AI agents in healthcare: 12 real-world use cases (2026)

Here’s how AI agents in healthcare solve everyday problems across provider, patient, and core business workflows.

Healthcare is grappling with mounting pressure from every direction. While providers are facing staff shortages and rising patient demand, payers, on the other hand, are managing growing claim volumes, complex authorizations, and increasing expectations from members. Across the board, the work of delivering and financing care has grown more complex.

According to the World Economic Forum, the global healthcare system is expected to face a shortage of 10 million workers by 2030. At the same time, even fairly routine tasks, like submitting a claim or scheduling a patient, now take too much time as the work sprawls across teams and systems. It’s telling that around 15% of healthcare claims are denied on first submission, often for avoidable reasons, even when those claims were pre-approved. 

The real problem is that too much healthcare work still depends on chasing updates, checking multiple systems, and piecing things together by hand. That fragmentation slows care delivery, increases administrative burden, fuels staff burnout, and creates frustration for patients and members alike.

This is exactly where AI agents in healthcare are starting to step in. Healthcare AI agents are designed to keep work moving when demand outpaces capacity. They handle end-to-end workflows autonomously, while bringing humans into the loop when clinical judgment is required. 

In this blog, we explore how healthcare AI agents are being used across everyday scenarios, and how they give much-needed breathing room to focus on delivering exceptional patient care.

Key takeaways (The TL;DR)

• Healthcare is too dynamic for rigid automation. AI agents succeed because they adapt to context and keep the workflow moving as conditions evolve.
• Healthcare AI agents go beyond answering questions. They take ownership of work across systems and teams, carrying tasks through to completion.
• The real payoff comes from eliminating follow-ups. When AI agents handle routine execution, clinicians and staff spend less time chasing and more time delivering care. 

What are AI agents in healthcare?

AI agents in healthcare are autonomous software systems that run workflows end-to-end. They use chat, voice, or text to understand requests, pull information from multiple systems, apply clinical or operational rules, and carry tasks through to resolution. When approval is required, they bring the right human in with full context.

Unlike standalone automation tools, healthcare AI agents are embedded directly into everyday clinical and administrative processes, where work actually happens. 

The word “agentic” signals a shift from reactive assistance to accountable execution. Earlier chatbots and virtual assistants were largely conversational. They answered FAQs, routed queries, or followed scripted flows. Once a request moved beyond predefined logic, the work stopped or was handed off. 

AI agents behave differently. They can: 

• Interpret intent across voice, chat, or text 
• Gather context from EHRs, payer systems, and enterprise platforms 
• Determine next steps based on policies and real-time inputs 
• Execute actions across systems 
• Track progress until completion 
• Escalate when human oversight is required 

In practical terms, they function like a digital team member. They take action where needed without constant manual follow-ups. It’s this ability to act autonomously that makes AI agents particularly well-suited to healthcare, where workflows are rarely linear, and conditions change quickly.

Why AI agents in healthcare matter?

The value of healthcare AI agents becomes clear when you look at how work actually unfolds day to day – and where traditional automation has fallen short. 

Over the years, healthcare organizations have invested heavily in digitization. Yet administrative costs in US healthcare still account for nearly 25-30% of total spend, and clinicians continue to spend over 13 hours a week on documentation alone. The problem isn’t a lack of systems. It’s that most automation has been task-based rather than workflow-based. 

That’s where AI agents shift the model.

Take a routine outpatient appointment, for instance. Before the visit, one AI agent can pull together a clear view of the patient’s history, recent lab results, medications, and reason for visit, so the clinician walks in prepared. During or after the appointment, another AI agent captures key details, drafts the clinical note, updates the record, and sets up follow-up actions. At the same time, other AI agents check policies, submit requests, route approvals, and make sure nothing falls through the cracks. 

What matters here isn’t any single action. It’s that the work moves forward without relying on someone to remember the next step, chase an update, or re-enter the same information elsewhere. 

And this is where the gains compound. If a clinician saves even 10-15 minutes per patient through better preparation and documentation, that translates into hours recovered each week. If denial rates drop by even a few percentage points, revenue leakage shrinks materially. If fewer handoffs stall, throughput improves without adding beds or staff.

