Evidence map›Paper›PMID 28487459›Full record

SynthesisBMJ open2017

Syed Mohiuddin, John Busby, Jelena Savović, Alison Richards, Kate Northstone, William Hollingworth, Jenny L Donovan, Christos Vasilakis

Abstract readSystematic Review
In one paragraph

Synthesis in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  10. Key performance indicators in emergency department simulation: a scoping review.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Syed MohiuddinNIHR CLAHRC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK.
John BusbySchool of Medicine, Queen's University Belfast, Belfast, UK.
Jelena SavovićNIHR CLAHRC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK.
Alison RichardsNIHR CLAHRC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK.
Kate NorthstoneNIHR CLAHRC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK.
William HollingworthNIHR CLAHRC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK.
Jenny L DonovanNIHR CLAHRC West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK.
Christos VasilakisCentre for Healthcare Innovation & Improvement (CHI2), School of Management, University of Bath, Bath, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesOvercrowding in the emergency department (ED) is common in the UK as in other countries worldwide. Computer simulation is one approach used for understanding the causes of ED overcrowding and assessing the likely impact of changes to the delivery of emergency care. However, little is known about the usefulness of computer simulation for analysis of ED patient flow. We undertook a systematic review to investigate the different computer simulation methods and their contribution for analysis of patient flow within EDs in the UK.

methodsWe searched eight bibliographic databases (MEDLINE, EMBASE, COCHRANE, WEB OF SCIENCE, CINAHL, INSPEC, MATHSCINET and ACM DIGITAL LIBRARY) from date of inception until 31 March 2016. Studies were included if they used a computer simulation method to capture patient progression within the ED of an established UK National Health Service hospital. Studies were summarised in terms of simulation method, key assumptions, input and output data, conclusions drawn and implementation of results.

resultsTwenty-one studies met the inclusion criteria. Of these, 19 used discrete event simulation and 2 used system dynamics models. The purpose of many of these studies (n=16; 76%) centred on service redesign. Seven studies (33%) provided no details about the ED being investigated. Most studies (n=18; 86%) used specific hospital models of ED patient flow. Overall, the reporting of underlying modelling assumptions was poor. Nineteen studies (90%) considered patient waiting or throughput times as the key outcome measure. Twelve studies (57%) reported some involvement of stakeholders in the simulation study. However, only three studies (14%) reported on the implementation of changes supported by the simulation.

conclusionsWe found that computer simulation can provide a means to pretest changes to ED care delivery before implementation in a safe and efficient manner. However, the evidence base is small and poorly developed. There are some methodological, data, stakeholder, implementation and reporting issues, which must be addressed by future studies.

Indexed as

Models, OrganizationalTriageComputer SimulationCost-Benefit AnalysisCrowdingEmergency Service, HospitalHumansQuality ImprovementState MedicineUnited KingdomComputer simulationEmergency careOvercrowdingPatient flowSystematic reviewWaiting times

Identifiers

PMID28487459
PMCPMC5566625

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.