The problem HealthSync solves

The healthcare sector stands as one of the most vital industries globally, tasked with delivering high-quality medical services every day. However, it faces numerous obstacles, including overwhelmed healthcare professionals and inadequate medical resources. The HealthSync platform revolutionizes healthcare delivery by integrating Gen-AI and ML to automate the process of daily medical data collection, analysis, and patient interaction. Here's how:

#Streamlined Patient Management
We allow doctors to efficiently manage their patients by adding them to the system and overseeing their progress. Patients receive login credentials via email, granting them access to their personalized dashboard.

#Personalized Patient Interaction
Patients are greeted by an AI Avatar, powered by Azure Text-To-Speech Service. This 2D photorealistic avatar engages with patients, posing relevant questions based on their initial diagnosis. By utilizing Azure AI Communication and Speech services, patients can communicate via text or speech, enhancing accessibility.

#Continuous Monitoring and Assessment
The chatbot tracks patients' well-being and mental health by periodically prompting them with inquiries tailored to their condition. Automation scripts ensure the seamless execution of these interactions and data collection processes.

#Data-driven Insights
Patient communication is parsed daily and analyzed, with essential information extracted and stored in the database. EDA techniques are applied to derive meaningful insights, empowering doctors with valuable information to make informed decisions.

#Mental Health Assessment
Leveraging ML techniques, a logistic regression model was trained. This model calculates a mental health score based on five different factors, enabling doctors to quantitatively monitor patients' mental well-being over time.

HealthSync represents a paradigm shift in healthcare delivery, harnessing the power of Gen-AI and ML to optimize the doctor-patient relationship.

Challenges we ran into

During the development of the HealthSync platform, one specific bug that we encountered revolved around the integration of Generative AI into our chatbot system. We faced challenges ensuring that the AI responded accurately and contextually in a three-person chat environment, where the patient, the doctor, and the AI interacted simultaneously. The complexity stemmed from the need for the AI to understand and respond appropriately to messages from only the patient, while still having access to the context from other chats. Additionally, ensuring that the AI maintained coherence and relevance in its responses presented a significant challenge.

To overcome this bug, we employed a multi-step approach. First, we created separate automated scheduling processes, which would run at specific times and ask custom-generated questions to each user. These questions were tailored based on the patient's initial diagnosis and previous interactions, ensuring relevance and personalization. This scheduling system allowed us to prompt users with inquiries at predetermined intervals, facilitating continuous monitoring and assessment of their well-being and mental health.

Additionally, we implemented LangChain, a sophisticated natural language processing (NLP) framework, to enhance the contextual understanding of user queries within the AI Chatbot. LangChain integrated contextual data for each user, extracted from the database, to accompany the user query while it was being processed. This contextual information provided valuable context to the AI Chatbot, enabling it to generate more accurate and relevant responses.