Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT)
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 int...
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| Published in | Internal and emergency medicine Vol. 19; no. 7; pp. 1913 - 1919 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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Springer International Publishing
01.10.2024
Springer Nature B.V |
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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. |
|---|---|
| AbstractList | 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. 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.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. 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. |
| Author | Satheakeerthy, Shrirajh Kovoor, Joshua Tan, Sheryn Evans, Shaun Maddison, John Leslie, Alasdair Akram, Waqas Chan, Weng Onn Zannettino, Andrew Gupta, Aashray Booth, Andrew E. C. McNeil, Keith Gilbert, Toby Stretton, Brandon Howson, Sarah Hopkins, Ashley Psaltis, Peter Bacchi, Stephen Zeitz, Kathryn Teo, Melissa Vanlint, Andrew O’Callaghan, Patrick G. Gluck, Samuel |
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| Cites_doi | 10.1080/02688697.2022.2151565 10.1007/s11739-021-02697-w 10.1007/s11739-021-02816-7 10.1111/ans.18559 10.1186/s13244-023-01541-3 10.1097/HCM.0000000000000284 10.1016/j.surg.2023.08.021 10.1111/ans.18546 10.1186/s12962-021-00322-3 10.1016/j.ijcard.2019.01.046 10.1007/s10729-005-2012-z 10.1002/14651858.CD000313.pub5 10.1016/j.ijge.2012.05.001 10.1111/ans.18263 10.1111/imj.14962 10.1111/imj.16308 10.1136/heart.87.3.216 10.1377/hlthaff.2010.1114 10.1136/bmjopen-2020-044291 10.1111/1742-6723.14395 10.4258/hir.2018.24.2.109 10.5694/j.1326-5377.2009.tb02451.x 10.1136/bmjinnov-2019-000359 10.1007/s10729-010-9128-5 10.1016/j.healthpol.2015.12.003 10.1007/s11739-019-02265-3 |
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| Keywords | Digital health Discharge Implementation Bed flow Machine learning |
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CasagrandaICostantinoGFalavignaGFurlanRIppolitiRArtificial Neural Networks and risk stratification models in emergency departments: the policy maker’s perspectiveHealth Policy2016120111111910.1016/j.healthpol.2015.12.003 StrettonBKovoorJGuptaAHainsLBacchiSWongBO'CallaghanPGBarretoSHughTJMurphyETrochslerMPadburyRBoydMMaddernGGet out what you put in: optimising electronic medical record dataANZ J Surg20239392056205810.1111/ans.18559 MaharlouHKalhoriSShahbaziSRavangardRPredicting length of stay in intensive care units after cardiac surgery: comparison of artificial neural networks and adaptive neuro-fuzzy systemHealthc Inform Res201824210911710.4258/hir.2018.24.2.109 LamLLamABacchiSAbou-HamdenANeurosurgery inpatient outcome prediction for discharge planning with deep learning and transfer learningBr J Neurosurg202210.1080/02688697.2022.2151565 IppolitiRFalavignaGZanelliCBelliniRNumicoGNeural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholdsCost Eff Resour Alloc20211916710.1186/s12962-021-00322-3 VarnavaASedgwickJDeanerARanjadayalanKTimmisARestricted weekend service inappropriately delays discharge after acute myocardial infarctionHeart20028732162191:STN:280:DC%2BD387hsVGjtQ%3D%3D10.1136/heart.87.3.216 Goncalves-BradleyDCLanninNAClemsonLMCameronIDShepperdSDischarge planning from hospitalCochrane Database Syst Rev20161CD00031310.1002/14651858.CD000313.pub5 Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray D, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X (2016) TensorFlow: a system for large-scale machine learning. Proceedings of the 12th USENIX symposium on operating systems design and implementation (OSDI ’16) KleinigOToMSOvendenCDKovoorJGGohRLamLWenzelTTanYHarishHGuptaAKGluckSGilbertTBacchiSVital sign measurements demonstrate terminal digit bias and boundary effectsEmerg Med Australas202410.1111/1742-6723.14395 DB Richardson (3689_CR2) 2009; 190 L Lam (3689_CR14) 2022 AP Brady (3689_CR25) 2024; 15 JG Kovoor (3689_CR29) 2023 R Ippoliti (3689_CR24) 2021; 19 I Casagranda (3689_CR23) 2016; 120 O Kleinig (3689_CR28) 2024 L Cadel (3689_CR3) 2021; 11 3689_CR1 3689_CR18 T Daghistani (3689_CR6) 2019 3689_CR19 JG Kovoor (3689_CR10) 2023 B Stretton (3689_CR26) 2024; 54 3689_CR16 B Stretton (3689_CR27) 2023; 93 H Maharlou (3689_CR7) 2018; 24 C-J Lin (3689_CR5) 2012; 6 MG Seneviratne (3689_CR9) 2020; 6 S Bacchi (3689_CR11) 2020 S Ratnapalan (3689_CR30) 2020; 39 A Varnava (3689_CR17) 2002; 87 A Marshall (3689_CR20) 2005; 8 S Bacchi (3689_CR13) 2021 JG Kovoor (3689_CR15) 2023; 174 E Litvak (3689_CR22) 2011; 30 GW Harrison (3689_CR21) 2010; 13 S Bacchi (3689_CR12) 2021; 16 S Bacchi (3689_CR8) 2022; 52 DC Goncalves-Bradley (3689_CR4) 2016; 1 |
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| SubjectTerms | Aged Algorithms Artificial intelligence Artificial Intelligence - trends Discharge planning Emergency medical care Female Hospitals Humans Im - Original Internal Medicine Length of stay Male Medicine Medicine & Public Health Middle Aged Neural networks Patient Care Team Patient Discharge - statistics & numerical data Patients Time Factors Validation studies |
| Title | Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT) |
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