Staffmatics
AI/ML Engineer & Full-Stack Developer
About the Project
Staffmatics is a healthcare workforce intelligence platform for hospitals, nursing homes, home health agencies, and staffing agencies. It replaces the spreadsheets, phone trees, and disconnected systems most healthcare organizations still use to fill shifts. The goal is to move an organization from reactive staffing to predictive workforce intelligence — forecasting shortages, matching qualified clinicians to open shifts, automating credential verification, and giving executives real-time visibility into labor cost, fill rates, and turnover risk. The system is four services: a clinician-facing web app, an admin console, the main API, and a dedicated Python ML service.
Key Highlights
- Built the ML service as a standalone FastAPI application handling match re-ranking, burnout prediction, and shortage forecasting
- Implemented the models directly in numpy — the deployment environment's Application Control policy blocks scipy's compiled solver DLLs
- Designed graceful degradation: the main API falls back to local rule-based matching if the ML service is unavailable, and the ML service falls back to heuristics if no trained artifacts exist
- Built AI shift matching that scores clinicians on credentials, specialty, availability, location, experience, and reliability
- Shipped a burnout and fatigue dashboard tracking overtime, consecutive shifts, and workload patterns to surface retention risk early
Technical Challenges
The interesting constraint was that the target environment blocked scipy's compiled solver DLLs, so scikit-learn was off the table entirely. Rather than fight the security policy, I reimplemented the logistic regression models in pure numpy and serialized the trained weights to .npz. The models are small enough that this was tractable, and it removed a heavy dependency chain from the deployment. The other design decision I care about is that nothing hard-fails: every layer has a defined fallback, so an ML outage degrades match quality rather than taking scheduling down.