Agentic AI in healthcare is moving from “interesting” to practical because it is designed to do something healthcare teams need badly right now: carry work forward instead of stopping at a suggestion. This shift is also driving demand for Agentic AI development services, as organizations seek systems that can operate within real workflows rather than generate content. Instead of only drafting a note or summarizing a message, agentic systems can follow a goal, take the next actions across tools, and keep a workflow moving toward a clear outcome, while staying within defined policies and escalating exceptions for review.
The momentum behind this shift is also showing up in the market. Grand View Research estimates the global agentic AI in healthcare market at USD 538.51 million in 2024 and projects it will reach USD 4.96 billion by 2030, growing at a 45.56% CAGR. That growth reflects a real change in demand: healthcare organizations want AI that reduces operational drag, supports overburdened teams, and improves execution across high-volume workflows.
This article explains what agentic AI in healthcare really means, why healthcare leaders are prioritizing it now, which agentic AI use cases deliver measurable value, and how to implement agentic AI in healthcare responsibly so it scales across the organization without increasing risk.
What is agentic AI in healthcare?
Agentic AI in healthcare refers to AI systems that can operate with a level of autonomy to complete healthcare tasks and workflows. Instead of only generating an answer, summary, or draft, agentic AI can plan the steps required to reach an outcome and take actions across tools and systems, while following clear policies and involving people when judgment is needed.
Agentic AI systems are built with abilities that go beyond basic automation. The key characteristics include:
- Goal-driven execution: It works toward an outcome, not only a response
- Multi-step planning: It breaks work into steps and completes them in sequence
- Action across systems: It can interact with software tools and intelligent workflows (within permissions)
- Adaptation: It adjusts when conditions change instead of failing when something unexpected happens
- Human oversight: It escalates exceptions and logs activity for accountability
Agentic AI is most valuable in multi-step processes where work slows down because tasks move across different teams and systems. In these workflows, an agent can gather required information, apply rules, trigger the next step, track progress, and route exceptions to the right person for review. This is why AI in healthcare is being utilized in areas such as scheduling, clinical documentation, prior authorization follow-ups, denial prevention, and discharge coordination.
Agentic AI vs. traditional automation (RPA)
Traditional automation tools follow fixed rules and predefined paths. They work well for repetitive tasks but struggle when workflows change, data is incomplete, or decisions depend on context. Agentic AI goes further by using context and reasoning to decide what to do next, which makes it suitable for more complex workflows while still operating within defined boundaries.
If you want a deeper comparison of how agentic AI differs from traditional virtual assistants, you can read how agentic AI is different from traditional virtual assistants.
How agentic AI is being applied in healthcare
Many organizations already use AI for isolated tasks such as drafting messages, summarizing notes, or supporting coding. Agentic systems go further by executing a sequence of steps to complete work.
Agentic AI is workflow execution, not a single output
An agent can take a goal such as “reduce appointment backlog” and then carry out steps like retrieving policy rules, collecting required patient inputs, verifying eligibility, proposing time slots, sending reminders, and escalating exceptions.
Agentic AI coordinates across departments
Healthcare workflows frequently cross clinical operations, patient access teams, finance, and care management. Agentic systems can reduce handoffs by coordinating steps across multiple tools and roles.
Agentic AI can run continuously
Instead of waiting for a prompt, agents can monitor queues and thresholds and act when conditions are met. This is especially relevant in environments like scheduling, authorizations, and revenue cycle operations.
Why healthcare leaders are prioritizing agentic AI now
Healthcare leaders are prioritizing agentic AI now because the industry has reached a point where incremental digital improvements are no longer sufficient. The focus has shifted from experimenting with AI tools to building scalable operational capability that can reliably execute work across complex systems, reduce friction in daily workflows, and deliver measurable results without relying on constant manual effort. Agentic AI fits this moment because it can coordinate multi-step processes end-to-end, directly supporting capacity, financial discipline, and service-level reliability.
This priority is not driven by hype. It is driven by three operational realities that are shaping near-term decisions:
Administrative burden continues to limit capacity
The Medscape and HIMSS reports show that administrative and recordkeeping tasks are among the most common areas of AI deployment, reflecting a focus on the highest-friction work in the system. Agentic AI fits because these workflows are high volume, rules-driven, and measurable, so that improvements can be tracked and scaled across departments.
Throughput and consistency have become strategic priorities
Healthcare organizations manage thousands of transactions daily, and small delays between workflow steps compound quickly. Agentic AI is attractive because it reduces “time between steps” by coordinating actions and triggering the next task without waiting on manual handoffs. This is a direct path to higher throughput and more reliable execution.
Pilots are no longer enough
AI adoption is moving into procurement, governance, integration, and performance measurement, because the priority is now enterprise deployment. This aligns with broader adoption signals, including McKinsey’s finding that 85% of healthcare leaders are exploring or already adopting gen AI capabilities. Agentic AI supports scalable programs because agents can be designed as repeatable capabilities that roll out across multiple teams and sites with clear controls and measurable performance.