It’s a well-established fact that many of the biggest delays and frustrations don’t come from difficult decisions, but from all the small gaps between them. Healthcare AI Agents help close those gaps by taking responsibility for the flow of work across systems and teams. 

When that happens consistently, the impact is structural. Clinicians regain time. Administrative teams reduce rework. Payers see fewer avoidable errors. Patients experience smoother journeys. And the organization begins to operate as a connected system rather than a collection of disconnected tasks.

Top 12 use cases where healthcare AI agents create value

In healthcare, very little work follows a straight line. This is where AI agents come into their own. Rather than relying on fixed scripts or rigid automation, AI agents can respond as situations evolve and adjust on the fly as new information becomes available. 

This flexibility is why AI agents fit so many healthcare scenarios. There are three broad areas where healthcare organizations are seeing the most value today:

• Provider use cases - supporting clinicians, care teams, and administrative staff in delivering and coordinating care more efficiently 
• Patient use cases - improving access, communication, and financial clarity across the patient journey
• Health plan and core business operations use cases - strengthening claims, billing, compliance, onboarding, and other operational workflows that keep the healthcare system running

Reimagine healthcare provider use cases: How AI agents help with productivity

Clinicians, administrators, and support staff lose a surprising amount of time preparing for work and follow-ups. Here’s where AI agents start to ease the load:

Use case 1 - Employee self-service and internal support

The problem: 

Healthcare support teams, such as HR, IT, billing, and operations,  deal with a steady stream of internal requests. Yet many of these still rely on phone calls and basic ticketing systems, where even simple queries turn into long waits and repeated explanations. 

In fact, average healthcare call center hold times exceed 4 minutes, with only about 50% of calls being resolved on first contact, leading to repeated follow-ups. 

How AI agents help:

AI agents remove the waiting game by interpreting the request and acting on it immediately. 

For example, when a clinician notices that a patient’s claim is on hold, the agent does not simply log a ticket. It retrieves the relevant encounter details from the EHR, cross-checks payer requirements, identifies what is missing, and initiates the appropriate correction. Another agent gathers the required documentation and updates or resubmits the claim. If approval is needed, it routes the case to the right billing specialist with the full context attached. 

Rather than leaving the clinician to chase updates, the agent tracks the issue through to resolution and escalates only when human judgment is required.

The result:

• Higher first-contact resolution 
• Shorter wait times 
• Reduced internal back-and-forth 
• Clinicians spend more time delivering care 

Use case 2 - Access to information across systems

The problem:

Healthcare staff spend an unreasonable amount of time searching for information. Patient records, policies, referrals, lab reports, and billing details sit across EHRs, document systems, payer portals, and shared drives. 

More than 80% of healthcare data is unstructured, and clinicians often navigate multiple systems just to prepare for a single visit. In fact, physicians spend nearly a quarter of their workday interacting with the EHR and desk work, rather than directly with patients.

This slows decision-making and increases risk as clinicians have to either guess or chase someone else for clarity. 

How AI agents help:

Healthcare AI agents act as a single access layer across systems. Instead of expecting staff to remember where information lives, the agent retrieves it on demand and presents it in context. 

Imagine a clinician preparing for a patient visit. Rather than opening multiple applications, she asks a question through chat or voice. The AI agent pulls structured data from the EHR, extracts relevant insights from recent lab reports, checks medication history, scans unstructured notes for important flags, and validates that required documentation is complete. 

If inconsistencies appear, such as missing lab values or conflicting entries, the agent highlights them before the consultation begins. What would normally take several minutes of system-switching is delivered as a consolidated, verified summary.

The result:

• Faster clinical preparation 
• Fewer documentation gaps 
• Reduced risk from incomplete information 
• More time focused on patient interaction  

Use case 3 - Clinical documentation automation

The problem:

Clinical documentation has quietly become one of the heaviest drains on clinician time. On average, physicians now spend over 13.5 hours a week on documentation, with much of it spilling into evenings and weekends. 

Clinicians have to switch between patient conversations and screens, re-enter the same information, and often complete notes long after the visit ends. This not only slows care but is also a well-documented driver of burnout. 

How AI agents help:

Healthcare AI agents shift documentation from a retrospective task to a real-time workflow. 

