rushithakkar873 / patient-data-management-system Goto Github PK

Description:
GPT-MediAssistant is a patient data management system designed to enhance the interaction between patients and doctors while ensuring the security and accuracy of medical information. It features intuitive interfaces for patients to input health data securely and for doctors to access, verify, and analyze this data using GPT technology.

Features:

• Patient Data Input: User-friendly interface for patients to input medical history, symptoms, medications, etc.
• Doctor Access and Verification: Secure access for doctors to review and validate patient-provided information.
• GPT-Powered Analysis: Integration of GPT technology for advanced analysis, offering automated insights and potential diagnoses.
• Secure Communication: Implementation of end-to-end encryption to safeguard sensitive health information.
• Secret Key Integration: Mechanism for doctors to integrate the GPT-powered analysis API using a secure secret key.

Technology Stack:

• Frontend: React.js
• Backend: Node.js (Express.js)
• Database: MongoDB
• Other Technologies: Tailwind CSS for styling

Deliverables:

• Fully functional web application with an intuitive user interface.
• Documentation for installation, usage, and additional configurations.

Description:
The backend of GPT-MediAssistant handles the logic and data management for the patient data management system. It provides secure APIs for patient data input, doctor access and verification, and integration with the GPT-powered analysis API.

Technologies Used:

• Node.js: For server-side logic
• Express.js: Web framework for Node.js
• MongoDB: NoSQL database for storing patient data
• OpenAI: Integration for GPT-powered analysis
• mongoose: MongoDB object modeling for Node.js
• Shadcn: API integration for secure communication
• git-github: Version control and collaboration

Challenges Faced:

• Ensuring secure communication between frontend and backend.
• Integrating OpenAI's GPT technology for accurate analysis.
• Implementing secret key authentication for doctor access to the GPT analysis API.

Lessons Learned:

• Importance of data security and encryption in healthcare applications.
• Handling and validating sensitive patient data effectively.
• Leveraging AI technology for advanced medical analysis.

Future Improvements:

• Implementing additional features for patient and doctor interaction.
• Enhancing scalability and performance for large-scale deployment.
• Continuous monitoring and updates to ensure compliance with healthcare regulations.

Contributors:

• Rijans Bhagat
• Rushi Thakkar
• Vipul Chaudhary

Hackathon Details:

• Event: Hack the Spring
• Date: 1st - 2nd March 2024