Evidence map›Paper›PMID 38907756›Full record

ArticleInternal and emergency medicine2024

Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT).

Brandon Stretton, Andrew E C Booth, Shrirajh Satheakeerthy, Sarah Howson, Shaun Evans, Joshua Kovoor, Waqas Akram, Keith McNeil, Ashley Hopkins, Kathryn Zeitz and 13 more

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Article in Internal and emergency medicine, 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
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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5 · Who and what money

Authors and funding

23 authors.

Brandon StrettonLyell McEwin Hospital, Elizabeth Vale, SA, 5112, Australia.
Andrew E C BoothSA Health, Adelaide, SA, 5000, Australia.
Shrirajh SatheakeerthySA Health, Adelaide, SA, 5000, Australia.
Sarah HowsonSA Health, Adelaide, SA, 5000, Australia.
Shaun EvansSA Health, Adelaide, SA, 5000, Australia.
Joshua KovoorUniversity of Adelaide, Adelaide, SA, 5005, Australia.
Waqas AkramLyell McEwin Hospital, Elizabeth Vale, SA, 5112, Australia.
Keith McNeilCommission On Excellence and Innovation in Health, Adelaide, SA, 5000, Australia.
Ashley HopkinsFlinders University, Bedford Park, SA, 5042, Australia.
Kathryn ZeitzSA Health, Adelaide, SA, 5000, Australia.
Alasdair LeslieSA Health, Adelaide, SA, 5000, Australia.
Peter PsaltisSA Health, Adelaide, SA, 5000, Australia.
Aashray GuptaRoyal North Shore Hospital, St Leonard's, NSW, 2065, Australia.
Sheryn TanUniversity of Adelaide, Adelaide, SA, 5005, Australia.
Melissa TeoLyell McEwin Hospital, Elizabeth Vale, SA, 5112, Australia.
Andrew VanlintLyell McEwin Hospital, Elizabeth Vale, SA, 5112, Australia.
Weng Onn ChanSA Health, Adelaide, SA, 5000, Australia.
Andrew ZannettinoUniversity of Adelaide, Adelaide, SA, 5005, Australia.
Patrick G O'CallaghanSA Health, Adelaide, SA, 5000, Australia.
John MaddisonLyell McEwin Hospital, Elizabeth Vale, SA, 5112, Australia.
Samuel GluckLyell McEwin Hospital, Elizabeth Vale, SA, 5112, Australia.
Toby GilbertLyell McEwin Hospital, Elizabeth Vale, SA, 5112, Australia.
Stephen BacchiLyell McEwin Hospital, Elizabeth Vale, SA, 5112, Australia. stephen.bacchi@sa.gov.au.ORCID 0000-0001-5130-8628

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Weekend discharges occur less frequently than discharges on weekdays, contributing to hospital congestion. Artificial intelligence algorithms have previously been derived to predict which patients are nearing discharge based upon ward round notes. In this implementation study, such an artificial intelligence algorithm was coupled with a multidisciplinary discharge facilitation team on weekend shifts. This approach was implemented in a tertiary hospital, and then compared to a historical cohort from the same time the previous year. There were 3990 patients included in the study. There was a significant increase in the proportion of inpatients who received weekend discharges in the intervention group compared to the control group (median 18%, IQR 18-20%, vs median 14%, IQR 12% to 17%, P = 0.031). There was a corresponding higher absolute number of weekend discharges during the intervention period compared to the control period (P = 0.025). The studied intervention was associated with an increase in weekend discharges and economic analyses support this approach as being cost-effective. Further studies are required to examine the generalizability of this approach to other centers.

Indexed as

Artificial IntelligencePatient DischargeAgedAlgorithmsFemaleHumansMaleMiddle AgedPatient Care TeamTime FactorsBed flowDigital healthDischargeImplementationMachine learning

Identifiers

PMID38907756

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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.