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PulsePoint AI - An AI-Powered Intelligent Healthcare Management Platform

Abhijeet Bansode
ambula-26-868031912005.us-west1.run.app
PulsePoint AI - An AI-Powered Intelligent Healthcare Management Platform

Overview

Reviewer Notes

Thank you for reviewing PulsePoint AI – An AI-Powered Intelligent Healthcare Management Platform.

🌐 Project Links

🚀 Project Overview

PulsePoint is a full-stack AI-powered healthcare platform developed for the Build for Ambula '26 challenge. The platform is designed to simplify healthcare management for both patients and doctors by combining modern web technologies with Artificial Intelligence and intelligent healthcare workflows.

The application goes beyond basic appointment booking by integrating AI-assisted clinical support, predictive healthcare analytics, medication management, and intelligent scheduling into a single responsive platform.

✨ Implemented Features

👨‍⚕️ Patient Module

  • Secure authentication
  • Personal health profile
  • AI-powered symptom triage
  • Intelligent doctor recommendation
  • Doctor search by specialization
  • Doctor profile with consultation details
  • Appointment booking
  • Appointment history
  • Booking confirmation
  • Digital health summary
  • Disease risk assessment
  • Medicine reminders
  • Medication adherence tracking
  • Interactive health analytics
  • Mobile-responsive interface

🩺 Doctor Module

  • Secure doctor login
  • Doctor dashboard
  • Daily appointment management
  • Patient health summary
  • Consultation management
  • AI-assisted consultation drafts
  • Digital prescription generation
  • Recovery timeline prediction
  • Follow-up recommendations
  • Appointment management

🤖 Artificial Intelligence Features

  • Intelligent Symptom Triage
  • Emergency Symptom Detection
  • Smart Doctor Recommendation
  • AI Clinical Decision Support
  • Consultation Summary Generation
  • Recovery Timeline Prediction
  • Medical Guidance Generation

AI capabilities are powered using Google Gemini.

📊 Machine Learning & Smart Healthcare Features

  • Disease Risk Prediction
  • Intelligent Appointment Recommendation
  • Medication Adherence Analysis
  • Personalized Health Insights
  • Interactive Risk Visualization
  • Radar Chart Analytics
  • Smart Healthcare Recommendations

📱 User Experience

  • Fully responsive (mobile-first)
  • Modern healthcare UI
  • Interactive dashboards
  • Responsive charts
  • Clean navigation
  • Fast page rendering
  • Accessible layouts

☁️ Cloud Services

  • Firebase Authentication
  • Firestore Database
  • Google Gemini API
  • Cloud-based data synchronization
  • Secure protected routes

⚙️ Technology Stack

Frontend

  • React
  • TypeScript
  • Vite
  • Tailwind CSS

Backend

  • Node.js
  • Express.js

Database

  • Firebase Firestore

Authentication

  • Firebase Authentication

Artificial Intelligence

  • Google Gemini API

📂 Repository

The GitHub repository includes:

  • Complete source code
  • Setup instructions
  • Installation guide
  • Environment configuration
  • Folder structure
  • Architecture documentation
  • Feature documentation
  • Deployment instructions

🔧 Setup

  1. Clone the repository.
  2. Install frontend and backend dependencies.
  3. Configure Firebase credentials.
  4. Add your own Google Gemini API key in the .env file.
  5. Run the development server.

The repository README contains detailed setup instructions.

🔑 Environment Variables

The repository does not include secret keys.

Please configure:

  • Firebase credentials
  • Google Gemini API Key

using your own .env file before running the project locally.

🧪 Test Credentials

If authentication is required, please use the credentials provided below.

Patient

  • Email: <PATIENT_EMAIL>
  • Password: <PATIENT_PASSWORD>

Doctor

  • Email: <DOCTOR_EMAIL>
  • Password: <DOCTOR_PASSWORD>

(If using Firebase Authentication with new registrations enabled, reviewers may also create their own accounts.)

🏗️ Design Decisions

The project was designed around four primary goals:

  • Deliver a smooth healthcare experience for patients.
  • Reduce administrative workload for healthcare professionals.
  • Integrate AI meaningfully into healthcare workflows instead of using AI only for demonstration purposes.
  • Build a scalable architecture suitable for future healthcare expansion.

The codebase follows a modular structure to improve maintainability and scalability.

📖 Documentation

The GitHub README contains comprehensive documentation covering:

  • Project overview
  • Problem statement
  • Vision and objectives
  • Complete feature list
  • AI & Machine Learning modules
  • System architecture
  • Technology stack
  • Project structure
  • Installation guide
  • Environment setup
  • Deployment
  • Database design
  • Application workflows
  • AI processing pipeline
  • Future roadmap
  • License
  • Acknowledgements

🎯 Build for Ambula '26

PulsePoint was developed as a submission for the Build for Ambula '26 hiring challenge and demonstrates how Artificial Intelligence can enhance healthcare through intelligent automation, predictive analytics, secure cloud infrastructure, and a modern user experience.

Thank you for taking the time to review the project. I hope you enjoy exploring PulsePoint.