Healthcare Chatbot Development: Use Cases, Compliance, and Best Practices for 2025

According to Grand View Research, the global healthcare chatbots market size was estimated at $787.1 million in 2022 and is projected to grow at a CAGR of 23.9% by 2030. Chatbots turned out to be effective for appointment scheduling, patient communication, and many other use cases. That’s why more and more healthcare providers implement them into their services. But what’s the big catch? 

There are still many risks in using AI chatbots in the healthcare industry. Right now, the biggest issues are related to regulations such as HIPAA compliance, data privacy, and information accuracy, etc. However, these challenges can be mitigated when working with an experienced healthcare software development team.

MobiDev has 15+ years of practical experience in healthcare app development, including creating HIPAA-compliant and effective chatbots for the healthcare industry. In this article, you’ll find insights based on our team’s professional background to learn how to build a medical chatbot and avoid all pitfalls.

Key Use Cases and Benefits of Chatbots in Healthcare

Although medical chatbots have multiple use cases, we can categorize them into patient-facing and hospital-facing solutions based on their features. Both options address different tasks and challenges, bringing benefits to their target audience. Understanding which option suits your needs best is essential to developing the right solution for your business.

Patient-Facing Applications

Patient-facing applications provide patients with various features that simplify their interactions with healthcare providers. These include:

• Appointment scheduling: used to book, reschedule, and cancel appointments remotely with further reminders and reports
• Symptom assessment: used to provide preliminary guidance and help patients understand which doctor to contact
• Health education: used to deliver accurate information on medical conditions, treatments, and preventive care. These are essential to help patients stay healthy in any situation.
• 24/7 support: a generalized chatbot that may include all the aforementioned use cases. It is used to answer questions, provide guidance, and help patients access health-related information.

Hospital-Facing Applications

Hospital-facing applications support healthcare providers by accelerating their workflows and automating repetitive tasks. These include:

• Automated recruitment: used to automate initial screening, schedule interviews, and answer candidates’ questions regarding the positions. This reduces the workload on HR teams and accelerates hiring timelines while maintaining consistency in communication.
• Medical research: used to help researchers organize, retrieve, and process large volumes of scientific data. They can provide summaries of research papers, guide users through databases, and suggest relevant studies for better health treatment.
• Workflow automation: used to automate data entry, report generation, and document filling. This allows staff to reduce human error and significantly speed up their work process.
• Support and onboarding: can act as virtual AI assistants for managers helping to monitor staff performance metrics, generate reports, and provide reminders about their tasks. This can also help new team members get detailed guidance and understand workplace policies.

Types of Healthcare Chatbots

There are various categories of chatbots, each designed with unique capabilities depending on the technology and programming involved. We can classify them into two big categories with several subcategories. Let’s take a closer look.

Based on the Type of Interaction

This is the simplest way to categorize a medical chatbot:

1. Text-based chatbots –  use NLP and written language to communicate with users, answer questions, and complete basic tasks
2. Voice-based chatbots – use speech recognition and TTS to interpret spoken commands and provide audio responses

Both options can usually be multilingual, applying various languages and dialects depending on the healthcare provider’s settings. 

Based on Technology

This approach is more complex as it involves three possible options.

1. The first option includes traditional chatbots:

• Menu-based – use predefined buttons to guide users without understanding natural language, following a strict script without deviations. For example, the chatbot can help patients schedule appointments by using menus like “Choose doctor” and “Choose a date”.
• Rule-based – follow the “if-then” logic to answer to specific scenarios. For instance, when users type certain predefined commands, the chatbot can provide information on visiting hours.
• Keyword-based – analyzes keywords in user messages to choose the best response. For example, if a user types “What are my latest test results?”, the chatbot will determine the keyword “test results” and provide relevant data.

2. The second option includes AI-powered chatbots:

• Contextual – rely on machine learning and natural language processing to understand both intent and context. Such bots can use follow-up questions to clarify the user’s request, providing a relevant answer in a conversation that feels real.
• Generative – usually GPT-based chatbots that can create human-like responses and maintain an open conversation with users. It’s more human-like and can adapt on the go, using its data from the uploaded knowledge base.

AI-powered chatbots also allow you to seamlessly transfer the conversation to a human support agent. This is needed when the conversation gets “stuck” and the chatbot can’t help the user. The agent can see the conversation history and provide qualified assistance.

3. The third option includes hybrid chatbots. It’s a combination of rule-based and AI-powered assistants. You can apply them to provide users with predefined menus while adding personalized health tips based on their interaction history.

How to Build a Medical Chatbot Step-by-Step

Although healthcare chatbot development may seem like a challenging task and can look a little different for each specific case, here is a general flow that will help you understand the key milestones.

