Think of this as a smart scheduling assistant for hospital operating rooms that learns from past data and live conditions (staffing, emergencies, cancellations) to constantly reshuffle the theatre list so more patients get treated on time with fewer last‑minute surprises.
Hospitals lose capacity and money when operating lists overrun, start late, or leave gaps because of poor scheduling and unforeseen changes. This tool aims to improve theatre utilisation, cut cancellations and delays, and increase the number of completed operations without adding more staff or rooms.
Tight integration with hospital theatre workflows and EHR/OR systems, plus access to rich historical theatre and patient-level data that can continuously refine duration and no‑show predictions, creates a workflow and data moat that is hard for generic AI tools to replicate.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Integration with heterogeneous hospital IT (EHR, theatre management, staffing systems) and ensuring data quality and timeliness for reliable predictions across multiple sites.
Early Majority
Compared with traditional OR scheduling modules bundled with big EHR/OR vendors, this approach leans more heavily on data-driven predictions of case duration, delays, and cancellations and can continuously learn from local performance, potentially delivering higher utilisation lifts without major workflow changes.