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UniCare - Unified Healthcare Platform

Here's a concise breakdown of the healthcare solution:

Problem Statement:

Healthcare providers lose 30% efficiency annually

Key inefficiencies:

Prolonged patient wait times

Incomplete pre-consultation data collection

Lack of personalized patient follow-up

Solution: Gen AI-Enabled Healthcare Application

Core Features:

Clinical Documentation Automation

AI-powered transcription of consultations

Automatic generation of structured clinical notes

Summarization of medical conversations

Digital medical record portfolio & Prescription digital wallet

Patient Data Analysis

Identifies health patterns and risks

Recommends proactive measures

Suggests preventive screenings

Analyze Google Fit and similar services data for real time analysis

Health timeline visualization

Real-Time Decision Support

Provides diagnostic recommendations (include AI Medical Imaging)

Leverages global medical literature and Access to Research Papers

Uses patient-specific data for insights

Personalized Patient Education

Creates tailored educational materials

Generates videos and infographics

Simplifies medical condition explanations

Develops easy-to-understand care plans

Medication reminder and management system (we can integrate 1mg and Netmeds like websites - can be a source of revenue for us.)

Mobile-based teleconsultation platform (video-based) & Appointment booking system

Billing and Invoice Generation

Benefits:

Reduces healthcare workflow inefficiencies

Improves patient outcomes

Enhances overall care experience

Seamless integration into existing healthcare systems

Unique Value Proposition:

AI-driven, patient-centric approach

Comprehensive healthcare solution

Intelligent, data-driven medical support

Would you like me to elaborate on any of these points?

Tools and Technology

Here are the tools and technology we will be using:

AI Models:

MedPALM: AI model by Google trained to answer the medical questions only. (

https://sites.research.google/med-palm/⁠

)

Chester: AI model for analyzing the Chest X-Ray reports to diagnose a disease. Research paper:

https://arxiv.org/abs/1901.11210#⁠

| GitHub Repo:

https://github.com/mlmed/chester-xray⁠

⁠

Chestx-ray8: Another dataset and model to work with Chest X-Ray reports for Diagnosis. Research paper:

https://arxiv.org/abs/1705.02315⁠

⁠

Medical Image Analysis with TensorFlow DLTK:

https://blog.tensorflow.org/2018/07/an-introduction-to-biomedical-image-analysis-tensorflow-dltk.html⁠

⁠

Privacy and Security:

Biometric Authentication: We can use the biometric authentication using WebAuth that allows us to use the indevice fingerprint scanner for a website or an app. Supported browsers list:

https://caniuse.com/?search=webAuthn⁠

⁠

Argon2id: The most powerful encryption library available right now. We will be using it to encrypt the confidential information available on our platform.

The Signal Protocol: For encrypting the conversations between a doctor and a patient

Backend:

Python: Python will be the primary language used for developing the backend of the portal.

Flask: Flask will be the framework I’ll be using for creating all the RESTful APIs for communicating with the frontend.

Database: PostgreSQL with Supabase or just MongoDB Atlas (saves us time).

Clerk: We can use Clerk for user authentication and Identity management so, we can focus more on main things for the platform.

ImageKit: For storing all the images of users as well as the report data.

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