For example, during a routine consultation, an AI agent listens to the interaction with patient consent and captures clinically relevant details as the conversation unfolds. A second agent structures the content to align with documentation standards, coding requirements, and compliance guidelines. It updates appropriate EHR fields automatically and checks for missing elements or inconsistencies before the note is finalized. 

Instead of starting from a blank screen, clinicians review a structured draft, make quick edits where necessary, and approve. If required information is incomplete or contradictory, the agent flags it immediately rather than allowing gaps to surface later in billing or audit processes.

The result:

• Significant reduction in documentation time 
• Lower after-hours charting burden 
• Improved coding accuracy and compliance 
• Measurable reduction in burnout

According to the American Medical Association, organizations using AI-driven documentation save over 15,000 hours annually and see a measurable reduction in burnout.

Use case 4 - Care team coordination and handoffs

The problem:

Care in healthcare rarely sits with one person. It moves between clinicians, nurses, specialists, labs, and administrative teams. Yet coordination across those handoffs is still largely manual, relying on notes, inboxes, and someone remembering to follow up. This poor coordination contributes to longer wait times and underused beds.

How AI agents help:

AI agents help by taking responsibility for how work moves between people. 

Consider a patient being discharged who requires follow-up imaging and an outpatient consultation. Instead of leaving coordination to manual outreach, an AI agent tracks the discharge order in real time. It checks appointment availability, schedules follow-ups based on urgency and provider capacity, and notifies the relevant care teams automatically. 

A second agent verifies that discharge documentation, prescriptions, and referral notes are complete before the patient leaves. If the required information is missing, it flags the issue immediately. If scheduling delays or resource bottlenecks appear, the agent escalates to the appropriate team with full context attached.

The result:

• Fewer stalled handoffs 
• Reduced discharge delays 
• Better utilization of beds and clinical capacity 
• Improved patient continuity of care

Reimagine patient use cases: How AI agents help with customer experience

From a patient's point of view, healthcare often feels harder than it needs to be. Simple tasks like booking appointments, filing intake forms, or keeping track of next steps still involve long calls and repeated questions. AI agents smooth out these moments, wrestling with an experience that feels responsive and predictable:

Use case 5 - Patient scheduling and intake

The problem:

Patient scheduling and intake remain surprisingly manual across much of healthcare. Nearly 88% of healthcare appointments are still booked by phone, with average call durations exceeding eight minutes. This creates long wait times, scheduling conflicts, and high no-show rates. 

How AI agents help:

AI agents improve this by treating scheduling and intake as a single, connected workflow. For instance, imagine a patient booking an appointment through chat or voice instead of calling the front desk. An AI agent checks provider availability, confirms eligibility, and books the visit. 

Another agent follows up ahead of time to collect intake information and complete forms. If the patient needs to reschedule, the agent adjusts the calendar automatically and reduces the risk of a missed appointment. 

The result: 

• Drop in front-desk workload
• Clinicians see better-prepared patients
• Fewer slots go unused

Use case 6 - Billing, claim status, and financial assistance

The problem:

For patients, billing is often the most confusing part of the healthcare experience. Statements arrive days after a visit, claim statuses are unclear, and questions about insurance coverage usually mean long calls and repeated explanations. 

In fact, billing and claims-related queries are among the highest drivers of contact center volume. All of this results in multiple follow-ups from patients and erosion of trust.

How AI agents help:

AI agents help by giving patients clear, real-time visibility into their financial journey. Instead of routing every question to a human agent, AI agents retrieve claim and billing details, explain balances in plain language, and guide patients through next steps, including payment options or financial assistance. 

Take, for example, a patient who checks the status of a recent claim through chat. An AI agent pulls claim details from billing systems, confirms payer responses, and explains what’s pending or owed. If the patient qualifies for financial assistance, another agent surfaces relevant options and initiates the application. When a question needs review, the case routes to a human expert with the full context already attached.

The result:

• Patients get answers without long waits
• Billing teams deal with fewer repetitive inquiries
• Issues are resolved faster, with less back-and-forth on both sides

Use case 7 - Reminders and follow-ups

The problem:

Missed appointments and incomplete follow-ups remain a persistent problem in healthcare, resulting in wasted clinical time and delayed care. With no-show rates reaching up to 30%, missed appointments alone cost the healthcare system billions each year. 

