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Swasya AI

Swasya AI

Turning dialogue into data and data into clarity.

Created on 9th November 2025

Swasya AI

Swasya AI

Turning dialogue into data and data into clarity.

The problem Swasya AI solves

The Problem It Solves

  • Overburdened Primary Healthcare System: With only 1.2–1.4 doctors per 1000 people and around 2–3 primary health centers per million population, doctors face a massive workload, leaving little time for each patient.

  • Communication Gaps: Many patients and doctors come from different linguistic backgrounds, making it difficult for doctors to understand patient problems accurately due to language and dialect differences.

  • Time Constraints: Doctors typically have less than 3 minutes per patient, which limits their ability to review past medical reports, prescriptions, and understand the case deeply.

  • Manual Record Analysis: Doctors and nurses rely on paper-based prescriptions and reports, making it hard to analyze old records, maintain patient histories, and connect previous diagnoses with current conditions.


What People Can Use It For

  • Nurses: Use the mobile app to record patient interactions, scan prescriptions, and automatically create structured medical summaries.

  • Doctors: Access AI-generated medical histories and visit summaries before consultation, allowing for faster and more informed decision-making.

  • Administrators: Monitor patient flow, manage health center data, and detect regional health trends or outbreaks in real time.


How It Makes Existing Tasks Easier and Safer

  • Saves Consultation Time: AI-generated patient summaries reduce time spent reading and asking repetitive questions.

  • Improves Accuracy: AI extracts key details from scanned reports and prescriptions, minimizing human error in manual data entry or report reading.

  • Bridges Communication Gaps: Real-time transcription and summarization help doctors understand patients better, even across different languages.

  • Enhances Record Management: All patient data, including prescriptions and medical histories, are digitally organized and accessible through UHID-based records.

  • Enables Preventive Care: Aggregated, anonymized data can help identify local disease outbreaks early, improving public health response.

  • Strengthens Trust: With clear records and better understanding, both patients and doctors experience more transparent and confident consultations.

Challenges we ran into

Challenges and Solutions

  • Real-time Transcription Accuracy

    • Challenge: Handling multi-language (Hindi + English) speech during nurse–patient conversations caused inconsistent transcription.
    • Solution: Implemented language detection and context-based transcription models to improve accuracy and maintain sentence flow.
  • Document Scanning and Data Extraction

    • Challenge: Extracting consistent information from scanned prescriptions and handwritten reports was difficult due to unstructured layouts.
    • Solution: Combined OCR with AI summarization to extract and organize data into a standardized medical history format.
  • Backend Scalability and Reliability

    • Challenge: Managing multiple user roles (nurse, doctor, admin) and real-time sync led to backend overload during parallel sessions.
    • Solution: Deployed backend services using AWS Lambda (serverless) architecture to auto-scale functions and ensure smooth, cost-efficient performance.
  • Real-time Data Sync Between Dashboards

    • Challenge: Nurse updates were not reflecting instantly on the doctor’s dashboard during initial builds.
    • Solution: Designed a polling mechanism algorithm to continuously fetch updated transcriptions and present them in real time on the doctor’s dashboard.
  • Outbreak Visualization on Maps

    • Challenge: Displaying regional health trends dynamically on maps required optimized data handling and fast rendering.
    • Solution: We will implement React-Leaflet integrated with MongoDB’s geospatial queries to visualize real-time health outbreaks efficiently and interactively.

Tracks Applied (6)

Base44 Innovation Challenge

Swasya AI addresses a critical healthcare challenge, the mismanagement of patient data and slow diagnosis in India’s pri...Read More

Base44

Build Your Business with AI - Using Studio & Base44

Swasya AI addresses a critical healthcare challenge the mismanagement of patient data and slow diagnosis in India’s prim...Read More

Base44

Best Use of Qapi

Swasya AI leverages QAPI by Qyrus to ensure reliability, scalability, and performance across our AI-powered healthcare e...Read More

Qyrus

REMARKABLE INTEGRATION OF AWS

Our project, Swasya AI, relies on a hybrid cloud architecture built entirely on AWS to achieve both scalability and low-...Read More
Devfolio

Devfolio

Best Use of Gemini API

Swasya AI uses the Gemini API to address a core challenge in India’s primary healthcare system — the delay in diagnosis ...Read More
Major League Hacking

Major League Hacking

Best Use of MongoDB Atlas

Swasya AI leverages MongoDB as the backbone of its real-time healthcare data infrastructure - powering fast, reliable, a...Read More
Major League Hacking

Major League Hacking

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