Agentic AI use cases in healthcare
How is agentic AI being used in healthcare today? To make this practical, here are real-world examples and use cases that show how agentic systems are already delivering value. These examples illustrate how agentic AI moved from concept to disease detection and deployment across 2024 and 2025.
Post-discharge follow-up agent at UHS (Hippocratic AI)
Use case: Post-hospitalization monitoring and support
Universal Health Services deployed a generative agent from Hippocratic AI to automate follow-up calls after discharge. The agent checks symptoms, answers questions about instructions, and confirms medication adherence.
In early trials at two UHS hospitals, patients responded positively, and nursing staff were able to shift time away from routine follow-up calls and back to direct care. Because the tool showed value in identifying issues early and keeping patients engaged, UHS expanded the program across additional hospitals.
AI voice agent for insurance preauthorization at a Houston specialty clinic
Use case: Faster authorization turnaround and reduced staff workload
A large specialty clinic handling high volumes of insurance approval requests implemented by an AI preauthorization agent to automate the process overnight. The agent contacts insurer systems through IVR lines or portals, submits required procedure information, and retrieves authorization numbers by morning.
It can also pull required patient details from the EHR, complete forms, and send supporting documentation when needed. Staff reported that authorization turnaround times improved significantly, in many cases dropping from several days to about one day, freeing staff to focus on patient-facing work.
Autonomous appointment reminder calls
Use case: Reducing missed home-care visits
A well-known company added an AI voice agent to its scheduling workflow to handle appointment confirmation calls for home care visits. The agent contacts patients the day before a nurse visit, speaks naturally to confirm availability, and can help reschedule when needed.
Before this, staff often struggled to make reminder calls consistently due to time constraints. With the AI agent in place, the company has reported fewer no-shows, which helps maintain care continuity while saving time and operational costs. The solution also highlights how agentic AI can combine multiple tools, using a language model and telephony technology to complete a defined task.
AI clinical documentation assistant at Beacon Health
Use case: Reducing documentation burden
Beacon Health System piloted Oracle’s Clinical Digital Assistant, an ambient agent that listens during the visit and generates a structured encounter note. While the clinician focuses on the patient, the agent transcribes and organizes the discussion into clinical sections such as history, assessment, and plan.
Physicians reported that notes were more complete than typical documentation and that patients responded well to having more attention during the visit. The agent functions like a quiet scribe, with clinicians reviewing the draft for accuracy rather than writing from scratch.
Best practices for implementing agentic AI in healthcare
Successfully adopting agentic AI in healthcare takes more than choosing a platform. It requires a clear strategy, alignment among stakeholders, and ongoing oversight to ensure the system remains safe, reliable, and effective. Below are practical recommendations for implementing agentic AI, based on lessons from early adopters and industry best practices.
1. Set up strong AI governance
Begin with a cross-functional governance group that guides the introduction and management of agentic AI. This group should include IT, clinical leaders, data and AI specialists, compliance and security teams, and, where appropriate, patient advocates or ethics experts. The role of governance is to prioritize the right use cases, define safe operating rules, and speed adoption by providing structure, not by creating unnecessary barriers.
2. Define clear goals and the right use cases
Be specific about what agentic AI is expected to improve. For example, you may want to shorten patient wait times, reduce administrative costs, or improve follow-up completion. Choose use cases where autonomy adds real value and define success metrics upfront, so implementation is driven by outcomes rather than novelty.
3. Decide whether to build or buy
Assess whether to develop agentic AI in-house or adopt a vendor solution. Organizations with strong internal AI capability and unique data may choose to build or fine-tune models using frameworks and open-source options. Many others will benefit from healthcare-focused vendors that already support common workflows. In either case, involve the people who will use the system in demos and evaluations to ensure it fits real workflows.
4. Make privacy and security foundational
Because healthcare data is sensitive, privacy and security must be built into the plan from day one. Ensure that deployments involving PHI adhere to strict controls, including secure hosting, role-based access, and audit logging. If using external AI services, put the appropriate legal agreements in place, confirm data-handling practices, and routinely review logs for unusual activity or unauthorized access.
5. Start with pilots and scale in phases
Pilot agentic AI in a limited setting before expanding. Choose one department, site, or workflow where results can be monitored closely, and feedback can be collected quickly. A phased rollout helps catch issues early, validate impact using defined KPIs, and build internal champions who can support broader adoption.
6. Train and support staff adoption
Both technical teams and end users need training to use agentic AI safely and effectively. Clinicians and staff should understand how the agent works, where it helps, and where human judgment remains essential. Assign “champions” or power users who can support peers, and ensure technical teams are prepared to maintain the system, manage updates, and address errors.
7. Integrate the agent into existing workflows
Adoption improves when agentic AI fits into the tools teams already use. Aim for seamless integration solutions for systems such as EHRs, scheduling platforms, and pharmacy or revenue tools using standards like HL7 FHIR and APIs. Where possible, automate triggers so the agent starts work automatically at the right point, such as launching a follow-up workflow when a discharge summary is signed.
8. Build oversight and feedback into the design
Define which actions require human review, especially those with clinical or financial risk. Provide simple ways for users to flag issues, correct mistakes, and report concerns. This feedback should flow into regular review cycles so the system improves and stays aligned with policy and safety requirements.