How AI agents help:

AI agents help by treating reminders and follow-ups as an ongoing workflow. Instead of sending generic reminders, agents tailor outreach based on appointment type and patient behaviour. 

For example, after an appointment is scheduled, an AI agent sends timely reminders through the patient’s preferred channel. Another agent follows up with preparation instructions, checks whether forms are complete, and answers common questions. 

After the visit, agents guide patients through the next steps, such as medication instructions, follow-up appointments, or outstanding paperwork. If something is missed, the agent nudges again or escalates to staff only when needed.

The result:

• Patients stay informed without feeling chased
• Staff spend less time making reminder calls.

Use case 8 - Personalized self-service across channels

The problem:

Patients rarely stick to one channel. They call when they’re in a hurry, use chat when they’re at work, and follow up via email after hours. Yet many healthcare organizations still treat each interaction as a fresh start, forcing patients to repeat themselves and staff to retrace steps. This fragmentation drives frustration and unnecessary contact volume. 

How AI agents help:

AI agents improve this by delivering personalized self-service that carries context across channels. Whether a patient starts on chat, switches to voice, or follows up later, agents recognize intent, retrieve relevant information, and pick up where things left off. 

The result:

• Patients get quick personalized answers
• Support team handles fewer repetitive queries

Organizations using AI-powered self-service see self-service handling increase by over 30%, alongside shorter wait times. 

Reimagine core business operations: How AI agents help with healthcare processes 

Every healthcare enterprise requires a seamless operational backbone. Claims, authorizations, onboarding, and billing workflows determine how quickly care is delivered. This is where AI agents help healthcare teams keep work moving at scale:

Use case 9 - Claims management and denial reduction

The problem:

Claims processing remains one of the most manual and error-prone workflows in healthcare. Billing teams move between EHRs, clearinghouses, and payer portals to submit and reconcile claims, all while navigating payer-specific rules. 

This results in claims being denied, directly affecting margins and cash flows. In fact, health systems spend close to $20 billion each year contesting claim denials. 

How AI agents help:

AI agents help by managing claims workflows end-to-end. For example, once a visit closes, an AI agent pulls encounter details from the EHR and reviews coding and documentation. Another agent checks payer requirements and identifies missing or mismatched information before submission. 

If corrections are needed, the claim routes back with clear guidance. After submission, agents track claim status across payer systems and act on denials automatically, escalating only complex cases to billing specialists. 

The result:

Billing teams no longer spend their days chasing statuses or reworking avoidable errors. According to AHA, AI-driven claims workflows see denial rates drop by up to 40% and faster reimbursement through improved first-pass yield.

Use case 10 - Provider onboarding and credentialing

The problem:

Bringing new providers on board should be straightforward. In reality, onboarding and credentialing are manual and highly fragmented. Verifications span multiple organizations, data sources, and compliance checks, many of which still rely on emails and follow-ups. The result is long delays before providers can see patients.

How AI agents help:

AI agents help by turning onboarding into a coordinated workflow. Instead of waiting for each step to be triggered manually, agents validate credentials and move the process forward as soon as prerequisites are met. 

For instance, when a new provider joins, an AI agent collects required documentation and initiates primary source verification across licensing boards and credentialing bodies. Another agent tracks responses, flags missing or inconsistent information, and requests clarification automatically. As approvals come through, agents update internal systems, trigger provisioning, and prepare compliance records.

If something stalls, the issue is instantly escalated to human experts with clear context. 

The result:

• Faster onboarding 
• Less follow-ups
• Providers who can start seeing patients sooner

Use case 11 - Billing and payment management 

The problem:

Even after a claim is approved, getting paid is rarely straightforward. Payments arrive late, amounts don’t always match expectations, and reconciliation often requires teams to compare EHRs, billing systems, bank records, and payer explanations of benefits. 

Even small mismatches create outsized work where staff spend hours tracking down payments and responding to patient questions about balances that don’t quite add up.

How AI agents help:

AI agents help by managing billing and payment workflows end-to-end. For example, when a payment is received, an AI agent matches it against submitted claims and payer remittance details. 

Another agent identifies underpayments or missing line items and initiates follow-up with the payer. At the same time, a third AI agent updates patient balances and generates clear statements.

