Developing a predictive model for resource allocation in healthcare: A case study from an Italian Hospital

The sustainability and efficiency of healthcare systems remain a global challenge, particularly in resource allocation for elective surgeries. This study examines the case of the Rizzoli Orthopedic Institute, a specialized Italian hospital facing significant waiting list imbalances, with approximately 24,000 patients awaiting surgery. The research employs a predictive modeling approach to optimize hospital resource allocation, particularly operating rooms and inpatient beds, to improve surgical scheduling efficiency. By leveraging statistical and computational methods, including historical data analysis and simulation modeling, this study aims to identify an optimal strategy to balance surgical demand and capacity. Over a sequence of 1811 total hip replacement surgeries, our mean calculated operating time was 74.31 min (SD: 19.41), and the estimate of resource demand calculated 1635 total operating hours (or 258 shifts) and 19 bed spaces to clear the current waiting list. Our model indicated potential for 30 % capacity-demand mismatch for this procedure alone. These findings indicate the need for strategic realignment of hospital resources. Key findings indicate that the existing operating-room capacity and bed assignments are insufficient to handle even a single high-volume surgery procedure (total hip replacements) without delays. In real-life practice, hospital managers can use these findings to inform scheduling policy, make staffing assignment priorities, and maybe even plan temporary capacity boosts or off-site sites for surgery.