1. Define Objectives and Scope

Before you start creating anything, set clear objectives to understand your needs and the project’s scope. This will help you plan everything and avoid spending extra resources. 

Start by asking yourself the following questions:

• What problems should the chatbot solve?
• Who is the target audience?
• What are the KPI metrics to consider?

You must clearly define whether you’re going to make a patient-facing or hospital-facing solution. This could even involve both sides, although the complexity would increase significantly. Once you answer these questions, you can move on to the next step.

2. Choose the Right Use Case and Tech Strategy

Now you have to understand how people will use the chatbot. This can include a variety of use cases:

• Appointment scheduling
• Medication reminders
• Providing general medical information
• Billing, and others

It’s possible to include a mix of use cases to cover multiple needs. Everything depends on your goals.

When it comes to your tech strategy, you’ll have to consider:

• Possible integrations
• HIPAA and GDPR compliance
• Scalability and adaptability

You’ll have to clearly decide between traditional, AI-based, and hybrid chatbots. This affects the time and costs to create your solution, especially considering the need for HIPAA-compliance forAI.

3. Design a User-Centric Interface

A user-centric interface is necessary to provide a high-quality experience to all your target audiences. Many factors will depend on the specific target audience you chose in step #1. However, you should still consider these generalized points that apply to all users:

• Accessibility features: voice commands, screen readers, and high-contrast modes to support users with disabilities
• Multi-language support: implement an extra language if your region doesn’t have a single used language (for example, English and Spanish are a good pick in the USA)
• Simplicity and clarity: ensure users need to take minimum steps to complete their tasks

At this stage, it’s a standard practice to invite real patients and healthcare staff to test the solution and share feedback. You can further implement A/B testing during deployment to understand which design elements are the most effective. 

4. Select Appropriate Technology

Your tech stack determines the chatbot’s capabilities and scalability. That’s why it’s necessary to choose the right development platform, AI and ML algorithms, and consider integration capabilities with your healthcare systems. You need a tech stack that allows you to comply with HIPAA and other regulations.

Here are some different platforms and technologies that can be used to create healthcare chatbots. The choice depends on your specific case and project requirements. 

*Where 1 – easy, 5 – complicated

You should also remember about the infrastructure you need to run your chatbot. Although cloud-based solutions like AWS and Google Cloud provide a high degree of scalability and simplified maintenance, on-premises infrastructure is always better for data control. However, this comes with increased costs and a need for ongoing support.

5. Ensure Data Management and Security

Working in the healthcare industry requires you to make both traditional and AI chatbots with key healthcare data security practices to avoid violations and penalties. The most important elements to consider are:

• PHI anonymization – remove PHI data when processing
• Business associate agreements – must be signed by all third-party providers
• Self-hosted LLMs – provide maximum control over data
• SOC-2, MFA, RBA – key security elements in all solutions
• Audit logging – maintain logs of data access for accountability
• Remove PHI from push notifications – information must only be accessed via relevant logins

Read also:

Building HIPAA-Compliant Software: Best Practices for Healthcare Providers

6. Integrate with Healthcare Systems

Depending on your chatbot’s functionality, you need to integrate it with relevant systems and databases within your healthcare organization. This will let your solution provide users with personalized, accurate, and relevant responses based on real-time data. 

If your existing healthcare system is a legacy one, consider necessary updates to support the necessary APIs or data formats used in your chatbot. Modernized systems can better accommodate growth and adapt to changing healthcare needs. Having your system up-to-date ensures that the chatbot can scale effectively as demand increases.

Read also:

Healthcare Software Modernization: Practical Guide for Upgrading Outdated Medical Systems

7. Test and Validate

Conduct manual and automated testing to locate usability issues, improve functionality, and prevent security breaches. You can involve real users and healthcare staff in the testing process to validate your chatbot’s responses. This is a great choice to understand whether the solution is designed appropriately. You can read more on how to test an AI chatbot.

8. Deploy and Monitor

Deploy the solution into a controlled environment for additional testing to ensure everything works as intended. Fix any issues that appear and roll out the chatbot into a real-world environment. Monitor performance through response accuracy, system uptime, and user engagement. If everything is done correctly, you’ll only have to maintain the solution and prepare updates based on your needs. 

Addressing Challenges in Healthcare Chatbot Development

Healthcare chatbot development can involve multiple challenges that must be considered to avoid penalties and breaches.

• Data privacy & security. Assess the regulations you need to comply with based on the data you are planning to use. This can involve HIPAA and GDPR, along with their regular updates, to protect PHI from all kinds of vulnerabilities.