The result:

Billing teams no longer have to chase every payment or manually reconcile records, resulting in improved cash flow and fewer billing errors

Use case 12 - Compliance and audit readiness

The problem:

Compliance in healthcare is constant. Regulations change, documentation requirements evolve, and audits can surface months after work is done. Yet much of compliance tracking still relies on periodic reviews and teams scrambling to reconstruct what happened after the fact. 

This reactive approach creates risk. Inconsistent records or unclear decision trails can lead to penalties and reputational damage. 

How AI agents help:

AI agents help by treating compliance as an ongoing workflow rather than a last-minute exercise. Instead of checking work after it’s complete, agents monitor processes as they happen, ensure required steps are followed, and keep an auditable record of decisions and actions. 

For example, as claims are processed, authorizations submitted, or providers onboarded, AI agents track whether required documentation, approvals, and policies are in place. 

Another agent maintains a clear audit trail, linking actions back to rules and source data. If something falls out of line, the issue is flagged early and corrected before it becomes a finding.

The result:

Whenever an audit does occur, teams don’t have to scramble as records are complete and evidence is already organized. This allows healthcare enterprises to gain confidence to scale without risk.

Real-world case studies of AI agents in healthcare

Healthcare enterprises are already seeing tangible results by deploying AI agents in complex, high-demand environments. Here’s how agentic AI works in practice:

Case study 1 - Scaling patient access at a California Healthcare Provider

A California-based healthcare provider faced rising patient demand and increasing strain on its support teams. High call volumes, complex scheduling needs, multilingual interactions, and limited staffing flexibility made it difficult to deliver timely assistance. 

The organization implemented pre-built AI agents tailored for healthcare workflows. These AI agents were designed to handle high-volume, repeatable patient interactions across channels and languages. They automated routine tasks, from appointment scheduling and reminders to lab and pharmacy queries, and provided support beyond standard clinic hours, with seamless escalation to human teams for more complex needs.

Over time, the deployment delivered measurable outcomes: 

• $3.2M in revenue enabled 
• 468% ROI 
• 24% inquiry containment 

Read full customer story

Case study 2 - Modernizing contact center operations at a major Health Insurer

A large North American health insurer serving millions of members struggled with rising call volumes and manual documentation burdens in its contact center. Voice and digital agents were fielding high volumes of routine inquiries, many of them repetitive, and live service representatives were spending significant time on post-call documentation and research. 

The organization rolled out AI agents in a phased manner to modernize member service delivery. 

Phase 1 introduced AI-powered front-end automation to contain routine inquiries and route members more efficiently. 

Phase 2 added AI-driven transcription and summarization, automatically capturing call details and reducing documentation workload. 

Phase 3 brought real-time intelligent agent assistance, offering live contextual support and suggested next steps for service representatives during complex interactions. 

The phased approach yielded meaningful improvements across operations and member experience: 

• 40% lower operational costs 
• Reduced post-call documentation 
• Improved multilingual accuracy

Read full customer story

How does Kore.ai help bring AI agents into healthcare?

Kore.ai helps healthcare organizations bring AI agents into everyday work, safely and at scale. The platform is built to handle regulated environments, complex workflows, and the reality of how healthcare operates day to day. 

Secure and compliant by design 

Kore.ai is built for HIPAA-regulated environments, with role-based access, PHI masking, audit trails, and human-in-the-loop controls. Security and governance are part of the foundation, not bolted on later. 

Pre-built AI agents for healthcare 

Launch quickly with 75+ pre-built healthcare AI agents designed around real clinical, patient, and operational workflows. Teams can adapt agents as policies and processes change, without rebuilding workflows.

Healthcare-ready integrations 

Kore.ai connects directly to EHRs, payer platforms, revenue cycle tools, scheduling systems, and more through 250+ pre-built connectors, so agents work with a full clinical and administrative context. 

Multi-agent collaboration 

Kore.ai agents work together, passing context, handling routine steps, and escalating exceptions when judgment is needed, keeping workflows moving across teams and systems. 

No-code and pro-code deployment 

Teams can design and scale AI-powered workflows using a visual, no-code builder, while developers extend and customize where required, without disruption. 

