A Guide to Developing an AI-Powered Virtual Heath Assistant: Insights from Our Recent Project

TL;DR 

• AI could save the U.S. healthcare system up to $150 billion annually by taking over repetitive administrative tasks.
• We created an AI assistant that saves our client $500,000 annually, following these 8 steps: gathering requirements, UI/UX design planning, system architecture, applying NLP, AI models development, interface development, testing and validation, and deployment. 
• Challenges we faced: integrating with the EHR system, ensuring accurate medical advice, and protecting patient data. 
• By 2030, AI chatbots will: make healthcare more accessible and inclusive by creating smaller healthcare centers connected digitally; let healthcare systems predict when someone is at risk of chronic diseases; help reduce wait times, make work easier for doctors, and take care of administrative tasks.

AI Virtual Assistant In Healthcare Use Cases:

• Buoy Health: By December 2019, over 8 million people had used the chatbot, and it handled a new interaction every 13 seconds. A study in JAMA found that using the chatbot reduced patient uncertainty about their health concerns from 34% to 21%.
• LYNA by Google: In comparative studies, it showed higher accuracy than human pathologists. It was able to distinguish slides with or without metastatic cancer 99% of the time and pinpoint suspicious regions.

• Molly by Sense.ly: Patients using Molly are 25% less likely to end up back in the hospital, meaning better condition management and fewer emergencies.

Success Story: How Our Client Saves $500,000 Annually After AI Health Assistant Implementation 

Managing a network of hospitals, clinics, and outpatient centers is no easy task, especially with thousands of calls and messages every day—people asking about symptoms, booking appointments, refilling prescriptions, and so on. 

That was the reality for our client, a large European healthcare provider (whose name we can’t disclose due to our NDA agreement) with a diverse group of clients. And, of course, long wait times had patients frustrated and staff overwhelmed. Something needed to change – and that’s when they came to us. 

Client’s Challenges

• Patients waited too long for help because our client didn’t have the resources to offer 24/7 support. 
• Staff were running around trying to juggle too many things at once, which meant patient care wasn’t always the priority. 
• Our client also had trouble connecting new solutions to its existing electronic health record (EHR) systems.

Features of the Virtual Health Assistant We Built

After analyzing all the issues and brainstorming possible solutions, we suggested creating an AI-powered assistant to improve our client’s patient communication and reduce their overall workload. Here’s how it works:

• 24/7 Support: From scheduling appointments to answering health questions, the AI assistant is available anytime—day or night. It uses natural language processing to provide answers that feel like speaking to a real human.
• Personalized Health Advice: The assistant takes each patient’s medical history into account to provide advice specific to them.
• EHR Integration: It connects with our client’s EHR system, ensuring it’s always up-to-date with the latest patient information.
• Security and Compliance: The solution is HIPAA-compliant, so everything stays safe and secure.
• Multichannel Support: Patients can reach the assistant via web chat, a mobile app, or even voice assistants.

Business Outcomes

And the results? They spoke for themselves:

• 70% Shorter Wait Times: Patients now get answers much faster, which obviously means better satisfaction all around.
• 40% Reduced Staff Workload: Our virtual health assistant takes care of routine tasks, leaving staff free to focus on what really matters—actual patient care.
• $500,000 in Annual Savings: With AI handling a lot of the work, they didn’t need to hire as many extra staff, saving half a million every year.

The AI assistant can make both your patients and employees happier. Don’t waste any time—start your project today and see real improvements tomorrow! We’re here to help.

Developing Your Virtual Health Assistant in 8 Steps: With Insights From the Implemented Project

Let’s imagine a doctor sitting with a patient, working to make the right diagnosis, recommend the best treatment, and offer tailored health advice. To do that, they need to study piles of medical records, test results, and other data—a time-consuming and exhausting task that often gets in the way of truly personalized care.  

Now, imagine the same scenario, but with an AI-powered app in the picture. Instead of spending hours buried in information, AI processes tonnes of data in seconds. It finds patterns and insights that people usually miss. Even better, it can detect potential health issues early, giving doctors a chance to take action before problems get worse!

In a recent TEDx Talk, Dr. Edmund Jackson told a powerful story that shows how this works:

“I found myself in the office of a singular individual, Dr. John Perlin. At the time, he was the Chief Medical Officer for a hospital company called HCA Healthcare, where I was applying for a job as a data scientist. I said to him “Dr. Perlin, what is it that you’d have me, a data guy, do for you?” And he proceeded to tell me about a disease called sepsis. It’s a systemic bloodstream infection that, if identified early, can be very readily treated with about $0.25 worth of antibiotics and fluid. But if it goes unrecognized, it spreads through the body, leading to systemic inflammation, organ failure, and all too frequently, death. Doctor Perlin proceeded to tell me at length about the etiology, biochemistry, and pharmacology of this disease. And I understood none of what he was telling me. And I said “John, I really don’t understand,  I’m not that kind of doctor. I’m a doctor of numbers.” And he said: “I understand. I tell you all this to convince you that as clinicians, we’ve tried everything we can do in the fight against sepsis, and we are still losing. Our care simply does not arrive in time to be relevant. We need an early warning of sepsis. We believe it’s in the data, and that you and your fellow data scientists can find it.”

