ArticleHealth systems (Basingstoke, England)2020
A decision support system for demand and capacity modelling of an accident and emergency department.
Article in Health systems (Basingstoke, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 36 citations in OpenAlex.
- A study of the evacuation and allocation of hospital beds during the Covid-19 epidemic: a case study in Iran.BMC health services research · 2022Article
- A hybrid analytical model for an entire hospital resource optimisation.Soft computing · 2021Article
- Improving chemotherapy infusion operations through the simulation of scheduling heuristics: a case study.Health systems (Basingstoke, England) · 2021Article
- A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak.Journal of healthcare engineering · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 - January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.