Agentic AI in Healthcare: From Hype to Real-World Impact

From generative AI tools like ChatGPT to predictive algorithms powering diagnostics, AI is no stranger to healthcare. But a new wave of intelligence is arriving – Agentic AI and it’s doing more than just analyzing data or suggesting answers. It’s acting on behalf of humans, autonomously managing tasks, adapting to changing conditions, and even initiating multi-step processes with little to no human intervention.

In short, Agentic AI in healthcare is not just a concept. It’s already reshaping the way hospitals handle claims, patients book appointments, and health systems scale their operations.

This article breaks down what Agentic AI means, how it differs from other types of automation, where it’s driving tangible ROI (especially in Revenue Cycle Management), and what’s coming next as major players like UiPath, Thoughtful AI, and Hyro double down on Agentic capabilities.

Traditional automation, such as RPA, follows predefined rules. Generative AI, such as GPT-based models, generates content and provides answers in response to user prompts. But neither makes decisions nor takes action on its own.

That’s where Agentic AI comes in.

An Agentic AI system doesn’t just wait for instructions -it takes a goal, decides what steps to follow, and executes them. It can adjust its strategy, access external systems, and even enlist the help of other agents to complete the task. This kind of AI behaves more like a tireless virtual teammate than a passive tool.

In healthcare, this means agents can:

• Log in to payer portals to verify insurance
• File prior authorizations or refile denials
• Manage billing workflows end-to-end
• Schedule appointments based on patient preferences
• Monitor hospital beds and medication inventory, and take appropriate action.

It’s autonomy with accountability – most systems still operate within set guardrails, but the speed, resilience, and contextual awareness go far beyond what legacy bots or scripts can do.

Unlike traditional AI, which offers insights or predictions, Agentic AI behaves more like an independent problem solver. It can make decisions, execute multi-step tasks, and adapt based on feedback – all without needing a person to micromanage each step. In other words, an Agentic AI in healthcare is like an intelligent assistant that prioritizes actions and handles tasks (e.g., scheduling follow-ups or coordinating care) much as a diligent human staffer would, but at digital speed.

To clarify the jargon, it helps to contrast Agentic AI with other AI types:

• Predictive AI analyzes data to forecast events (“What might happen?”).
• Generative AI (think ChatGPT) creates new content or suggestions in response to prompts, but it remains reactive, waiting for human instructions.

Agentic AI, by contrast, acts proactively. It leverages techniques such as Large Language Models (LLMs), Machine Learning, and automation to carry out tasks and achieve goals with minimal supervision. In short, generative AI creates, while Agentic AI acts. This means that Agentic AI might not wait for a specific prompt every time; once given an objective, it can figure out the steps and execute them, adjusting as conditions change.

Several converging trends explain why now is the time for Agentic AI in healthcare. First, the industry is grappling with unprecedented administrative burdens and workforce challenges. Healthcare providers spend a considerable amount of time and money on paperwork, billing, and coordination tasks that don’t directly improve patient outcomes. In the U.S. alone, an estimated $60 billion was spent on administrative tasks in 2022 – a jump of $18 billion from the prior year. These tasks (like insurance eligibility checks, prior authorizations, claims processing, etc.) are necessary but labor-intensive. They also contribute to high error rates – claim denial rates can reach up to 20%, resulting in billions of dollars in lost revenue. The situation has been worsened by staff shortages and burnout, which drive up costs and slow down operations.

Agentic AI in healthcare directly addresses these pain points by automating high-volume, routine processes in a more intelligent manner than earlier technologies. 

Crucially, Agentic AI arrives at a time when AI technology itself has matured. The boom in generative AI has familiarized healthcare leaders with AI’s potential (everyone saw what GPT-style models could do with text, images, and even medical knowledge). Agentic AI builds on that foundation: it uses advanced AI models under the hood, but ties them to actionable automation. For instance, a generative AI might draft a response to a patient query; the AI agent will send that response, schedule a follow-up appointment based on the content, update the patient’s record, and alert the care team if needed – all autonomously. Early examples of Agentic AI, such as autonomous chat assistants and scheduling bots, have demonstrated productivity boosts by automating end-to-end routine tasks. With Machine Learning and NLP now more reliable, and APIs enabling integration with hospital systems, the feasibility of deploying Agentic AI at scale is significantly higher in 2025 than it was even a couple of years ago.

