Clinical Decision Support System

Overview

Developed intelligent maternal and child health dashboards with AI-backed risk analysis for early intervention — deployed in remote areas with low bandwidth. This system helps healthcare workers identify high-risk pregnancies and childhood illnesses that require urgent attention, improving outcomes in areas with limited access to specialists.

Challenge

Rural healthcare facilities in Pakistan face critical shortages of specialists and diagnostic equipment. Many preventable maternal and child deaths occur because risk factors aren't identified early enough. Healthcare workers needed a system that could help them make better clinical decisions with limited data and resources, while functioning reliably in environments with intermittent connectivity and power.

Approach

We developed a two-tier system: a lightweight mobile application for frontline health workers to collect and access patient data, and a more sophisticated backend that applies machine learning algorithms to identify risk patterns. The system was designed to work with minimal data inputs while providing actionable insights, rather than attempting to replace clinical judgment.

Technologies Used

• Progressive Web App for cross-device compatibility
• TensorFlow Lite for on-device inference
• Optimized ML models for low-resource environments
• Offline-first architecture with intelligent sync
• HL7 FHIR for healthcare data interoperability
• Explainable AI techniques for transparent recommendations

Implementation

The implementation began with extensive field research to understand the workflows and constraints of rural healthcare facilities. We collaborated with obstetricians, pediatricians, and public health experts to develop risk assessment algorithms based on WHO guidelines and local clinical protocols. The system was piloted in 5 facilities before being expanded to 35 locations across two districts, with continuous refinement based on user feedback and outcome data.

Results & Impact

• Deployed in 35 healthcare facilities serving a population of approximately 1.2 million
• Helped identify over 1,200 high-risk pregnancies requiring specialist intervention
• Reduced referral time for critical cases by 64%
• Improved protocol adherence from 43% to 87% for common childhood illnesses
• System functions effectively in areas with as little as 2G connectivity
• Received recognition from WHO regional office as a promising digital health innovation

Lessons Learned

This project taught us that successful healthcare AI in resource-constrained settings requires a fundamentally different approach than in high-resource environments. Rather than aiming for diagnostic perfection, we found greater value in systems that augment existing clinical workflows, respect the expertise of healthcare workers, and focus on actionable recommendations. We also learned the importance of designing for the entire healthcare ecosystem, not just the technology components.

Next Steps

Future development is focused on expanding the system to cover additional health conditions, improving the explainability of AI recommendations, and developing more sophisticated offline capabilities for extremely remote areas.