To help you figure out how an AI medical assistant can improve your productivity and the quality of your services, we’ve broken down the development process and included expert insights from our team. Keep in mind that every company has its own challenges, so there isn’t a single solution that works for everyone.

Step #1: Gathering Requirements

First things first, we talked with the people who know the system best: medical staff, admin teams, and patients: 

• Doctors shared how their days were full of distracting questions—empty health concerns, prescription refills, follow-ups, and more. They couldn’t concentrate on one task and were always in a hurry, feeling completely exhausted and mentally drained.
• Patients, on the other hand, told us how hard it was to get a moment of exclusive attention from hospital workers and how they often felt neglected. Long waits, double bookings, and errors added to the problem.

“When we started, it was all about asking the right questions,” says the business analysts at Inoxoft. “We didn’t just sit there with a checklist. We dug into every detail, making sure we understood not just what people were saying, but what they needed. That’s how we map out solutions that actually work in real-life scenarios.”

If your current processes also feel like a bottleneck, let’s chat. We offer a free consultation to explore the best ways to transform your workflow.

Step #2: UI/UX Design Planning

After writing down the challenges, we moved to the drawing board—literally. Wireframes and low-fidelity mockups became the starting point, helping us share ideas and gather feedback from stakeholders early in the process. With these bare-bones sketches, we locked down the layout and user flow before diving deeper.

Once we had the green light on the structure, we got to work on high-fidelity mockups. This stage brought the design to life, layering in typography, colors, and imagery to create a look and feel that matched the system’s goals.

Interactive prototypes were next. Think clickable designs that mimic real-world interactions. We tested these thoroughly to fine-tune usability and ensure everything flowed smoothly for the end user.

Step #3: System Architecture

When it came to building the system, we took a microservices approach. Why? Because breaking the system into smaller, independent components makes it easier to develop, deploy, and maintain without creating a tangled mess.

To connect with existing EHR systems, we built custom APIs that could seamlessly pull in patient data. It wasn’t a one-and-done effort—this part required several rounds of testing to make sure every integration was rock-solid.

Of course, security wasn’t an afterthought. From encrypting data to setting up robust user authentication, every step was designed to safeguard patient information and tick every box for HIPAA compliance.

“Microservices architecture gives businesses a major edge. It’s scalable, flexible, and lets you adapt quickly without overhauling the entire system. For a setup like this, it’s a game-changer.”

– explained the project’s team lead.

Step #4: Applying NLP

During this step, we were teaching the client’s virtual healthcare assistant to understand human language—no small feat. Using Natural Language Processing (NLP), we tackled everything from text to voice data, helping the system grasp what patients were really asking for.

Breaking down language, we used techniques like tokenization (splitting sentences into digestible chunks), contextual understanding (reading between the lines), and sentiment analysis (picking up on emotions). Our goal was to give AI the smarts to deliver accurate, relevant, and even empathetic responses.

“NLP is where the magic happens,” explains our AI lead. “It’s not just about understanding words; it’s about understanding meaning. That’s where techniques like context modeling and sentiment analysis come into play—it’s what makes the system feel human.”

Step #5: AI Models Development

Here’s where the real brainpower kicked in. We built AI models to analyze mountains of medical data and deliver personalized advice based on each patient’s history. This required us to untangle complex medical records, lab results, and other health details to create something truly smart and practical.

We built models that could accurately predict and classify medical information using deep learning techniques like neural networks. But we didn’t stop there.

“We used regression models to predict outcomes like recovery times and treatment durations. Clustering algorithms helped us group patients with similar traits to find patterns and offer targeted recommendations. It was about creating a system that sees the bigger picture in patient care.”

– shared the project’s team lead.

Step #6: Interface Development 

A good system needs to feel natural, so we built an interface that patients could use on web chat, mobile apps, or even voice assistants. Whether they were at home, on the go, or waiting in a clinic, they could access it anytime, anywhere.

And we didn’t just guess what users would like. Our team tested it out with real people to ensure the interface wasn’t just functional but genuinely easy and intuitive.

Step #7: Testing and Validation

Before rolling it out, we gave every component a thorough checkup – each component individually and as part of the system to catch and resolve errors early. Real users joined the testing process to confirm that the system functioned as expected.

