Healthcare Chatbot Using AI/ML

Client’s Demand

The client proposed the idea of building a mental health AI Chatbot with the aim of providing an accessible and scalable solution that provides an interactive means for engaging users in behavioural health interventions driven by Artificial Intelligence on their mobile phones. They needed the chatbot to engage users and identify their stress type. Further, they also wanted the Chatbot to recommend ideas, blogs, podcasts, meditation, and more to help them cope with their mental or emotional state of affairs.

Our Solution

Our developers and designers collaborated to build an AI-powered chatbot based on seven well-defined user interaction use cases. These use cases reflected real-world scenarios and guided the conversational flow, enabling the chatbot to deliver structured, empathetic, and context-aware responses.

Detection and Recommendation Using ML Models

The detection of behavioral patterns, sentiments in the message, stress levels, and mental position paired with suitable recommendations (of podcast, training, tools, resources, etc.) are powered by ML Models for each use-case. These will help users solve their problems, which will mostly be w.r.t Healthcare, Stress, Behavior, etc. For example, the application will conduct an online chat conversation via text and detect the cause of stress so that the system can recommend a solution for the same, eradicating the need for a human agent. 

The algorithm will use the input data and known responses to the data (output), and train a model to generate reasonable predictions for the response to new users and even the existing users that use the app again. 

Therefore, using existing conversation data, the chatbot will understand the type of questions people ask. Using Machine Learning & NLP (Natural Language Processing) to learn context and even perform context analysis, it will analyze correct answers to those questions through a ‘training’ period and continually get better at answering those questions in the future, which will lead to a better response rate.

The basic or the first use case includes

1. Use existing questionnaires to generate an ML model
2. Create an API to parse the conversation data to understand the type of response to the predefined questions. 
3. Analyze and evaluate the answers and generate ratings/points for each user. 
4. Use these points/ratings to recommend the predefined solutions for each use case.