Deporte Adaptado a personas con discapacidad.

The demand for hospital beds can be divided into elective (scheduled) and emergency (unsched- uled) admissions. Both categories of admissions impact on how many beds are needed to meet demand, while maintaining reasonable bed utilisation rates. In the literature, most bed planning queueing models attempt to overcome bed shortages or policies that lead to patient misplacement, bumping, or rejection. Hospital managers are under pressure to reduce bed capacity and decrease occupancy rates in the name of operational efficiency.

Young (1962a [172], 1962b [171]) proposed an incremental analysis approach in which the cost of an additional bed is compared with the benefits it generates. Beds were added until the increased cost was equal to the benefits.

Kao et al, 1981 [97] proposed an M/G/∞ model to periodically reallocate beds to medical ser- vices to minimise the expected overflows in different hospital wards. A demand forecasting system using the existing data base for generating inputs to the bed allocation model was constructed. For each medical service, the authors established the base line requirement by requiring that the number of beds assigned to the service should be sufficient, in terms of yearly aggregate, to accommodate a

Chapter 6 MATHEMATICALMODELLING OF THE NHAND THERG CCUS 150 pre-specified amount of patient load generated by the target population. The authors distributed the remaining beds to services with the objective of minimising the expected total average overflows over the months in the planning horizon.

Green et al, 2001 [70] applied a queueing model approach to the hospital bed planning issue to gain insights on the potential impact of cost-cutting strategies on patients’ delays for beds. Using a queueing theory approach, the factors that have the greatest impact on the trade-off between hospi- tal occupancy levels and delays were identified. It was stated that using target occupancy levels as the primary determinant of bed capacity is inadequate and may lead to excessive delays for beds. By integrating queueing theory and compartmental models of flow, Gorunescu et al, 2002 [62] demonstrated how by changing arrival rates, length of stay and bed allocation influences bed oc- cupancy, emptiness and rejection rates in departments of geriatric medicine in a London teaching hospital. By considering an M/P H/c/n queue model, the authors showed how the provision of extra emergency use beds could improve performance while controlling costs.

de Bruin et al, 2005 [39] applied a stationary 2-D queueing system with blocking to analyse conges- tion in emergency care chains. The primary goal was to determine the optimal bed allocation over the emergency care chain, given a required service level (maximum of 5% refused admissions). The bottlenecks were identified, the impact of fluctuation in demand was described and the optimal bed capacity distribution for cardiac patients was calculated. Cooper et al, 1974 [34] dealt with a very similar problem extended to estimating the number of beds necessary for two units: acute and intermediate coronary care, each of which should have a maximum turn-away rate of 5%.

Koizumi et al, 2005 [104] analysed congestion levels in the Philadelphia mental health system us- ing a queueing network model with blocking. Their model focused on blocking between three types of mental facilities, namely, extended acute hospitals, residential facilities and supported housing. The authors investigated how effectively the increase in the number of supported housing beds could reduce the steady-state congestion level in the system.

Cochran et al, 2006 [31] proposed a multi-stage stochastic methodology for analysing the flow of patients in a whole hospital setting with multiple patient types. The authors combined queueing network analysis and discrete event simulation to balance bed utilisation targets with the associated benefits of reduced: waiting, patient blocking, poor bed assignment and emergency department overflow behaviours. The methodology is applied to a 400 bed major hospital including an emer- gency unit.

Chapter 6 MATHEMATICALMODELLING OF THE NHAND THERG CCUS 151 hospitals using queueing networks and discrete event simulation models. The methodology aims to balance bed unit utilisations in an entire hospital and minimise the blocking of beds from upstream units within given constraints on bed reallocation. The methodology included the assessment and effect of time-dependent patterns. Queueing networks were used to assess the flows between units and to establish target utilisations of bed units. Discrete event simulation was then used to max- imise the flow through the balanced system including non-homogeneous effects, non-exponential lengths of stay, and blocking behaviour.

Chaussalet et al, 2006 [26] developed a patient flow model through healthcare systems with con- strained capacity. The model used a closed queueing network with the assumption that the system is always full. The authors modelled the progression of patients through a geriatric department in the UK as a set of conceptual phases. On admission, patients enter the first phase (assessment, diagnosis, etc.), from which they are either discharged, or transferred in to the second phase (some form of rehabilitation). In the final phase, which corresponds to long-stay care, all patients are eventually discharged. The model assisted service managers and clinicians with decision-making on bed allocation and on discharge policies.

de Bruin et al, 2007 [40] investigated the emergency in-patient flow of cardiac patients in a uni- versity medical centre. The impact of variability (in both length of stay and arrivals) on capacity requirements was described. They applied a queueing model to analyse congestion in the emer- gency care chain. With this model, the number of beds in the care chain is determined for several service levels, given a maximum number of refused admissions. In 2009, de Bruin et al [38] devel- oped a decision support system, based on the Erlang loss model, which can be used to evaluate the current size of nursing units, and to quantify the impact of bed reallocations and merging of wards.