An Objective Method for Bed Capacity Planning in a Hospital Department

Abstract

Summary Objectives: To propose an objective approach in order to determine the number of beds required for a hospital department by considering how recruitment fluctuates over time. To compare this approach with classical bed capacity planning techniques. Methods: Asimulated data-based evaluation of the impact that the variability in hospital department activity produces upon the performance of methods used for determining the number of beds required. The evaluation criteria included productive efficiency measured bythe bed occupancy rate, accessibility measured by the transfer rate of patients due to lack of available beds and a proxy of clinical effectiveness, by the proportion of days during which there is no possibility forunscheduled admission. Results: When the variability of the number of daily patients increases, the Target Occupancy Rate favors productive efficiency at the expense of accessibility and proxy clinical effectiveness. On the contrary, when the variability of the department activity is marginal, the Target Activity Rate penalizes the proxy of clinical effectiveness, and the Target Occupancy Rate under-optimizes productive efficiency.The method we propose led to a superior performance in terms of accessibility and proxy of clinical effectiveness at the expense of productive efficiency. Such a situation is suitable for intensive care units. In the case of other departments, a weighting procedure should be used to improve productive efficiency. Conclusions: This approach could be considered as the first step of a family of methods for quantitative healthcare planning.

Integrated decision making in healthcare : an operations research and management science perspective

“…The desirable occupancy level should be calculated as a complex function of the service mix, the number of beds and the length of stay distribution [242,243]. This non-linear relationship between number of beds, mean occupancy level and the number of patients that have to be rejected for admission due to lack of bed capacity is often emphasized [7,123,242,247,295,371,372,415]. In determining the appropriate average utilization, the effect of economies-of-scale due to the so-called portfolio effect plays a role: larger facilities can operate under a higher occupancy level than smaller ones in trying to achieve a given patient service level [217,243,244,295], since randomness balances out.…”

“…Methods: computer simulation [7,19,104,108,112,156,157,206,221,222,238,241,242,243,244,259,292,295,347,371,372,373,375,415,439,478,497,517,519,523,524], heuristics [309], Markov processes [7,68,172,192,199,245,246,247,335], mathematical programming [127,202,241,318,327,375,376], queueing theory [11,29,…”