296. Process Mining Unscheduled Attendances in a Paediatric Emergency Department in Northern Ireland
Contributed abstract in session MD-5: Emergency Department /1, stream Regular talks.
Monday, 13:50-15:00Room: Room S6
Authors (first author is the speaker)
| 1. | Adele H Marshall
|
| Mathematical Sciences Research Centre (MSRC), Queen's University of Belfast | |
| 2. | Christine Kennedy
|
| Belfast Health and Social Care Trust | |
| 3. | Danny McWilliams
|
| Belfast Health and Social Care Trust | |
| 4. | Peter Cosgrove
|
| Emergency Medicine, Royal Belfast Hospital for Sick Children |
Abstract
Emergency departments (EDs) in UK, after the COVID pandemic, have seen increasing demand and high pressures on resources and staff fueled by challenges accessing routine services. Northern Ireland EDs have described excess patient mortality due to overcrowding and increasing four-hour waiting time benchmarking from 28.2% in January 2013 to 56.6% in September 2023. Paediatric Healthcare in Northern Ireland is under particular pressure driven by unscheduled attendances, exacerbated by risk-adverse approach to children, and mis-match of healthcare resources for specialty services.
This research uses routinely collected ED data for unscheduled attendances and admissions for paediatric care during 2022-2023 and applies advanced analytics and process mining to identify key characteristics for unscheduled pressures. The approach provides an understanding of flow of unscheduled care in the paediatric ED and a method for identifying clusters of patient-types who present at the ED. The characteristics can also be linked with each patient type, waiting time distribution in ED and subsequent stay in hospital and may be used to cost-effectively redistribute the allocation of resources to high-yield areas and to model future planning scenarios.
Keywords
- Emergency Department
- Analytics
- Patient flow
Status: accepted
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