FLEXIBILITY IN HOSPITAL BED CAPACITY PLANNING

One of the major concerns of health care management (HCM) is to optimise the health care operations processes while working within imposed constraints of the management system. One of the major operations is bed capacity planning and control that aims to satisfy patients’ needs, optimise ward efficiency and increase the utilisation of the provided service. Often, bed capacity planning is considered as a long-term strategic decision due to the lack of flexibility in managing the capacity. However, in realistic settings such as seasonal demand fluctuations, staff absences, emergency requirements for resources and beds, short-term and temporary maintenance, repair or renewal works, capacity decisions have to be made to improve the flexibility in management. To satisfy the need for flexibility in HCM decision makers need quantitative tools to organise short-term changes in operations and to review periodically tactical and strategic plans for anticipated bed capacity to reorganise department resources. Several methods such as the ratio method, discrete event simulation, queuing models and stochastic simulation have been suggested to solve the bed capacity planning problem in the literature (Kokangul, 2008) for long-term strategic decisions. The ratiobased method which uses the average length of stays has been applied extensively to determine the size of the required bed capacity (Nguyen et al, 2005). The size of the required bed capacity is determined using the length of stay (LOS) ratio (Nguyen et al, 2005). Capacity decisions have also been made based on target occupancy levels. However, these methods do not take into account the variation of requested admissions over time. These variations arise due to the unpredictable nature of hospital admission rates and patients length of stays. Stochastic simulation models used to determine the size of the required bed capacity based on the number of patients in the hospital (El- Darzi et al, 1998). A combination of simulation, queuing theory, statistical analysis and mathematical modelling are used to determine the bed requirements and to predict the probability that patients needing admission will be turned away (Hershley et al, 1981).