Evidence map›Paper›PMID 33835338›Full record

ArticleHealth care management science2021

Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation.

G J Melman, A K Parlikad, E A B Cameron

Abstract read
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In one paragraph

Article in Health care management science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 2 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, 2 syntheses or guidelines pooled it.

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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

3 authors.

G J MelmanInstitute for Manufacturing, Department of Engineering, University of Cambridge, 17 Charles Babbage Rd, Cambridge, CB3 0FS, UK. gj.melman@gmail.com.ORCID http://orcid.org/0000-0002-8920-8435
A K ParlikadInstitute for Manufacturing, Department of Engineering, University of Cambridge, 17 Charles Babbage Rd, Cambridge, CB3 0FS, UK.ORCID https://orcid.org/0000-0001-6214-1739
E A B CameronConsultant Gastroenterologist and Director of Improvement and Transformation, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Box 146 Executive Offices, Cambridge, CB2 0QQ, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke's hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

Indexed as

COVID-19Efficiency, OrganizationalHospitalsPandemicsResource AllocationCritical CareElective Surgical ProceduresEquipment and Supplies, HospitalHumansOperating RoomsSARS-CoV-2United KingdomCapacity managementCOVID-19Intensive careOperations researchResource allocationSimulation

Identifiers

PMID33835338
PMCPMC8033099

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LicenceCC BY
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Registered trials

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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.