AI Agents in Healthcare: Our Insights on How They Benefit the Industry

TL;DR

• From 2020 to 2023, the AI healthcare market grew by over 230%, reaching $22.4 billion.
• Our case study: An AI agent for digital transformation helped a clinic handle referrals 45% faster and reduce the number of support tickets by 33%.

What AI Agents in Healthcare Do:

• Complete administrative tasks like scheduling and extracting data from insurance cards.
• Support decision-making by analyzing patient history to flag risks or recommend actions.
• Route insurance approvals, send lab results, and coordinate follow-ups.
• Provide always-on patient support, check symptoms, and remind patients about medications.
• Help identify ideal drug candidates and monitor patient cohorts.

Use Cases of AI Agents in Healthcare:

• NIB Health Insurance: The digital assistant Nibby reduced the need for human patient support by 60%.
• Deep Medical: The predictive booking system identifies likely no-shows and backfills appointments.
• Flok Health: Kirsty, an AI physiotherapist, triages patients with back pain and offers treatment plans.
• Charta Health: AI automates chart reviews, surfacing missing billing codes and flagging errors beforehand.

Pitfalls of Deploying AI Agents in Healthcare:

• Using chatbot UIs instead of full integration
• Neglecting ongoing retraining
• Ignoring security and compliance
• Relying on shallow AI

An AI Agent for Healthcare in Action: How a Medical Care Provider Cut Referral Handling Time by 45%

Everyone’s trying to digitize their operations—some follow the trend, others want to stay ahead in the market, but our client didn’t have a defined goal. What they had was a growing sense of friction. Or, as they put it: “We’re spending more time collecting patient data than actually using it. We need something that works for us, not otherwise.”

Client’s Challenges

An established regional clinic network started to notice some problems with their usual processes: intake was slow, care coordination was disorganized, and staff were switching between tasks that didn’t belong to their job descriptions.

Despite these issues, the clinic wasn’t falling apart. Patients were checked in, labs were processed, and claims were filed. But behind the scenes, personnel were drained with routine, re-entering the same information into different systems and double-checking all documents before visits. And every form meant another delay, another follow-up, another phone call. Clearly, their system wasn’t built to scale, so they asked us for a solution.

The Backstory

Before we got to work, we ran a discovery phase and found the client had already tried basic automation—template forms, email confirmations, and a scheduling tool. But none of these fit how they worked, so adoption wasn’t going well. One team member said they were working around the system, not with it. We took that in and started thinking.

Final Solution

We quickly became aware that another “bot” wasn’t for them. After some brainstorming and discussion, we landed on the idea – designing an AI agent. Our solution quietly picked up the low-value work nobody had time for. No new dashboards, no workflow changes, no steep learning curves – just more focus and a system that finally worked the way people wanted it to. 

Here’s what our agent does:

• pulls key data from referrals
• checks insurance info
• matches patients with the right specialists
• fills out intake records

And the best part: it all happens inside the tools they are already using. Within weeks, data quality went up, task queues got lighter, and the team started delegating repetitive or just boring stuff to the agent.

Project Outcomes

Over time, key KPIs revealed the bigger difference our AI made:

• Time for referral handling dropped by 45%, which gave admins more time for real coordination.
• Document correction requests fell by 38% with cleaner intake data.
• Specialist assignments got 22% more accurate, which meant fewer last-minute reshuffles.
• Support tickets from staff decreased by over a third, mostly because the usual routing problems had disappeared.

Want to know how a custom AI agent can help your clinic? Let’s chat.

Why Healthcare Needs a Smarter Operating Model with AI Agents

When we talk about automation in healthcare, most organizations think only of clinical care. But a lot of the daily pressure comes from everything around it: checking insurance, answering requests, entering patient info, etc. It’s not hard work, but it’s time-consuming and distracting.

Let’s talk about that.

• Lack of automation. Lots of businesses are only starting to learn about integrations and automated workflows—most processes are manual. Here’s a quick example of how it hurts your business: a patient submits a digital form, but someone still has to copy that data into the EHR. Seems like a minor task, but repeated a thousand times over a month, it makes a whole working day. 
• Rising customer expectations. We’re living in a technology boom, which means patients are used to apps, instant replies, and personalized everything. And healthcare providers often expect teams to do more, not giving them extra resources. As a result, you have a frustrated staff and patients who don’t understand why it takes so long to get a response.
• Staff burnout. Burnout rates in healthcare are among the highest, 50% on average and 66% in nursing. You don’t need a nurse to follow up on missing insurance details, but without automation, they’re often swamped with work that doesn’t match their skills. Over time, that leads to disengagement and, eventually, layoffs.

All of these problems are common for healthcare, and we want to change that with AI innovation. Here’s how our senior AI engineer describes it:

People are afraid AI is going to replace them, but it won’t—it’ll help them. Right now, it can read patient forms, validate data, check insurance status, route tasks, and answer something like ‘Where’s my lab report?’ These are the tasks no one likes doing.