Finally, the economic pressure on healthcare systems can’t be ignored. Automation is viewed as a key strategy for achieving more with less. Executives (CTOs, CFOs, etc.) are eyeing Agentic AI as a way to augment the workforce – essentially a digital labor force that can work 24/7, eliminate backlog, and reduce errors. A McKinsey analysis famously estimated that about 45% of work activities could be automated with current technology. Healthcare, with its mix of data-heavy and rules-based tasks, is ripe for this. In a nutshell, Agentic AI in healthcare provides timely relief, addressing cost and efficiency challenges while leveraging recent AI advancements. It aligns with the urgent need to improve operational resilience in an industry that’s both highly complex and highly regulated.

Agentic AI is redefining how providers manage the most critical operational function: getting paid. It automates nearly every component of the revenue cycle, including:

• Eligibility verification
• Prior authorizations
• Claims submission and tracking
• Denial management
• Payment posting and reconciliation

Unlike traditional bots, Agentic AI understands workflow context, learns payer rules, and can autonomously correct and refile claims, leading to faster reimbursements, fewer denials, and leaner staffing.

Case Study – Flobotics for Claims Automation

A U.S. clinic partnered with Flobotics to automate claims processing end-to-end.

Results:

• 449% ROI within months
• Claims processed 10× faster
• The rejection rate decreased significantly.

Case Study – Thoughtful AI + Behavioral Health Works

Behavioral Health Works deployed a comprehensive RCM stack, comprising EVA (Eligibility Verification), CAM (Claims Automation), and PHIL (Payment Posting).

Results:

• 100% of eligibility tasks are fully automated
• 400% increase in payments processed
• Near-zero staff time on eligibility checks

Agentic AI handles more than just filing claims—it ensures they’re accurate, complete, and payer-optimized. These agents:

• Cross-check claim data against EHRs and payer rules
• Auto-correct errors pre-submission
• Track denials and autonomously refile with proper documentation
• Adapt over time by learning each payer’s quirks

Case Study – Schneck Medical Center + Experian Health AI Advantage™

Sneck Medical Center has adopted Experian Health’s AI-powered tools (AI Advantage™), which predict denials before claims are submitted and prioritize cases for optimal appeal.

Results:

• Significant reduction in denied claims
• Staff are able to focus only on high-probability appeals
• Better data visibility into denial root causes

Case Study – People’s Care

Automated monthly billing and claims workflows across 100+ sites.

Results:

• Manual workload reduced from days to 5 hours
• Staff refocused on escalated cases.
• 200+ vendors invoiced per month

Agentic AI significantly enhances access to care by automating the booking, confirmation, and management of appointments for patients. It can:

• Offer appointment slots in real-time
• Reschedule based on provider/patient preferences
• Answer FAQs
• Route calls or chats to the correct department
• Send reminders and reduce no-shows

Case Study – Intermountain Health + Hyro

Intermountain used Hyro’s conversational AI agents across web, mobile, and call center platforms.

Results:

Case Study – Inova Health

AI agent handled 100% of patient access calls, routing and answering common questions.

Results:

• 8.8× ROI
• Over 4,300 staff hours saved per month

Agentic AI agents excel at cross-system orchestration, especially in healthcare environments plagued by disconnected EHRs, scheduling tools, and billing platforms. Use cases include:

• Syncing data across EHR and RCM systems
• Monitoring hospital bed availability or OR utilization
• Coordinating discharge planning and housekeeping
• Auto-ordering supplies when inventory runs low

Case Study – Mandolin Specialty Pharmacy

Automated payer communication and documentation retrieval for specialty prescriptions.

Results:

• Reduced average cycle time from 30 days to just 3
• Deployed across 700+ clinics

Case Study – Omega Healthcare Management Services + UiPath

Omega Healthcare – a major RCM firm serving 350+ providers – partnered with UiPath to deploy agentic AI tools, particularly UiPath’s AI-powered Document Understanding and agentic automation framework. This combination enabled automatic extraction of key data from medical documents and insurance correspondence, elimination of mundane manual tasks, and seamless integration with billing and claims systems.

Results:

Unlike brittle bots, agents adapt. If a form changes or a data field is moved, it self-corrects or asks for human input once, then learns from the experience. This eliminates the downtime that previously hindered legacy automation.

The trajectory for Agentic AI in healthcare over the next few years looks extremely promising. As technology matures and early adopters report successes, we can expect widespread adoption. Analysts project explosive growth (45%+ CAGR) in the Agentic healthcare AI market through 2030, and it’s not hard to see why. The ROI is becoming clear – from cost savings and efficiency gains to improved patient outcomes – and that will drive more investment.