• Integration with existing systems. If your organization uses outdated infrastructure and systems, you might have to modernize them first. 

• Handling complex and sensitive interactions. Chatbots must be able to connect users with a human agent whenever required.

• Accuracy and limitation. It’s necessary to provide users with accurate information to avoid harmful outcomes. Chatbots aren’t a good idea for diagnostics, for example. They can provide patients with a general overview of their symptoms and suggest booking an appointment with the right healthcare specialist. 

• Hallucinations. LLM models are subject to so-called hallucinations when they can broadcast false information. To prevent this, Retrieval-Augmented Generation (RAG) architecture is used. It improves the accuracy of language model outputs by integrating external databases or knowledge sources, which provides a factual basis for the model’s responses.

If you want to create a successful chatbot that supports your brand instead of hurting it, then you must consider all these challenges in your tech strategy.

Best Practices for Medical Chatbot Development

Our engineers use both their experience and the industry’s best practices when creating medical chatbots for healthcare organizations. These are some of the most important components you should remember when developing a chatbot for your healthcare business. 

1. Human Escalation

While AI seems to be getting smarter and smarter each day, it’s still not a living being. Artificial intelligence in healthcare can’t solve all our problems, so it’s necessary to implement a possibility to contact a human agent whenever required. You typically have two options:

• Automate the redirection
• Provide a clear “contact support” button

We recommend adding the “contact support” button that would appear when the chatbot is unable to answer the user’s request. Yes, it will increase the load on your support team, but you’ll get a far better user experience, which is much more valuable.

2. Support Customization

Your chatbot should include customization capabilities from the very beginning. It’s similar to scalability – you must be able to apply changes at any moment, whenever needed. This means being able to:

• Tailor responses
• Add custom features like follow-up scheduling, reminders, etc.
• Adapt the design according to the organization’s branding

A custom solution always provides you with all the capabilities to customize and maintain full control over all processes. That’s why it’s the best solution in the long run.

3. Experienced Development Team

Work with developers who are experienced in healthcare, chatbot, and AI development to ensure your solutions meet all needs and regulations. Your engineers must understand how to make the chatbot HIPAA-compliant and prevent all potential breaches. This is the biggest element of your success.

Success Story: Healthcare Chatbot Development for a US-based Medical Company

Since 2017, MobiDev has been providing healthcare software development services to a multi-billion dollar US-based medical company that engaged our team to build a comprehensive web and mobile solution for healthcare management and integrate it into their existing ecosystem. After the launch of the solution, the MobiDev team continued to support the client with new features and platform updates, including the implementation of an AI chatbot that provides 24/7 patient assistance.

Client’s goal:

The client was looking for a chatbot solution that would reduce the call center’s workload and enhance patient-doctor interaction. 

How we delivered:

• Conducted the consulting stage

First of all, our engineers conducted a software audit for an assessment of the infrastructure to identify the best tech stack that met the client’s tech and business goals. This resulted in choosing the combination of Microsoft Azure Bot Framework and Lex for chatbot development. The chosen framework provides great scalability to accommodate large volumes of users and meets HIPAA compliance requirements which was crucial in our case. 

• Integrated AI capabilities

The Microsoft Azure Bot Framework allowed us to easily integrate AI capabilities using Azure Cognitive Services, ready-to-use artificial intelligence APIs. In particular, we integrated the LUIS Service to make the chatbot understand user intents and extract key details from requests. This ensured enhanced user experience through human-like communication.

• Streamlined information delivery

QnA Maker Service was used to create a scalable knowledge base for the chatbot. This allowed users to get quick and accurate answers to frequently asked questions. 

The crucial advantage of QnA Maker Service is that it doesn’t store customer data. When a customer sets up QnA Maker, they choose a specific Azure region for their service deployment. All data associated with that service will be stored in that chosen region. By not storing customer data, QnA Maker helps organizations minimize risks associated with data retention, making it easier to comply with HIPAA regulations.

Outcomes and Achievements: 

The developed chatbot helped the client reduce call center workload by over 15% and saved around $5 million in operational costs within the first year of use.

Read the full success story:

HIPAA-compliant cross-platform healthcare management solution

Build Your HIPAA-Compliant Healthcare Chatbot with MobiDev

MobiDev’s team helped dozens of clients create and maintain HIPAA-compliant software since 2009. With 15+ years of experience, our healthtech experts combine traditional software and AI expertise to deliver solutions that meet your business requirements and comply with regulations. 

Check out our AI healthcare consulting services to learn more about our experience and book a consultation with our experts. You can get all development on our side or collaborate with our experts in staff augmentation mode. Book a call now to get a consultation!