MedQ - Revolutionizing Healthcare with Real-Time Data

Idea & Approach

MedQ is a hospital-based app designed to bring healthcare services directly to users’ fingertips. Despite the presence of OPD registration platforms, they often lack real-time data integration, providing limited insights into bed availability and using inefficient queuing models.

The MedQ app addresses these challenges by:

• Implementing a priority-based queue system, prioritizing patients based on the severity of their conditions.
• Providing real-time availability of OPD beds and timely updates, allowing users to choose hospitals based on bed count, distance, cost, and more.
• Gathering real-time data from hospitals and updating it in the database, making this information easily accessible to users.

Key Features:

1. Priority-Based Queuing System - Prioritizes patients based on their medical condition's urgency.
2. Real-Time Bed Availability - Allows users to see current bed counts and book accordingly.
3. User-Centric Filters - Search by bed count, distance, hospital cost, and more.

Architecture Diagram

Dependencies

• Flutter Framework: Cross-platform front-end development.
• MERN Stack: Backend development for hospitals' real-time data.
• Firebase: Storage for real-time data.

Tech Stack

Frontend:

• HTML5, CSS, React JS

Backend:

• JavaScript, Node JS, Flask, Python, Flutter

Database:

• Firebase, Firestore

Authentication:

• Firebase

UI Design:

• Figma, Visily, Whimsical, Lucid

APIs:

• Google Maps API, TomTom API

Feasibility Analysis

We conducted a survey across hospitals and gained insights from staff, revealing these crucial statistics:

• 94% of respondents prefer an app that provides faster OPD registration, reducing inline waiting time.
• 76% believe in a priority-based registration system.
• 82% support real-time bed and doctor availability information.

Survey Questions:

1. Will you use an app that minimizes waiting time for OPD registration?
2. Should OPD slots be registered based on patient priority or FCFS (First-Come-First-Serve)?
3. Would real-time information on beds and doctors improve your hospital experience?
4. Have you seen an app providing real-time bed/doctor availability and patient priority slot booking?

Challenges and Solutions

Challenge: Understanding the real problems faced by hospitals
Solution: Hospital visits provided key insights into actual pain points.

Challenge: Designing a Queuing Model
Solution: Integrating priorities with FCFS created an optimal dynamic queuing system.

Challenge: ML Model Integration
Solution: Flask provided an effective way to overcome integration issues.

Viability Analysis

Target Market Size:

• Population: Our app targets 33 million people in Delhi.
• Patient Segment: 50,000 daily OPD registrations in government hospitals.
• Hospital Segment: Targeting 180 hospitals (148 private, 32 government).

Revenue Model:

1. Subscription fees from hospitals.
2. Advertisements.
3. MedCoin: A reward system providing additional benefits like discounted services.

Top Selling Points:

1. Reduction in Pre-Admit Struggle: Seamless bed and doctor availability.
2. Asset to Life-Saving: Speedy admission through real-time updates.
3. By 2030, we aim to reduce patient waiting times by 30%.

Research and References

• "The Role of Real-Time Data in Healthcare"
  Journal: Healthcare Informatics Research
  Link

• "Queueing Models for Healthcare Operations"
  International Series in Operations Research & Management Science, Volume 184
  Link

• "Predictive Analytics in Hospital Management"
  Author: Gopalakrishna Palem
  Link

• "Impact of Bed Availability on Patient Care"
  Source: National Library of Medicine
  Link