Hospital admission planning to optimize major resources utilization under uncertainty

Admission policies for elective inpatient services mainly result in the management of a single resource: the operating theatre as it is commonly considered as the most critical and expensive resource in a hospital. However, other bottleneck resources may lead to surgery cancellations, such as bed capacity and nursing staff in Intensive Care (IC) units and bed occupancy in wards or medium care (MC) services. Our incentive is therefore to determine a master schedule of a given number of patients that are divided in several homogeneous categories in terms of the utilization of each resource: operating theatre, IC beds, IC nursing hours and MC beds. The objective is to minimize the weighted deviations of the resource use from their targets and probabilistic lengths of stay in each unit (IC and MC) are considered. We use a Mixed Integer Program model to determine the best admission policy. The resulting admission policy is a tactical plan, as it is based upon the expected number of patients with their expected characteristics. On the operational level, this tactical plan must be adapted to account for the actual arriving number of patients in each category. We develop several strategies to build an operational schedule that leans upon the tactical plan more or less closely. The strategies result from the combination of several options to create a feasible operational schedule from the tactical plan: overplanning, flexibility in selecting the patient groups to be operated and updating the tactical plan. The strategies were tested on real data from a Thoracic Surgery Centre over a 10-year simulation horizon. The performance was assessed by the average waiting time for patients, the weighted target deviations and some indicators of the plan changes between the tactical plan and the operational schedule. Simulation results show that the best strategies include overplanning, a limited flexibility and infrequent updates of the tactical plan.