HemoSync: AI Blood Demand Forecasting

Inspiration

Working on the floors of large tertiary facilities like the University of Benin Teaching Hospital, I see the human cost of a reactive blood supply chain. When neurosurgical or trauma patients require immediate transfusions, discovering that a specific blood group is critically low forces families into frantic "replacement donation" scrambles. HemoSync was inspired by the realization that this is not just a healthcare problem; it is a data bottleneck. Historical usage, seasonal trauma spikes, and daily ward admissions are rarely aggregated. I wanted to build a bridge between clinical realities and backend logistics to shift blood banking from reactive emergencies to proactive planning.

What it does

HemoSync is a centralized, API-driven backend that synchronizes daily blood usage data from clinical wards with central blood bank inventory. It utilizes an AI-driven forecasting model to analyze consumption rates and scheduled elective surgeries, generating a 7-day predictive demand forecast. Crucially, the system is strictly advisory: it highlights optimal collection targets, but human blood bank managers retain complete control over resource allocation.

How we built it

I architected the core backend utilizing Python and FastAPI to ensure robust, asynchronous data handling and secure API routing. The deployment infrastructure leverages platforms like Google Cloud and Railway for scalable, reliable uptime. The AI component processes historical and synthetic ward data to output confidence-scored predictions, which are delivered via endpoints to the frontend dashboard.

Challenges

we ran into The biggest technical hurdle was engineering for variable infrastructure while maintaining data integrity. We had to design rule-based fallback alerts—hard-coded thresholds that automatically trigger if the predictive model underperforms or if the hospital's network drops, ensuring patient safety is never compromised by an offline system.

Accomplishments that we're proud of

What we learned

Translating high-stakes clinical logistics into functional code reinforced that raw engineering must be tempered by operational reality. I learned how to structure AI not as a replacement for human judgment, but as a heavily guardrailed tool that empowers clinicians to make faster, safer decisions.

What's next for HemoSync: AI Blood Demand Forecasting

The immediate next step is expanding our API architecture to demonstrate full interoperability with the proposed African Blood Bank Information System (ABBIS). We aim to refine our MVP into a controlled pilot, mapping out specific technical dependencies for localization across different regional health networks.

Built With

• api
• fastapi
• google-cloud
• machine-learning
• predictive-analytics
• python
• railway