And it takes away stress, too. We often procrastinate things we don’t enjoy and then worry about them. With AI, that just doesn’t happen. For example, a client submits an intake form, and the AI agent instantly validates it, checks coverage, and routes the case to the right department—no waiting for the patient, no effort for the staff.

What an AI Agent in Healthcare Does

When people hear “AI agent,” they usually think of a chatbot that answers a few questions or schedules an appointment. But in healthcare, that’s just scratching the surface. Let’s look at some tasks AI solutions can help with—or fully take over.

Task Execution Across Clinical and Admin Workflows

AI agents are no longer just passive observers—you can now compare them to humans in terms of what they can do and how independently they operate. Pulling data from insurance cards and checking eligibility, filling out lab forms and sending results to both patients and doctors, preparing bills, and writing up documentation—and that’s not even the whole list.

Besides the obvious benefit of giving doctors and admin staff more time for patient satisfaction improvement and meaningful work, we’ve also seen some measurable improvements: agents cut intake and documentation time by 50% and reduce manual report generation to nearly zero. On top of that, they help predict patient volume and acuity, so leaders can make smarter staffing decisions.

Clinical Decision Support in Real Time

In laboratory settings, AI agents help sort through and analyze records, imaging, and patient history for early diagnostics and preventive treatment, or just point out things a doctor may want to double-check.

Some of these tools have already gotten the green light from the FDA. Take Prenosis, for example—the first-of-its-kind medical device that diagnoses sepsis. It is built on a massive dataset: more than 100,000 blood samples from around 25,000 patients. This AI-powered solution looks at 22 clinical signs, including heart rate, temperature, and blood cell counts, and alerts the doctor if the patient is at risk.

Despite common skepticism, Prenosis and similar tools have proven to be more accurate than humans. With the right use, they can cut medical errors by 30–40% and bring down treatment costs by half.

Intelligent Data Routing and Workflow Orchestration

A task as simple as sending lab results to the doctor can take up lots of space in the minds of nurses and administrators. AI agents can do it quietly, instantly, and in the background, so your employees don’t have to worry about routing insurance approvals, assigning patients to the doctors, or flagging urgent cases. 

For example, if a patient fills out a form before their visit and mentions something that needs immediate attention, the AI can flag it for a nurse, create a triage task, and adjust the day’s schedule on its own.

Continuous Monitoring and Patient Engagement

Humans can’t stay in touch day and night, but AI doesn’t need sleep. Personal health assistants constantly check in on patients’ symptoms, send medication reminders, and ask for updates after discharge—they can even call an ambulance if a person needs it.

And they can diagnose, too. Recently, AI tools have achieved 98% accuracy in detecting tuberculosis via chest X-rays, outperforming human radiologists by 96%. No need to say AI does it way faster than humans can. So, non-stop monitoring, early diagnostics, and personalized follow-up care—that’s what you can expect from AI agents in healthcare.

Drug Discovery and Trial Acceleration

Not only for care, AI agents are also used in research operations. Finding promising drug candidates, picking out patients who are a good fit for clinical trials, and tracking how different groups respond – you can do it all with AI.

Especially valuable for rare diseases, cancer treatments, or high-risk patients, these AI features shorten development cycles and improve trial targeting, leading to the most important change: fewer deaths and healthier nations.

Want to build your personal AI agent? Don’t hesitate and connect with us for details.

High-Impact Use Cases of AI Agents in Healthcare Workflows

What was an experimental tool a few years ago is now a critical part of any functional healthcare setup. Faster, leaner, and more accurate are three words to describe operational efficiency achieved with AI agents. Let’s discover some use cases.

Healthcare AI Agent for Patient Access and Appointment Management

Front desks deal with backlogs, overbooking, and endless calls daily. AI agents come to the rescue, confirming appointments, sending reminders, and coordinating scheduling via chat, phone, and the web. Here are a few actual instances:

• Nibby is a digital assistant developed by NIB Health Insurance. Since its launch, they have saved $22 million in expenses by reducing the need for live support (almost 60%) and their call volume (about 15%).
• Deep Medical created a system that anticipates which patients might miss their appointments and automatically fills “uncertain” slots. As a result, their calendar stays full, and no one has to micromanage it. 

AI Agent in Healthcare Data Intake and Triage

Every visit, patients fill out a form, but in many places, someone still has to read those forms line by line. Maybe they don’t know AI agents can already check insurance information, conduct prior authorizations, and direct customers to the right location before they even enter the building. As an illustration:

Kirsty, an AI physio assistant, was released by Flok Health in Scotland. Through an app, it talks to patients with back pain, collects their information and symptoms, provides treatment plans, and highlights any issues that need further attention. As expected, their intake became a piece of cake, and front-line staff can finally spend less time on data work.

AI Agent for Medical Document Processing

Legal and clinical documentation, referrals, lab results, patient charts — these pile up quickly in any hospital and soon become a burden for the staff. AI agents trained in natural language processing (NLP) and intelligent document processing (IDP) can deal with these tasks in a matter of seconds. Here’s how:

To manage chart reviews, Charta Health developed an AI-powered system that finds errors in insurance documents and highlights missing billing codes. With this setup, EHRs remain current, documentation becomes cleaner, and the clinical and billing teams work more quickly, with a lot less double-checking.

