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 | , , , , , , , , , , , , , , , , , , , , , , |
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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. |
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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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References | KovoorJGBacchiSGuptaAKStrettonBNannSDAujayebNLuANathinKLamLJiangMLeeSToMSOvendenCDHewittJNGohRGluckSReidJLKhuranaSDobbinsCHewettPJPadburyRTMalychaJTrochslerMIHughTJMaddernGJSurgery’s rosetta stone: natural language processing to predict discharge and readmission after general surgerySurgery202317461309131410.1016/j.surg.2023.08.021 BradyAPAllenBChongJKotterEKottlerNMonganJOakden-RaynerLDos SantosDPTangAWaldCSlavotinekJDeveloping, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR RANZCR & RSNAInsights Imaging20241511610.1186/s13244-023-01541-3 DaghistaniTElshawiRSakrSAhmedAAl-ThwayeeAAl-MallahMPredictors of in-hospital length of stay among cardiac patients: a machine learning approachInt J Cardiol201910.1016/j.ijcard.2019.01.046 LitvakEBisognanoMMore patients, less payment: increasing hospital efficiency in the aftermath of health reformHealth Aff2011301768010.1377/hlthaff.2010.1114 HarrisonGWEscobarGJLength of stay and imminent discharge probability distributions from multistage models: variation by diagnosis, severity of illness, and hospitalHealth Care Manag Sci201013326827910.1007/s10729-010-9128-5 CadelLGuilcherSJTKokoreliasKMSutherlandJGlasbyJKiranTKuluskiKInitiatives for improving delayed discharge from a hospital setting: a scoping reviewBMJ Open202111210.1136/bmjopen-2020-044291 RichardsonDBMountainDMyths versus facts in emergency department overcrowding and hospital access blockMed J Aust2009190736937410.5694/j.1326-5377.2009.tb02451.x KovoorJGBacchiSGuptaAKStrettonBMalychaJReddiBALiewDO'CallaghanPGBeltrameJFZannettinoACJonesKLHorowitzMDobbinsCHewettPJTrochslerMIMaddernGJThe adelaide score: an artificial intelligence measure of readiness for discharge after general surgeryANZ J Surg202310.1111/ans.18546 RatnapalanSLangDHealth care organizations as complex adaptive systemsHealth Care Manag (Frederick)2020391182310.1097/HCM.0000000000000284 BacchiSTanYOakden-RaynerLJannesJKleinigTKoblarSMachine learning in the prediction of medical inpatient length of stayIntern Med J202252217618510.1111/imj.14962 MarshallAVasilakisCEl-DarziELength of stay-based patient flow models: recent developments and future directionsHealth Care Manag Sci20058321322010.1007/s10729-005-2012-z BacchiSGilbertTGluckSChengJTanYChimIJannesJKleinigTKoblarSDaily estimates of individual discharge likelihood with deep learning natural language processing in general medicine: a prospective and external validation studyIntern Emerg Med202110.1007/s11739-021-02816-7 LinC-JChengS-JShihS-CChuC-HTjungJ-JDischarge planningInt J Gerontol20126423724010.1016/j.ijge.2012.05.001 SeneviratneMGShahNHChuLBridging the implementation gap of machine learning in healthcareBMJ Innov202062454710.1136/bmjinnov-2019-000359 StrettonBKovoorJGHainsLKleinigOTanSGuptaAKIttimaniMDwyerAMcNeilKChanWCusackMO'CallaghanPGMaddisonJBacchiSHow will the artificial intelligence algorithm work within the constraints of this healthcare system?Intern Med J202454119019110.1111/imj.16308 Australian Medical Association (2021) Public hospitals: cycle of crisis BacchiSGluckSTanYChimIChengJGilbertTJannesJKleinigTKoblarSMixed-data deep learning in repeated predictions of general medicine length of stay: a derivation studyIntern Emerg Med20211661613161710.1007/s11739-021-02697-w KovoorJGBacchiSGuptaAKO'CallaghanPGAbou-HamdenAMaddernGJArtificial intelligence clinical trials and critical appraisal: a necessityANZ J Surg202310.1111/ans.18263 BacchiSGluckSTanYChimIChengJGilbertTMenonDKJannesJKleinigTKoblarSPrediction of general medical admission length of stay with natural language processing and deep learning: a pilot studyIntern Emerg Med202010.1007/s11739-019-02265-3 SA Health (2023) Non-Medicare and Long Stay Nursing Home Patient Fees. Government of South Australia. https://www.sahealth.sa.gov.au/wps/wcm/connect/public+content/sa+health+internet/services/hospitals/going+to+hospital+what+to+know+and+expect/non-medicare+and+long+stay+nursing+home+patient+fees. Rix E (2022) Fixing weekend discharge key to improving capacity at South Australia's hospitals. Australian Broadcasting Corporation. https://www.abc.net.au/news/2022-08-13/why-monday-is-the-worst-day-for-south-australian-hospitals/101330274. 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 |
