Comparing de-congestion scenarios using a hospital event simulation model.

W Hou, S Qin, C Thompson

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

The ever increasing demand for hospital services has resulted in more and more congestion episodes occurring in hospital emergency departments (ED). Congestions lead to serious problems such as delayed treatment, increased mortality, as well as stressed hospital staff. It is thus important to explore suitable solutions to manage congestions when they occur. This study seeks to identify effective ways to resolve the serious situation of congestion in a hospital by testing a range of de-congestion strategies using simulated scenarios. All scenarios were investigated through a sophisticated simulation model HESMAD (Hospital Event Simulation Model: Arrivals to Discharge) that captures the characteristics of patient flow based on an existing patient journey database from a large tertiary hospital in South Australia. Simulation models can demonstrate the changes and the impacts of different operational parameters within a complex hospital system before, during and after congestions. In this study, eight scenarios, suggested by senior hospital staff, were investigated using the simulation model, in addition to the base scenario where no intervention was applied. These scenarios include diverting ambulances, postponing elective patients, and discharging certain groups of patients earlier when congestions occur. Parameters such as the number of congestions over a one-year period, the duration of a congestion, and the 10 am hospital occupancy of each scenario were examined. Simulation of each scenario was replicated 20 times under the same conditions to obtain an average behaviour that would allow meaningful comparison of the results from different intervention scenarios. The results show that, for the scale of actions taken, hospital occupancy remained almost the same for all scenarios, while the duration of congestions and the frequency of their occurrence exhibited different levels of reduction. The scenario of temporarily diverting ambulances was most effective in reducing the number of congestion days (from 76.5 days in the base case to 44.2 days; a reduction of over 44%). However, other simulation scenarios, such as removing particularly long-staying patients and postponing the admission of elective patients may be preferable to diverting new emergency admissions when trying to reduce congestion duration and frequency. Although the aim was to shorten the duration of a congestion when it occurred, an important side benefit was observed for all interventions tested, that is, the number of congestion episodes in the long term (over one-year simulation period) was also reduced.

Original languageEnglish
Pages1281-1287
Number of pages7
Publication statusPublished - 2017
EventThe 22nd International Congress on Modelling and Simulation (MODSIM2017) -
Duration: 3 Dec 2017 → …

Conference

ConferenceThe 22nd International Congress on Modelling and Simulation (MODSIM2017)
Period3/12/17 → …

Fingerprint Dive into the research topics of 'Comparing de-congestion scenarios using a hospital event simulation model.'. Together they form a unique fingerprint.

Cite this