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. 2024 Oct;19(7):1913-1919.
doi: 10.1007/s11739-024-03689-2. Epub 2024 Jun 22.

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

Brandon Stretton  1   2   3   4 , Andrew E C Booth  2   4 , Shrirajh Satheakeerthy  2   4 , Sarah Howson  2   4 , Shaun Evans  2   3   4 , Joshua Kovoor  3   5 , Waqas Akram  1 , Keith McNeil  6 , Ashley Hopkins  7 , Kathryn Zeitz  2   4 , Alasdair Leslie  2   4 , Peter Psaltis  2   3   4 , Aashray Gupta  8 , Sheryn Tan  3 , Melissa Teo  1   2 , Andrew Vanlint  1   2 , Weng Onn Chan  2   3   4 , Andrew Zannettino  3 , Patrick G O'Callaghan  2   4 , John Maddison  1   2 , Samuel Gluck  1   2   3 , Toby Gilbert  1   2   3 , Stephen Bacchi  9   10   11   12
Affiliations

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

Brandon Stretton et al. Intern Emerg Med. 2024 Oct.

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.

Keywords: Bed flow; Digital health; Discharge; Implementation; Machine learning.

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