Document Type

Article

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Business and Management.

Publication Details

Emerg Med J 2011;28:A3-A4 doi:10.1136/emermed-2011-200617.7

Abstract

Objectives and Backgrounds Emergency Department (ED) overcrowding and associated excessive Patient Experience Times (PETs) have proven deleterious impacts on patient mortality, morbidity and overall length of hospital stay. Health systems constantly seek cost-effective organisational strategies to reduce ED crowding and improve patient outcomes, but complex change implementation is constrained by the necessity of maintaining concurrent safe patient-care. Computer modelling in a “virtual reality” has been successfully utilised in industries outside medicine, in providing innovative “real-time” solutions to outdated practices. Therefore a bespoke “Virtual ED” computer model, based on a Discrete Event Simulation (DES) -Based Framework was constructed to determine the best simulation scenarios needed for effective “real-time” strategies to improve PETs in a Dublin teaching hospital ED. The three simulation scenarios tested were: (1) Increasing medical staffing. (2) Increasing assessment space. (3) Enforcing the national 6-h boarding limit.

Methods A collaborative interactive decision support model was constructed to analyse patient flow through the ED, considering the variability in patients' arrival rate, the complexity levels of patients' acuity, and the dynamic interactions between key resources (eg, clinical staffing, physical capacity, and spatial relationships). ED Process Mapping utilised IDEF0, for modelling complex systems in a hierarchical form and Extend Suite V.7 software was used to develop the DES—based framework model. Historical, anonymised ED patient data of 59 986 patient episodes (tracking times, indirect acuity and clinical resource utilisation) was analysed from the “real-time” ED Information System. Baseline ED Key Performance Indicators (KPIs), PETS and resource utilisation was determined for comparison with the DES model. Distinct study scenario variables (Abstract 007 table 1) were added to the DES model and run for 3 month continuous blocks to eliminate confounders. Continuous verification and validation of the ED simulation model was ensured by using Kolmogorov–Smirnov goodness of fit test with a 5% significance level. The ultimate results of the simulation model were validated using three techniques; face validation, comparison testing, and hypothesis testing, with the deviation between actual and simulated results ranging from 1% to 9% with an average of only 5% deviation.

DOI

https://doi.org/10.21427/D7MJ56

Funder

Technological University Dublin

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