AI-Powered Healthcare Chatbot for Women’s Wellness App

Our client is a Norwegian telecom company that serves small and medium-sized businesses across Europe. They provide mobile services, internet, and business packages that keep teams connected and online.

Our client is a Polish healthcare startup focused on making medical advice more accessible to women. They recognized that many women struggle to find trustworthy, personalized health information online and often hesitate to reach out to doctors for routine concerns.

The client came to us with a vision: create a mobile app where women could ask health-related questions and receive personalized, reliable advice based on verified medical content instantly and privately.

team

Our Side

Project Manager

2 Full-Stack Developers

(React Native + Node.js)

Machine Learning Engineer

Client’s Side

CEO

DevOps Developer

QA Engineer

Project Manager

technology

React Native

Node.js

OpenAI GPT-4

RAG (Retrieval Augmented Generation) module

Client’s brief

The client reached out with an idea to build a mobile chatbot that could provide women with accurate, personalized healthcare advice. The key requirement was that answers needed to be based on verified medical articles instead of generic information pulled from the internet.

They also wanted the system to understand each user’s unique health profile. When a woman asked about managing stress, for example, the chatbot should consider her age, medical history, current medications, and lifestyle factors before responding.

Most importantly, the solution needed to prioritize safety. If the chatbot didn’t have enough information to answer accurately, it should admit that openly rather than risk giving incorrect medical advice.

Challenges

Handling Sensitive Medical Data
We were working with highly personal health information such as chronic conditions, medications, mental health history. This required strict data protection measures and full anonymization to ensure user privacy and comply with healthcare regulations.

Building Trust Through Transparency
Users needed to trust the chatbot with intimate health concerns. The system had to be transparent about its limitations and never pretend to know something it didn’t. This was especially challenging given how AI models can sometimes generate confident-sounding but incorrect responses.

Making Advice Truly Personal
Generic health advice isn’t helpful and can even be harmful. The chatbot needed to factor in each user’s complete medical profile with every response, while searching through hundreds of medical articles to find the most relevant information.

Working with Emerging Technology
RAG technology was relatively new, and we needed to implement it in a way that was both technically sound and ethically responsible for a healthcare application.

Solutions

Comprehensive User Onboarding

We designed a detailed onboarding questionnaire that collects essential medical information: age, weight, chronic conditions, medications, lifestyle factors, and health goals. This creates a complete health profile that the system references with every question. The questionnaire is carefully structured to gather necessary information while respecting user privacy.

RAG Implementation for Contextual Accuracy

We implemented Retrieval Augmented Generation (RAG) technology, which works by combining the user’s question with relevant medical articles before generating a response. Instead of relying solely on the AI’s training data, the system searches through verified medical content to find the most accurate information for each specific query. This significantly reduces the risk of incorrect or made-up answers.

Personalized Context with Every Response

The chatbot remembers what users told it during onboarding and actively uses this information with every interaction. When a user asks about managing insomnia, the system considers their current medications, stress levels, exercise habits, and any relevant medical conditions before crafting its response.

Data Security and Anonymization

All personal health data is encrypted and fully anonymized. We implemented secure data handling protocols that ensure user information remains private and compliant with healthcare data protection requirements.

Medical Content Integration

We worked closely with the client’s medical content specialist to structure and integrate verified medical articles into the system. This created a reliable knowledge base that the chatbot draws from when answering questions.