Deep Learning Turns Patient Volume Data Into Smarter Schedules

The Client:

A healthcare system managing staff scheduling across multiple departments, where scheduling was done manually based on experience and rough volume estimates leading to chronic under- and over-staffing.

The Engagement:

SSI built an end-to-end workforce optimization platform combining MLP-powered patient volume forecasting with mathematical optimization models automating shift planning and aligning staffing levels to forecasted demand.

The Challenge:

Scheduling managers were building shift plans based on historical averages and intuition. With patient volume varying significantly by day, season, and department, this approach consistently produced mismatches between staff on the floor and actual workload. Under-staffing created patient care risks and staff burnout. Over-staffing inflated labor costs. Neither was systematically tracked or corrected. The process restarted each scheduling cycle with the same manual approach. The administrative burden of scheduling was also significant. Managers reported spending up to 50% of their time on roster management, time that should have been spent on clinical operations.

The Solution:

SSI implemented a deep learning forecasting model, a Multi-Layer Perceptron (MLP) network, trained on the health system’s patient volume history. The model predicts demand by department and time slot with measurably higher accuracy than traditional methods. The forecasting output feeds directly into a scheduling optimization engine using mathematical optimization (OR-based models) to generate shift plans that match staffing levels to forecasted demand, minimizing both under- and over-staffing risk. The platform integrates credentialing, role constraints, and compliance requirements into the optimization model, producing schedules that are not just operationally optimal but immediately actionable.

The Impact:

Administrative time spent on scheduling dropped by 50%. Managers moved from building schedules to reviewing and approving them.

Forecast accuracy improved by 40% versus the baseline method, translating directly into better-matched staffing levels and reduced variance in care delivery.