Analytics and performance visibility 

Built-in analytics show how agents perform in real time, highlight exceptions, and support continuous improvement through clear, actionable insights.

How to build an Agentic healthcare enterprise today

Getting started with AI agents in healthcare doesn’t require a wholesale transformation on day one. In fact, the organizations seeing the most value tend to start small and build from real work. Here are a few principles that can help your healthcare business: 

1. Start with workflows, not technology 

AI agents work best where processes are end-to-end, involve multiple systems, and change as new information comes in. 

Instead of asking where AI might fit, start by looking at workflows that regularly stall, such as claims, authorizations, scheduling, documentation, or internal support, and ask where work slows down or breaks. 

If a task is simple and one-off, traditional automation may be enough. AI agents earn their keep when coordination, context, and follow-through matter. 

2. Focus on a small number of high-impact use cases 

It’s tempting to experiment everywhere at once, but that often leads to pilots that never scale. The better approach is to pick a few use cases with clear operational or experience impact and build momentum there. 

Once agents are embedded into real workflows and delivering value, it becomes much easier to extend them into adjacent use cases and create a flywheel effect across the organization. 

3. Design for collaboration between agents and people 

In healthcare, autonomy always needs boundaries. AI agents should move work forward, but humans should stay involved where judgment, approval, or clinical oversight is required. 

Clear handoff points, audit trails, and escalation paths build trust and make it easier for teams to adopt agent-led workflows with confidence. 

4. Build on a platform that can grow with you 

As use cases expand, complexity increases. Some workflows can be handled by a single agent, while others benefit from multiple agents working together, each with a clear role. 

Choosing a platform that supports multi-agent collaboration, deep healthcare integrations, and strong governance makes it easier to scale without constantly re-engineering what’s already in place. 

5. Bring people along early 

AI agents change how work gets done, which means change management matters. Teams need to understand where agents help, how they fit into daily work, and how success will be measured. Organizations that invest early in communication, training, and feedback loops tend to see faster adoption and better outcomes than those that treat AI purely as a technical rollout.

Conclusion: Where healthcare AI agents go next

As healthcare demand rises and capacity stays tight, organizations will rely less on manual coordination and more on agent-led workflows that keep work moving end to end. 

What changes next isn’t just efficiency, but expectations. Healthcare teams will expect work to progress without constant chasing. Patients will expect clearer, faster interactions. Operations leaders will expect real-time visibility, not after-the-fact reports. 

AI agents make this possible by taking responsibility for the flow of work, adapting as conditions change, involving humans when needed, and scaling without adding complexity. 

The organizations that move first will give their people the space to focus on care and outcomes, while AI agents handle the coordination in between. 

Learn more about how Kore.ai helps healthcare organizations put AI agents to work across provider, patient, and core operational workflows.

FAQs

Q1. How are healthcare AI agents different from chatbots? 

Chatbots mainly answer questions. AI agents take responsibility for getting work done. They don’t stop at responding — they retrieve context, take action across systems, track progress, and follow through until a workflow is complete. 

Q2. Do AI agents in healthcare work independently, or do they need to be carefully scripted? 

AI agents operate with clear boundaries rather than rigid scripts. They follow defined rules, policies, and permissions, but adapt their actions as situations change. That balance allows them to handle real-world variability without losing control. 

Q3. What happens when a healthcare AI agent encounters something it can’t resolve? 

When agents reach ambiguity, risk, or policy limits, they escalate to humans with full context attached. This means staff don’t start from scratch; they see what’s been done, what’s missing, and what decision is needed. 

Q4. Can AI agents in healthcare work across clinical and administrative workflows at the same time? 

Yes. Many of the strongest use cases sit at the intersection of clinical and operational work. AI agents are particularly effective when they can pass context between clinical systems, billing platforms, and administrative tools without breaking the flow. 

Q5. How do healthcare enterprises avoid creating yet another layer of complexity? 

By embedding agents into existing workflows rather than adding parallel tools. The goal isn’t to introduce more systems, but to reduce handoffs, duplication, and manual coordination across the systems already in place.

Q6. What does success with AI agents look like in practice? 

Fewer follow-up calls. Fewer stalled cases. Less rework. Teams spend less time chasing and more time completing work. Patients notice smoother journeys even if they never realize an agent was involved.

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