For accuracy and safety, we collaborated with medical experts. Every step focused on delivering a system that people could trust to handle their health-related concerns.

Step #8: Deployment

When it was time to go live, we had everything ready. Our developers transferred the files, databases, and other components with automated scripts to minimize risks.

Next came a series of tests—from smoke testing to integration and user acceptance testing—to iron out any last-minute issues. We also set up monitoring tools and alerts to keep an eye on performance and quickly flag any problems.

At Inoxoft, we solve real business challenges and focus on results that matter.  If you’re curious about how we can bring fresh ideas and smart solutions to your business, contact us. Our free consultation is a great place to start exploring your possibilities.

Challenges We Faced

No project is without its challenges, and this one was no exception. While working, we faced a few hurdles that asked for creative thinking and teamwork to overcome. Here’s a breakdown, so you can learn from our mistakes:

1. Integrating with the existing Electronic Health Record (EHR) system was challenging because these systems have complex APIs and require precise configurations for secure data access. We worked closely with the client’s team to develop custom APIs and conducted multiple tests to ensure smooth and reliable integration.
2. Another critical area was the accuracy of medical advice. The system needed to provide safe and reliable recommendations based on the latest medical research. To achieve this, we worked with medical experts to build and validate machine learning models and regularly updated the knowledge base with current data.
3. Finally, from day one, we focused on meeting regulatory requirements like HIPAA. We implemented strong encryption, user authentication, and regular security audits to ensure compliance and protect sensitive information.

Expert insight for the project’s team lead: 

“Challenges are just part of the process. Instead of trying to dodge them, we dive in and figure things out.  By staying flexible, collaborating closely, and always keeping the end goal in mind, we turn obstacles into stepping stones toward a better solution.”

How Leading Companies Implement AI Health Assistants

Chatbots and online assistants aren’t new, but AI has taken them to the next level. Instead of just following scripts, they now deliver personalized responses—something that’s vital when it comes to caring for patients. 

We’ve gathered some of the best-known examples of AI virtual assistants in the healthcare industry, so you can better understand how they work in real-life settings.

Buoy Health

How do hospitals reduce unnecessary ER visits while keeping patients well-guided? Buoy Health offers an answer with its AI-powered chatbot. Partnering with major health systems like Boston Children’s Hospital, this AI gives patients quick, reliable advice.

How It Works

At its core, the Buoy Health chatbot uses advanced machine learning algorithms to act as a digital health assistant. Here’s what makes it stand out:

• Symptom Assessment: Patients describe their symptoms, and the chatbot asks questions to gather critical details.
• Intelligent Recommendations: Using patient-provided data, the system suggests the next steps—whether it’s home care or seeking urgent medical attention.

Clinical Impact

Buoy isn’t just a symptom checker—it’s a tool that helps patients take charge of their health while easing pressure on healthcare systems:

• Reducing ER Overload: By December 2019, over 8 million people had used Buoy Health, with the chatbot handling a new interaction every 13 seconds. A study in JAMA found that using the chatbot reduced patient uncertainty about their health concerns from 34% to 21%.
• Better Patient Engagement: The conversational format encourages users to follow through with recommended care plans. And engaged patients are more likely to adhere to treatments.
• Accessible Care for All: Buoy Health makes healthcare more inclusive and offers a simple and convenient way to seek guidance even in remote locations. 

Buoy Health demonstrates that technology is a catalyst for smarter, more efficient, and patient-focused care.

LYNA by Google Health

Cancer detection can be challenging, but LYNA, Google Health’s AI tool, is changing the game. Designed to help pathologists analyze lymph node biopsies, LYNA uses machine learning to find cancer cells with stunning accuracy.

How It Works 

LYNA processes biopsy images to spot cancerous cells, handling the initial screening so pathologists can focus on the trickier cases. It’s especially good at finding very small cancer clusters that human eyes might miss.

Why It Matters

In comparative studies, LYNA demonstrated higher accuracy than human pathologists, especially in detecting small metastases. It was able to distinguish slides with or without metastatic cancer 99% of the time and pinpoint suspicious regions, including those too small for consistent human detection.

Google explained:

“LYNA was able to accurately pinpoint the location of both cancers and other suspicious regions within each slide, some of which were too small to be consistently detected by pathologists. As such, we reasoned that one potential benefit of LYNA could be to highlight these areas of concern for pathologists to review and determine the final diagnosis.”

Clinical Impact

• Fewer Errors: LYNA helps ensure fewer misdiagnoses, so patients get the right treatment faster.
• Lower Costs: Avoiding unnecessary tests or treatments saves money and stress.
• Better Use of Time: Pathologists can focus on complex cases instead of routine screenings, speeding up results.