References_xml | – year: 2022 ident: 3689_CR14 publication-title: Br J Neurosurg doi: 10.1080/02688697.2022.2151565 contributor: fullname: L Lam – volume: 16 start-page: 1613 issue: 6 year: 2021 ident: 3689_CR12 publication-title: Intern Emerg Med doi: 10.1007/s11739-021-02697-w contributor: fullname: S Bacchi – year: 2021 ident: 3689_CR13 publication-title: Intern Emerg Med doi: 10.1007/s11739-021-02816-7 contributor: fullname: S Bacchi – volume: 93 start-page: 2056 issue: 9 year: 2023 ident: 3689_CR27 publication-title: ANZ J Surg doi: 10.1111/ans.18559 contributor: fullname: B Stretton – volume: 15 start-page: 16 issue: 1 year: 2024 ident: 3689_CR25 publication-title: Insights Imaging doi: 10.1186/s13244-023-01541-3 contributor: fullname: AP Brady – volume: 39 start-page: 18 issue: 1 year: 2020 ident: 3689_CR30 publication-title: Health Care Manag (Frederick) doi: 10.1097/HCM.0000000000000284 contributor: fullname: S Ratnapalan – volume: 174 start-page: 1309 issue: 6 year: 2023 ident: 3689_CR15 publication-title: Surgery doi: 10.1016/j.surg.2023.08.021 contributor: fullname: JG Kovoor – year: 2023 ident: 3689_CR29 publication-title: ANZ J Surg doi: 10.1111/ans.18546 contributor: fullname: JG Kovoor – volume: 19 start-page: 67 issue: 1 year: 2021 ident: 3689_CR24 publication-title: Cost Eff Resour Alloc doi: 10.1186/s12962-021-00322-3 contributor: fullname: R Ippoliti – year: 2019 ident: 3689_CR6 publication-title: Int J Cardiol doi: 10.1016/j.ijcard.2019.01.046 contributor: fullname: T Daghistani – volume: 8 start-page: 213 issue: 3 year: 2005 ident: 3689_CR20 publication-title: Health Care Manag Sci doi: 10.1007/s10729-005-2012-z contributor: fullname: A Marshall – volume: 1 start-page: CD000313 year: 2016 ident: 3689_CR4 publication-title: Cochrane Database Syst Rev doi: 10.1002/14651858.CD000313.pub5 contributor: fullname: DC Goncalves-Bradley – ident: 3689_CR1 – volume: 6 start-page: 237 issue: 4 year: 2012 ident: 3689_CR5 publication-title: Int J Gerontol doi: 10.1016/j.ijge.2012.05.001 contributor: fullname: C-J Lin – year: 2023 ident: 3689_CR10 publication-title: ANZ J Surg doi: 10.1111/ans.18263 contributor: fullname: JG Kovoor – volume: 52 start-page: 176 issue: 2 year: 2022 ident: 3689_CR8 publication-title: Intern Med J doi: 10.1111/imj.14962 contributor: fullname: S Bacchi – ident: 3689_CR19 – volume: 54 start-page: 190 issue: 1 year: 2024 ident: 3689_CR26 publication-title: Intern Med J doi: 10.1111/imj.16308 contributor: fullname: B Stretton – volume: 87 start-page: 216 issue: 3 year: 2002 ident: 3689_CR17 publication-title: Heart doi: 10.1136/heart.87.3.216 contributor: fullname: A Varnava – volume: 30 start-page: 76 issue: 1 year: 2011 ident: 3689_CR22 publication-title: Health Aff doi: 10.1377/hlthaff.2010.1114 contributor: fullname: E Litvak – volume: 11 issue: 2 year: 2021 ident: 3689_CR3 publication-title: BMJ Open doi: 10.1136/bmjopen-2020-044291 contributor: fullname: L Cadel – year: 2024 ident: 3689_CR28 publication-title: Emerg Med Australas doi: 10.1111/1742-6723.14395 contributor: fullname: O Kleinig – volume: 24 start-page: 109 issue: 2 year: 2018 ident: 3689_CR7 publication-title: Healthc Inform Res doi: 10.4258/hir.2018.24.2.109 contributor: fullname: H Maharlou – ident: 3689_CR16 – volume: 190 start-page: 369 issue: 7 year: 2009 ident: 3689_CR2 publication-title: Med J Aust doi: 10.5694/j.1326-5377.2009.tb02451.x contributor: fullname: DB Richardson – ident: 3689_CR18 – volume: 6 start-page: 45 issue: 2 year: 2020 ident: 3689_CR9 publication-title: BMJ Innov doi: 10.1136/bmjinnov-2019-000359 contributor: fullname: MG Seneviratne – volume: 13 start-page: 268 issue: 3 year: 2010 ident: 3689_CR21 publication-title: Health Care Manag Sci doi: 10.1007/s10729-010-9128-5 contributor: fullname: GW Harrison – volume: 120 start-page: 111 issue: 1 year: 2016 ident: 3689_CR23 publication-title: Health Policy doi: 10.1016/j.healthpol.2015.12.003 contributor: fullname: I Casagranda – year: 2020 ident: 3689_CR11 publication-title: Intern Emerg Med doi: 10.1007/s11739-019-02265-3 contributor: fullname: S Bacchi |
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Snippet | Weekend discharges occur less frequently than discharges on weekdays, contributing to hospital congestion. Artificial intelligence algorithms have previously... |
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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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