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

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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.

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Funding

S.B. is supported by a Fulbright Scholarship. P.J.P. receives a L3 Future Leader Fellowship from the National Heart Foundation of Australia (FLF106656).

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Authors and Affiliations

  1. Lyell McEwin Hospital, Elizabeth Vale, SA, 5112, Australia

    Brandon Stretton, Waqas Akram, Melissa Teo, Andrew Vanlint, John Maddison, Samuel Gluck, Toby Gilbert & Stephen Bacchi

  2. SA Health, Adelaide, SA, 5000, Australia

    Brandon Stretton, Andrew E. C. Booth, Shrirajh Satheakeerthy, Sarah Howson, Shaun Evans, Kathryn Zeitz, Alasdair Leslie, Peter Psaltis, Melissa Teo, Andrew Vanlint, Weng Onn Chan, Patrick G. O’Callaghan, John Maddison, Samuel Gluck, Toby Gilbert & Stephen Bacchi

  3. University of Adelaide, Adelaide, SA, 5005, Australia

    Brandon Stretton, Shaun Evans, Joshua Kovoor, Peter Psaltis, Sheryn Tan, Weng Onn Chan, Andrew Zannettino, Samuel Gluck, Toby Gilbert & Stephen Bacchi

  4. Royal Adelaide Hospital, Adelaide, SA, 5000, Australia

    Brandon Stretton, Andrew E. C. Booth, Shrirajh Satheakeerthy, Sarah Howson, Shaun Evans, Kathryn Zeitz, Alasdair Leslie, Peter Psaltis, Weng Onn Chan & Patrick G. O’Callaghan

  5. Ballarat Base Hospital, Ballarat Vic, Australia

    Joshua Kovoor

  6. Commission On Excellence and Innovation in Health, Adelaide, SA, 5000, Australia

    Keith McNeil

  7. Flinders University, Bedford Park, SA, 5042, Australia

    Ashley Hopkins & Stephen Bacchi

  8. Royal North Shore Hospital, St Leonard’s, NSW, 2065, Australia

    Aashray Gupta

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  1. Brandon Stretton
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  2. Andrew E. C. Booth
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Corresponding author

Correspondence to Stephen Bacchi.

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Conflict of interest

P.J.P. has received consulting fees from Amgen, Ely Lilly and Esperion and speaker honoraria from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Merck Schering-Plough, Novartis, Novo Nordisk, Pfizer and Sanofi.

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Ethics approval was granted for this project by the Central Adelaide Local Health Network Human Research Ethics Committee (reference number 18686).

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Stretton, B., Booth, A.E.C., Satheakeerthy, S. et al. Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT). Intern Emerg Med (2024). https://doi.org/10.1007/s11739-024-03689-2

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