9. Monitor performance and measure outcomes
Track KPIs before and after deployment and validate impact with structured measurement. Regularly audit outputs, such as AI-generated notes or agent-handled interactions, to ensure quality stays high. Monitor for drift, since performance can change when workflows or data patterns shift, and plan for periodic updates or re-training.
10. Address ethical and legal considerations early
Assess potential bias and define how it will be detected and mitigated. Ensure transparency by notifying patients when AI is involved in communications or care-related workflows. Many organizations establish ethics oversight through dedicated committees or existing quality boards and may need to update consent processes and clarify legal accountability where appropriate.
Challenges of implementing agentic AI in healthcare
Implementing agentic AI in healthcare comes with real obstacles, even though the potential value is strong. Healthcare is complex and has high stakes, which means autonomous systems must be introduced carefully and with the right controls. Below are the main challenges organizations need to address.
1. Data integration and data quality
Agentic AI works best when it can pull information from multiple sources such as EHRs, imaging systems, lab platforms, and care management tools. In many healthcare environments, that data is scattered across systems, stored in different formats, and often incomplete. When an agent cannot access clean, consistent, and complete data, the risk of inaccurate decisions increases. Solving this usually requires major effort in interoperability using standards like FHIR, along with data cleaning and governance.
2. Privacy and security
Agentic systems often rely on sensitive patient data and may take actions based on that data. This increases the importance of privacy, security, and compliance. Organizations must meet HIPAA and other regulations while also ensuring that AI agents do not store, share, or expose protected information inappropriately. Strong safeguards are essential, including encryption, strict access control, identity verification, and ongoing monitoring to detect suspicious activity.
3. Ethical and bias risks
Agentic AI can carry forward bias found in the data it was trained on, and in healthcare that can lead to unequal outcomes. If an AI agent performs better for one population than another, it can create real harm. For example, some diagnostic models may perform poorly when training data lacks diversity. This is why fairness testing, bias monitoring, and ethical oversight are increasingly necessary, often through formal review frameworks or governance committees.
4. Regulatory and compliance complexity
Regulation for autonomous AI in healthcare is still developing. In some cases, systems that influence clinical decisions may fall under medical device rules and require additional approval. Many current agentic AI deployments focus on administrative workflows or require human confirmation for clinical actions to reduce regulatory risk. As agent capabilities increase, organizations will need clearer compliance strategies to stay aligned with evolving guidance.
5. Fit within clinical and operational workflows
Even a strong agentic solution can fail if it does not fit how teams actually work. If clinicians must switch tools repeatedly, manage extra alerts, or correct frequent mistakes, the system can add friction instead of reducing it. The ‘last mile’ of adoption is often the hardest part, which is why change management and stakeholder alignment—core topics in a global health leadership program—matter as much as the technology.
How Xavor can support your agentic AI journey
Building and deploying agentic AI in healthcare takes more than strong engineering. It requires healthcare domain understanding, alignment with regulatory and privacy requirements, and the ability to integrate smoothly into real clinical and operational workflows. Xavor brings experience in building AI-enabled healthcare solutions that support intelligent automation, workflow coordination, and patient-facing engagement.
- Custom AI development: Design and implement agentic AI systems that support clinical workflows, streamline administrative operations, and improve decision support where it adds measurable value.
- Healthcare-focused AI expertise: Address real operational and clinical challenges using AI tailored to healthcare data, policies, and care delivery environments.
- Seamless integration and scalability: Integrate AI agents into existing EHR environments, patient platforms, and backend systems, so adoption is practical, secure, and maintainable over time.
If you are looking for agentic AI companies and planning to implement agentic AI in your healthcare solutions, partner with Xavor to build systems that deliver measurable workflow impact while meeting the reliability and governance expectations of healthcare environments.
Conclusion
Agentic AI is becoming a real operating advantage for healthcare organizations because it helps work move forward without constant manual coordination. The organizations that benefit most will treat agents as part of the operating model, build them around the workflows that drive performance, and put clear boundaries, auditability, and oversight in place from day one.
The next step is simple: pick one high-volume workflow where delays are visible, measure today’s cycle time and manual touches, then design an agent that reduces friction while staying inside policy controls. Once value is proven, scale it carefully across teams and sites.
If you are exploring or building agentic AI in healthcare, Xavor can help you identify the right use cases, design the agent workflows, and integrate them into your environment responsibly. Reach us at [email protected].
FAQs
Yes. Most healthcare agentic AI implementations integrate with EHRs and operational systems using APIs and standards such as HL7 FHIR. Successful deployments focus on fitting agents into existing workflows rather than forcing teams to adopt new tools.
No. Agentic AI is designed to reduce administrative burden and operational drag, not replace clinical judgment. It handles routine coordination and execution tasks so clinicians and staff can focus on patient care and higher-value work.
The best place to start is identifying one high-volume workflow where delays or manual effort are holding teams back. Share your goals and ideas with us, and we’ll help define success metrics, pilot an agent with clear boundaries, and scale based on proven results.