In the near future, we’ll see Agentic AI permeate almost every facet of health operations. Virtual AI assistants could become standard in call centers and patient outreach, handling appointment logistics, routine check-ins, and initial triage questions before a patient ever speaks to a human. Hospitals might deploy fleets of AI agents to continuously monitor everything from equipment maintenance needs to patient vital signs in the ICU, alerting staff the moment something requires attention. The concept of a “lights-out” hospital night shift (with AI handling routine tasks while humans sleep, calling them only for exceptions) is already being tested in forward-looking institutions.

On the clinical side, Agentic AI will increasingly serve as a collaborative partner in care delivery. We may see AI agents assigned to specific roles on care teams – for example, an “AI care coordinator” that ensures all the recommended preventive screenings for a patient are completed on time, or an “AI diagnostic assistant” that pre-reads scans and flags abnormalities for the radiologist. Multi-agent systems might be employed where different AI agents specialize (one monitors labs, another handles scheduling, another manages insurance issues) and communicate with each other to coordinate care seamlessly. This could reduce the fragmentation in healthcare processes.

Importantly, big tech companies and healthcare startups are heavily investing in Agentic AI. Microsoft, for instance, has integrated new AI capabilities into its Cloud for Healthcare platform, including a Healthcare AI Agent service that can assist care teams by automating tasks and answering queries within clinical workflows. Likewise, major EHR vendors are exploring agent-like features to automate chart reviews and patient messaging. On the startup front, companies like Hyro, DeepMind (Google Health), and Hippocratic AI are pushing boundaries. Hippocratic AI, known for its focus on “empathetic” large language models, partnered with NVIDIA in 2024 to develop AI healthcare agents that can engage in ultra-responsive, empathetic conversations with patients. This hints at a future where AI-driven telehealth or customer service is not only quick and efficient, but also emotionally intelligent – a crucial factor in patient satisfaction.

From a broader perspective, as Agentic AI systems prove their reliability, they might earn greater autonomy under regulatory frameworks. We might see, for instance, an AI agent certified to handle medication refills up to a certain risk level without pharmacist review, or an AI triage system authorized to directly schedule patients to appropriate clinics. Regulators are actively working on standards – expect frameworks from bodies like the FDA, the EU’s AI Act, and medical specialty boards on how to validate and audit AI decisions. This regulatory clarity will further encourage adoption because it will define the rules of the road.

For healthcare executives, the rise of Agentic AI represents both an opportunity and a strategic imperative. Those who leverage these technologies effectively can achieve significant competitive advantages – lower operating costs, higher patient throughput, better quality metrics, and reduced staff burnout. Imagine a hospital that can handle 30% more patient volume without adding staff, because AI agents are orchestrating much of the workflow behind the scenes. That isn’t science fiction; it’s on the horizon. Conversely, organizations that drag their feet may find themselves falling behind, much like those who were late to adopt electronic health records or telehealth.

In simple terms, Agentic AI “acts,” while Generative AI “creates.” Generative AI might write the report, whereas Agentic AI will file the report, send it to the relevant parties, and initiate the next steps based on the report’s findings. Generative AI is like a talented content producer or brainstormer at your disposal, and Agentic AI is like a smart, autonomous executive assistant that can handle tasks on your behalf.

It’s also worth noting that Agentic AI is relatively new to the scene. Many Generative AI applications are already mainstream (dozens of AI writing assistants, image generators, coding copilots, etc., in daily use), whereas Agentic AI applications are just starting to emerge beyond RPA. Tech companies are now racing to embed Agentic capabilities into their platforms. UiPath, a leading automation vendor, released an “Agentic AI” update to its automation platform in 2025, reflecting the growing importance of the fusion of AI and action in the RPA world. In short, Generative AI vs Agentic AI is not about which is better – each has distinctive capabilities. The real power lies in their combined use.

Behind every AI agent is a robust ecosystem of tools, models, and frameworks. While Agentic AI is a broad field, some platforms are tailored specifically for the unique compliance, data, and workflow needs of healthcare. Below is a curated list of key technologies that enable real-world deployments, ranging from revenue cycle automation to clinical support.

1. UiPath Agentic AI Framework

UiPath has taken the lead in enterprise automation by launching its Agentic AI architecture in 2024. Designed to integrate LLMs, APIs, and RPA, this framework enables autonomous AI agents that act on goals, not just scripts. For healthcare, UiPath enables agentic automation in:

• Claims processing
• Eligibility and prior auth
• Document understanding for EHR and billing
• HIPAA-compliant bot orchestration

It’s especially valuable for providers already using UiPath for automation, offering a smooth path into AI agents deployments.

