What impact would reducing low-acuity attendance have on emergency department length of stay? A discrete event simulation modelling study

פוסט זה זמין גם ב: עברית

 Hazel Squires1Suzanne Mason1,

Colin O’Keeffe1,  Susan Croft1,  George Millington2

Correspondence to Dr Hazel Squires, Sheffield Centre for Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK; h.squires@sheffield.ac.uk

Abstract                             

Background Long waiting times in the ED have been shown to cause negative outcomes for patients. This study aims to assess the effect in reducing length of stay of (1) preventing low-acuity attenders from attending the ED and (2) diverting low-acuity attenders at triage to a colocated general practice (GP) service.

Methods Discrete event simulation was used to model a large urban teaching hospital in the UK, as a case study, with a colocated GP service. The Centre for Urgent and Emergency Care research database patient-level database (May 2015–April 2016), secondary literature and expert elicitation were used to inform the model. The model predicted length of stay, the percentage of patients being seen within 4 hours and the incremental cost-effectiveness of the colocated GP service.

Results The model predicted that diverting low-acuity patients to a colocated GP open 9:00 to 17:00 reduces the average time in the system for higher acuity attenders by 29 min at an estimated additional cost of £6.76 per patient on average. The percentage of higher acuity patients being seen within 4 hours increased from 61% to 67% due to the reduction in the length of stay of those who were in the ED for the longest time. However, the model is sensitive to changes in model inputs and there is uncertainty around ED activity durations, for which further primary data collection would be useful.

Conclusion Reducing the proportion of low-acuity attenders at the ED could have an impact on the time in the ED for higher acuity patients due to their use of shared resources, but is insufficient alone to meet current targets. The simulation model could be adapted for further analyses to understand which other changes would be needed to meet current government targets.

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