CareConnect

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  CareConnect Homepage

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  Medical Documents

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  Snowflake_Chunks_Table

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  Demo_output1

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  Demo_output2

Inspiration

Access to quality healthcare is a global challenge, with half the population lacking essential health services. Overcrowded systems, delayed diagnoses, and poor-quality care result in millions of preventable deaths annually. CareConnect was inspired by the need to bridge this gap by bringing healthcare closer to patients and supporting doctors in making faster, more accurate decisions.

What it does

CareConnect processes medical data by storing it in a Snowflake database, where documents are divided into manageable chunks using LangChain. A hybrid search system powered by Cortex Search enables low-latency, high-quality retrieval of relevant information by combining vector and keyword-based search, ensuring precise and contextualized responses. Users interact through a Streamlit interface to upload prescriptions or ask health-related questions, with relevant data extracted and matched from the database. This information is then processed by the Mistral AI model to generate accurate, context-aware responses, streamlining diagnosis and decision-making in healthcare.

How we built it

1. Frontend: Built with Streamlit for a user-friendly interface.
   
2. Backend: Developed in Python, integrating with Snowflake for data storage and Cortex Search processing and communication with Streamlit.
   
3. Core Components: Utilized LangChain for document processing, Snowflake Cortex Search for hybrid search, and Mistral AI for generating context-aware responses.
   

Challenges we ran into

1. Processing large documents efficiently.
   
2. Optimizing similarity searches for faster response times.
   
3. Managing version issues during deployment.
   

Accomplishments that we're proud of

1. Successfully implemented a hybrid search system with Cortex Search for more accurate results.
   
2. Addressed and resolved the above-mentioned challenges through thorough research, making the app highly reliable and efficient.
   

What we learned

1. The importance of combining vector-based and keyword-based search using Snowflake Cortex Search for more accurate and contextually relevant results.
   
2. Technically, we have learned skills in data management with Snowflake, hybrid search optimization using Cortex Search, AI integration, and full-stack development with Python and Streamlit.
   

What's next for CareConnect

1. Multilingual Support: To make the system accessible globally.
   
2. Voice Inputs: To enhance user interaction and accessibility.
   
3. Data Storage Expansion: To include more comprehensive medical knowledge, improving the system’s overall effectiveness.
   

Built With

• cortexsearch
• llm
• mistralai
• python
• rag
• snowflake
• streamlit