2. Microsoft Azure Health Bot & DAX Copilot

Microsoft offers multiple healthcare-native AI tools:

• Azure Health Bot enables intelligent patient interactions and task automation (triage, symptom checking, appointment booking).
• Nuance DAX Copilot is integrated into EHRs like Epic and Cerner to autonomously document patient visits, generate billing codes, and summarize notes, functioning as an agentic scribe in the room.

Microsoft also powers ambient intelligence features within Epic, enabling AI to suggest clinical actions and identify documentation gaps in real-time.

3. Google Med-PaLM 2 & Vertex AI for Healthcare

Google’s Med-PaLM 2 is a large language model specifically trained for medical reasoning. It’s being tested in partner hospitals like the Mayo Clinic to:

• Assist in diagnosis and documentation
• Answer clinician queries safely and explainably
• Power backend agents for summarization and task routing

Coupled with Vertex AI, providers can build goal-oriented agents for patient messaging, coding support, or decision assistance, all on Google’s HIPAA-compliant cloud.

4. AWS HealthLake Agents

AWS HealthLake allows healthcare organizations to unify data across FHIR, HL7, and claims systems. Using AWS-native AI tools, you can deploy agents to:

• Automate eligibility validation
• Review care gaps
• Generate summaries and insights for clinical or billing workflows

For those already in the AWS ecosystem, this framework supports low-latency, regulation-compliant agent deployment.

5. Hippocratic AI (NVIDIA Partnered)

Hippocratic AI focuses exclusively on building safety-focused LLM agents for healthcare, with emphasis on:

• Patient communication (pre/post-op, medication guidance)
• Scripted but adaptable interactions
• Voice-based agents with empathy modeling

In 2024, they partnered with NVIDIA to run agents on secure NIM microservices, enabling HIPAA-ready, scalable, voice-enabled AI assistants in clinical and call center settings.

6. Epic Ambient Intelligence

In collaboration with Microsoft, Epic now offers built-in ambient intelligence features:

• Drafting visit notes and referrals automatically
• Suggesting billing codes
• Monitoring tasks like follow-ups or labs

It’s a native agentic layer inside one of the most widely used EHRs, helping reduce clinician burnout and documentation lag.

7. LangChain & AutoGen (Open Source)

Though not built for healthcare out of the box, frameworks like LangChain and AutoGen are being used by healthtech startups to create:

• Autonomous agents that interact with EHR portals
• Multi-agent task handlers for intake, billing, and triage
• Chat-enabled patient interfaces powered by task-chaining

For more experimental or custom use cases, these open frameworks offer maximum flexibility.

Getting started doesn’t mean ripping out your tech stack. Many deployments begin with a single pain point, such as automating eligibility checks or streamlining denials, before scaling across the organization.

Look for a partner that:

• Knows healthcare-specific workflows and systems
• Offers secure, HIPAA-compliant infrastructure
• Provides explainability and audit logs
• Helps you define guardrails for human-in-the-loop oversight

In closing, Agentic AI in healthcare is moving from hype to tangible reality. It promises a future where clinicians are supported by tireless digital assistants, where patients navigate the system more easily, and where the business of healthcare runs with fewer bottlenecks and errors.

To be sure, the journey will require careful navigation of challenges, ensuring these AI agents are safe, fair, compliant, and aligned with human values. But if we get it right, the payoff is a healthcare system that’s more efficient, more responsive, and ultimately more human-centric (because humans can focus on what matters—and let machines handle the rest). As one industry report put it, this is not just another tech upgrade but a fundamental shift in how healthcare operates—akin to having a new digital workforce alongside the human one.

In the RCM space alone, Agentic AI is unlocking huge savings, faster payments, and fewer denials. For patient access and operations, it reduces wait times, eliminates repetitive tasks, and boosts satisfaction. And for the future? It’s paving the way for a more proactive, resilient, and scalable healthcare system.

At Flobotics, we specialize in helping healthcare organizations harness the power of Agentic automation. Whether you’re looking to optimize claims processing, eliminate denial backlog, or build intelligent workflows that bridge your EHR and billing systems, we bring together RPA, AI, and domain expertise to deliver automation that works. If you’re ready to bring Agentic AI into your healthcare operations, we’re ready to help.