnout agents catch overload before it triggers errors. Ethics agents surface bias before it escalates. Interoperability agents prevent context loss before AI decisions go wrong.

What Makes These Use Cases Different?

Each of the four scenarios in this report is designed not just for potential but for plausibility:

• They rely on real-time data and clinical context, not pre-trained outputs.
• They integrate into existing workflows without demanding new behavior.
• They offer human review, escalation, and adaptation, not black-box decisions.
• They’ve already been piloted, proposed, or published in peer-reviewed studies or major medical institutions.

These are not moonshots. They are moon landers, small, powerful systems that touch down in one place and solve one problem intelligently.

Conclusion

Agentic AI is ushering in the second wave of digital transformation in healthcare. This shift is not about smarter tools; it's about autonomous systems that understand context, take action within guardrails, and reduce burden across clinical workflows. As the industry grapples with care complexity, staffing strain, and operational risk, success will depend on how quickly healthcare teams can adapt, not just technically but behaviorally.

Edstellar is here to help healthcare organizations prepare for that shift. As a trusted corporate training provider, Edstellar offers customized, instructor-led AI programs designed for your clinical and healthcare teams. From AI literacy for medical professionals to advanced prompt design for safe agentic system usage, our programs align with real-world roles, regulatory standards, and patient care responsibilities.

What makes Edstellar uniquely positioned is our Skill Matrix platform, a proprietary tool that enables you to map staff competencies, identify skill gaps, and deliver targeted, role-based training. Whether you're a hospital deploying digital twins, a care network scaling autonomous consent agents, or an innovation leader piloting burnout mitigation systems, Edstellar provides the upskilling foundation you need.

Your AI strategy is only as strong as your people's readiness to implement it. Start with the right partner. Prepare your teams to lead the change.

The agentic future is here. Edstellar helps you make it real.