LYNA proved the transformative potential of AI in healthcare, helping pathologists focus on saving lives while ensuring every patient receives precise care.

Molly by Sensely

Managing chronic conditions needs consistent support, timely reminders, and personalized care. Molly, Sensely’s AI-powered nursing assistant, provides all that and more.

How It Works 

Molly’s interactive design is built around patient needs:

• Interactive Engagement: Through natural, conversational interactions, Molly builds trust and helps patients stick to treatment plans.
• Timely Reminders: Regular notifications encourage patients to take medications on schedule.
• Continuous Monitoring: Tracking health metrics such as vital signs and symptoms allows Molly to detect early signs of deterioration.
• Tailored Advice: Each interaction provides guidance, ensuring care aligns with the unique needs of each patient.

Clinical Impact

Molly is making a real difference:

• Fewer Hospital Visits: Patients using Molly are 25% less likely to end up back in the hospital, meaning better condition management and fewer emergencies.
• Sticking to Medications: Molly’s reminders help people stay on top of their meds, which is key to managing chronic illnesses.
• A Better Experience: Having Molly’s support anytime makes patients feel more confident and connected to their care.

Molly shows how AI can take the stress out of managing chronic conditions, giving patients the support they need to live healthier lives.

How AI Health Assistants Will Change The Way Healthcare Companies Operate

We came across an article from the World Economic Forum that explores the future of AI in healthcare, and we thought it would be great to share some insights with you. Here’s a look at some key trends:

Making Healthcare More Accessible and Inclusive

Many experts predict that by 2030, healthcare will be more spread out – and the reason for that is AI. Instead of just big hospitals, we’ll have smaller centers like retail clinics and home care, all connected digitally. 

These networks will use AI to predict health issues early, spot system bottlenecks, and make sure patients get the right care at the right time. The focus won’t be on location anymore but on improving people’s experiences across the whole system.

Predictive Healthcare for Proactive Disease Prevention

Healthcare produces enormous amounts of data—about 30% of the global total, according to some estimates. This creates opportunities to improve care in ways that weren’t possible before. One of our specialists shared: 

“As the saying goes, prevention is better than cure. But so many variables affect the development of illnesses—climate, mobility, population density, lifestyle—and the list goes on. A single person simply can’t analyze all these factors for one patient. But AI can and does.” 

We believe that in the future AI will let healthcare systems better predict when someone is at risk of chronic diseases and offer ways to prevent them before they get worse. For example, diseases like diabetes and heart issues, influenced by these factors, are expected to decline thanks to these predictions.

Optimizing Patient Experience

The experience of healthcare matters, whether you’re a patient or a provider. As our business analyst shares: 

“Even in developed countries, people often don’t get treatment when they need it,” one of our experts noted. “AI can help hospitals manage their time better, so both patients and staff can focus on what’s important.”

AI chatbots will further help reduce wait times, make work easier for doctors, and take care of administrative tasks. Smart systems will also learn from each patient and procedure, making healthcare better for everyone, helping prevent doctor shortages, and creating a system that keeps people healthy for life.

Let Us Help You Improve Your Medical Operations with AI-Powered Health Assistants

With over 230 projects completed, our team knows how to prioritize what matters most—your patients. Since 73% of people say a great experience keeps them loyal, we build chatbots that make every interaction feel personal.

When partnering with us, you’re choosing a team that uses cutting-edge AI and natural language processing to create smart, efficient, and future-ready chatbots. Our expertise helps clients see increased engagement and revenue. 

We offer a wide range of chatbot solutions tailored to your needs:

• GPT-Based Chatbots: Handle complex customer queries, provide recommendations, and engage users in human-like conversations.
• AI Voice Assistants: Automate voice-driven tasks, schedule appointments, and improve accessibility for users.
• Transactional Chatbots: Streamline purchases, payments, and order tracking to boost user experience and revenue.
• Social Media Chatbots: Enhance your presence on social platforms with automated, real-time interactions.
• Personal Assistants: Simplify routine tasks and create seamless user experiences.
• Entertainment Chatbots: Provide engaging content and interactions to captivate your audience.

If you want to learn more about our work, experience, and processes, take a look at our AI virtual assistant in healthcare case study.

Want to know how to start your chatbot project? Reach out to us for expert guidance and a reliable partner for your journey!

Wrapping Up

AI assistants are a powerful tool for solving a range of challenges. In healthcare, they can reduce the workload on staff, improve patient support, analyze data, and streamline operations. The key to success is understanding your goals and working with an experienced team to bring them to life.

If you’re exploring how to create an AI assistant or need a trusted partner, we’re here for you. Our team builds AI solutions designed to tackle real-world problems, so you can focus on what matters most.

Share your vision, and let us take care of the technical side.