Data-Driven Optimisation of Endoscopy Department Resources Through Statistical Analysis and Mixed-Integer Linear Programming
Year: 2026
Type: article
Abstract: The efficient use of resources represents a critical challenge for public healthcare systems facing increasing demand. In this study, an operational analysis was conducted at Hospital del Mar (Barcelona) to demonstrate that persistent bottlenecks and capacity deficits are primarily organizational and not only quantitative. Through a prospective observational study and exploratory data analysis (EDA), it was identified that high apparent workloads often coexist with structural inefficiencies, particularly regarding the unpredictable demand of urgent and inpatient procedures. To address these gaps, a Mixed-Integer Linear Programming (MILP) model was implemented to optimize spatial and temporal resource allocation. Unlike reactive scheduling, this data-driven approach explicitly incorporates capacity reserves for non-programmable activities and ensures realistic time slots without increasing physical or human resources. It is shown that MILP-optimized scheduling significantly balances workload, eliminates artificial overlaps, and improves room utilization—reaching rates of 99.5%. The findings highlight that temporal agenda design constitutes a critical, yet underutilized, lever for hospital management. A scalable tool for evidence-based decision-making is provided by this framework, allowing for a clear distinction between apparent productivity and real efficiency. The proposed model is considered highly transferable to other clinical settings facing similar operational constraints.
Source: Applied Sciences
DOI: 10.3390/app16041864
Language: English
Subfield: Emergency Medical Services
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7128775063