Internal Workflow Automation with AI Agents in Healthcare

Hospitals don’t run only on doctors and nurses—they run on a flow of internal requests, like IT issues, HR questions, treatment updates, etc. And AI agents can manage, triage, and even resolve many of these. Here’s one of our cases to demonstrate the real-world impact of AI solutions:

We recently worked on a project with a city hospital that wanted to automate a few internal procedures, like knowledge sharing and data flow. To assist staff with daily inquiries, we created an AI agent. Nurses can ask, “Where’s the updated infection control protocol?” and get an instant link. Support response times decreased by 40% in a matter of weeks, and no extra hires were needed.

Healthcare AI Agent for Claims and Billing Support

Hospitals lose a lot of time and money to billing errors, delays, and claim denials. AI agents can change that by checking documents, matching codes, noticing issues, and making the revenue cycle faster and more predictable.

One of our clients, a small dental clinic, wanted to fix its billing process. We built them a custom AI that reviewed each claim before it went out. It double-checked the documents, matched ICD-10 codes, and checked if everything lined up with the insurer’s rules. A year in, their finance team reported a 23% drop in denials, and they were spending way less time on manual fixes.

Want to achieve similar results for your clinic or hospital? We’re up for a chat.

Case Snapshot: AI Agent Healthcare Workflow Savings

Here’s a quick look at how AI agents help clinicians save money. The numbers come from our real cases—not estimates or averages—and we’ve put them into a table for your convenience. 

Common Pitfalls When Deploying AI Agents for Healthcare

AI in healthcare has a lot to offer, but a huge number of projects don’t even make it past the pilot stage. And it’s not because the model itself fails—it’s because too many other things go wrong. As they say, forewarned is forearmed, so here are some common places where you can find pitfalls.

• Starting with a Chatbot UI, Ignoring Integrations. A chatbot seems like an obvious first step. It’s visible and often comes ready-made. But unless it connects to your systems—like the EHR, scheduling, and billing—it’s just a slick interface. A bot saying “your lab results are ready” isn’t helpful if it can’t pull that result, check who’s asking, or flag something strange to a real person.
• No Retraining, No Feedback Loop. Things change fast in healthcare—rules, workflows, paperwork—and the AI needs to keep up. If no one is retraining it or logging the weird edge cases, it slowly gets worse. Errors start showing. People stop trusting it. And then it just sits unused.
• Neglecting Security and Compliance Aspects. Too many projects start with ready-made software that wasn’t designed with PHI in mind. They look fine until the legal gets involved or someone notices noncompliance with HIPAA, GDPR, or local population health data protection laws. Things like role-based access, audit logs, and encryption—none of it should be an afterthought.
• Shallow AI Breaks Under Pressure. Surface-level AI does great in a clean demo, but real hospital life isn’t that ideal. If an agent can check for appointment slots but can’t figure out what to do when insurance is wrong or a provider’s not available, it’s not helping. Now, someone has to fix what the bot couldn’t handle. A real AI agent should know when it’s out of its depth and pass things off to a human without making more work.

You can have the best NLP, but if your system can’t trigger the right downstream actions, adapt to local protocols, learn from edge cases, or, simply put, deal with how unpredictable hospitals are – it won’t last. You have to build it into the real day-to-day, where things change, decisions aren’t black and white, and where no two ‘routine cases’ are the same.

— explains a senior AI engineer at Inoxoft. 

Why Choose Us to Deliver Your Healthcare AI Agent

Building AI agents sounds expensive and complicated—and honestly, it can be if you don’t have the right team by your side. That’s why a lot of companies hesitate to even start. But we’ve figured out how to dispel your doubts. Here’s why you might enjoy working with us:

• We’ve already launched 8+ AI agents in healthcare and other heavily regulated industries. And we’re not starting from scratch each time—we’ve got pre-trained modules that fit perfectly into real healthcare workflows.
• You don’t have to wait months to see results. We usually go live in 2 to 4 weeks, not 6 months like the industry average.
• Our company works with HIPAA-aware, ISO 27001 infrastructure, and all our AI/ML engineers are certified to ensure the highest level of security and compliance.
• Our architecture scales easily and cuts implementation costs by up to 3x.

We’ve also got 10 years under our belt, 170+ specialists, and over 230 projects delivered. Our 5/5 Clutch rating reflects the quality our clients count on.

Schedule a free consultation, and let’s see if you’d want to build something together.

To Sum Up

From 2020 to 2023, the AI healthcare market grew by over 230%, which is strong evidence of how promising AI technology is for healthcare. AI agents augment human expertise, improve patient interactions, and let professionals focus on better care. The future development of the technology will allow it to automate even more healthcare operations, so don’t wait—start investing in your success now.

We have developed 8 AI agents for healthcare and other high-priority industries, earning the trust of clients worldwide. If you want to work with a team that knows its stuff, doesn’t overcomplicate things, and supports you at every stage for better results, contact us.