Dynamics of bed use in accommodating emergency admissions: stochastic simulation model

Abstract

Objective To examine the daily bed requirements arising from the flow of emergency admissions to an acute hospital, to identify the implications of fluctuating and unpredictable demands for emergency admission for the management of hospital bed capacity, and to quantify the daily risk of insufficient capacity for patients requiring immediate admission. Design Modelling of the dynamics of the hospital system, using a discrete-event stochastic simulation model, which reflects the relation between demand and available bed capacity. Setting Hypothetical acute hospital in England. Subjects Simulated emergency admissions of all types except mental disorder. Main outcome measures The risk of having no bed available for any patient requiring immediate admission; the daily risk that there is no bed available for at least one patient requiring immediate admission; the mean bed occupancy rate. Results Risks are discernible when average bed occupancy rates exceed about 85%, and an acute hospital can expect regular bed shortages and periodic bed crises if average bed occupancy rises to 90% or more. Conclusions There are limits to the occupancy rates that can be achieved safely without considerable risk to patients and to the efficient delivery of emergency care. Spare bed capacity is therefore essential for the effective management of emergency admissions, and its cost should be borne by purchasers as an essential element of an acute hospital service.

Emergency and on-demand health care: modelling a large complex system

“…The bed occupancy target for 2004 set by Government 18 is 82% and the current figures for QMC and NCH are 84.7 and 86.7%, respectively. Bagust et al 19 have used DES to show that it is risky to have average occupancy figures higher than 85%.…”

Discrete event simulation for performance modelling in health care: a review of the literature

“…This is not only true for operational-level models but also true for policy-level models. For example, Bagust et al (1999) is an influential paper that reports a simulation of inpatient beds for emergency admissions using Excel spreadsheets, concluding that the risk of a hospital bed shortage is low when mean bed occupancy remains under 85%. This simple yet effective model demonstrates that bed crises occur not necessarily because of poor management but because of the nature of stochastic arrivals.…”

Managing access block

“…Bagust (1999) used a stochastic simulation model to test the ability of a hospital system to accommodate new emergency admissions. They showed that at hospital occupancy rates of greater than 85% there was a significant risk of not having a bed available for acute hospital admissions and at greater than 90% occupancy, regular bed crisis began to develop.…”