Medical Card is a solution designed to provide a comprehensive overview of medical data from the client's perspective. The project aims to centralize medical data from various sources, make it easily understandable, allow clear comparisons over time, identify health trends, and make this data accessible in multiple languages and through an API.
Currently, there is no clear overview of medical data over time from the client's side. Many people, especially in Europe, switch doctors and labs frequently, resulting in fragmented medical data. Additionally, many individuals, including the project initiator's mother, still maintain their health data in paper notebooks. While hospitals have specific health portals to pass information between doctors, these solutions are not accessible to patients and are often limited to specific countries.
- Centralize Data: Aggregate medical data from different sources into one platform.
- Simplify Explanations: Provide clear explanations of medical metrics for better understanding.
- Enable Comparisons: Allow users to make clear comparisons of their medical data over time.
- Identify Trends: Highlight trends in the user's health data.
- Multi-language Support: Make the data accessible in various languages.
- API Access: Provide data accessibility through a well-structured API.
- Time Efficiency: Reduce the time spent by both patients and doctors on simple cases.
- Self-awareness: Empower individuals with a third perspective on their health situation, potentially offering insights different from their doctor’s assessments.
- Travel Convenience: Support data in various languages, beneficial for frequent travelers.
- Future-proofing: Prepare for the advent of AI doctors by centralizing medical data and making it accessible through an API.
- Next.js: Utilized for building the user interface of the application.
- Python: Handles the backend logic and processes.
- Clerk: Manages user authentication.
- Supabase: Provides database services.
- Groq Llama 3 70b: Used for processing and understanding text data.
- Langchain + Langgraph: Framework for creating and managing agents.
- AgentOps: Tracks agent activities.
- GPT 4 Vision: Recognizes and processes text from scanned documents.
- Docker: Ensures the application is containerized for easy deployment and scalability.
- AWS: Hosts the application, providing robust and scalable cloud services.
We welcome contributions to enhance the Medical Card project. Please contact [email protected]
For further information, please contact:
- Name: Maksym Liamin
- Email: [email protected]
- GitHub: https://github.com/monami44