Evidence map›Paper›PMID 38925661›Full record

ArticleBMJ open quality2024

Use of computer simulation to identify effects on hospital census with reduction of transfers for non-procedural patients in community hospitals.

Laura Walker, Katharina Kohler, Matthew Jankowski, Todd Huschka

Abstract read
In one paragraph

Article in BMJ open quality, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Laura WalkerEmergency Medicine, Mayo Clinic Minnesota, Rochester, Minnesota, USA walker.laura@mayo.edu.ORCID 0000-0002-1127-327X
Katharina KohlerDivision of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK.
Matthew JankowskiEnterprise Solution Activation and Services, Mayo Clinic, Rochester, Minnesota, USA.
Todd HuschkaKern Center for the Science of Healthcare Delivery, Mayo Clinic Minnesota, Rochester, Minnesota, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIn-person healthcare delivery is rapidly changing with a shifting employment landscape and technological advances. Opportunities to care for patients in more efficient ways include leveraging technology and focusing on caring for patients in the right place at the right time. We aim to use computer modelling to understand the impact of interventions, such as virtual consultation, on hospital census for referring and referral centres if non-procedural patients are cared for locally rather than transferred. PATIENTS AND

methodsWe created computer modelling based on 25 138 hospital transfers between June 2019 and June 2022 with patients originating at one of 17 community-based hospitals and a regional or academic referral centre receiving them. We identified patients that likely could have been cared for at a community facility, with attention to hospital internal medicine and cardiology patients. The model was run for 33 500 days.

resultsApproximately 121 beds/day were occupied by transferred patients at the academic centre, and on average, approximately 17 beds/day were used for hospital internal medicine and nine beds/day for non-procedural cardiology patients. Typical census for all internal medicine beds is approximately 175 and for cardiology is approximately 70.

conclusionDeferring transfers for patients in favour of local hospitalisation would increase the availability of beds for complex care at the referral centre. Potential downstream effects also include increased patient satisfaction due to proximity to home and viability of the local hospital system/economy, and decreased resource utilisation for transfer systems.

Indexed as

Computer SimulationHospitals, CommunityPatient TransferCensusesHumansEfficiency, OrganizationalHealth services researchSimulation

Identifiers

PMID38925661
PMCPMC11202728

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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.