AI-Powered Diabetes Risk Scoring—Instant, Paperless, Predictive

Predict diabetes risk scoring with iQud's real-time AI model for proactive and paperless healthcare decisions.

Overview

Our client is a leading multi-specialty hospital group with a prime focus on preventive healthcare and community outreach programs. The pain point of the client was the early identification of diabetes that required manual dependency on lab-based screening methods. Thus, to overcome this challenge, iQud suggested a quick, reliable, and real-time AI-powered solution helping with predictive diabetes risk scoring without any human dependency.

Technical Stack

React js
Industry
Healthcare
Region
Thailand
Compliance
Data Privacy
Focus Point
Risk Scoring
Compliance without compromise — PDPA, GDPR, HIPAA

Highlights

📈
86% accuracy compared to lab-tested HbA1c results
🩺
Processed 1,000+ patients/day in screening camps
✅
Enabled early intervention in 3 out of 10 high-risk users
📉️
Reduced diagnostic backlog by 45% during OPD surges

Challenges

Solutions

Challenges & Solutions

Low Adoption and Trust in the AI Model

Built AI predictive accuracy through clinical training and pilot studies.

Data Privacy and Compliance Concern

For patient data privacy, implemented encryption, anonymization, and compliance checks.

Integration issue with the current EMR System

For seamless EMR workflow integration, developed plug-and-play APIs

Reduction in Demographic Variability due to Model Accuracy

For accuracy with local population data, calibrate AI models to increase chances for variability.

Limited Connectivity or Community Screening

For rural camps and community screening, offline AI scoring was enabled with cutting-edge deployment.

Empowering Early Diabetes Detection – Smarter, Faster, Anywhere

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Key Features

  • Non-invasive digital questionnaire
  • Real-time risk scoring using a trained ML model
  • Works on mobile, tablet, and kiosk devices
  • Optional lead capture form for follow-up
  • PDF summary with doctor handover notes

Conclusion:

By enabling instant, paperless, and predictive diabetes risk scoring with an AI-powered solution, the hospitals and health centers were able to identify high-risk patients efficiently. The proposed solution not only bridged the early diagnosis gaps but also helped drive preventive care with much more confidence and with ease.

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