TY - JOUR
T1 - The Adelaide Score
T2 - prospective implementation of an artificial intelligence system to improve hospital and cost efficiency
AU - Kovoor, Joshua G.
AU - Stretton, Brandon
AU - Gupta, Aashray K.
AU - Beath, Alexander
AU - Jacob, Mathew O.
AU - Kefalianos, John M.
AU - Carmichael, Gavin J.
AU - Zaka, Ammar
AU - O'Callaghan, Gerry
AU - Satheakeerthy, Shrirajh
AU - Booth, Andrew
AU - Delloso, Thomson
AU - Hugh, Thomas J.
AU - Chan, Weng Onn
AU - Maddern, Guy J.
AU - Balan-Vnuk, Eva
AU - Cusack, Michael
AU - Gilbert, Toby
AU - Maddison, John
AU - Bacchi, Stephen
AU - the Adelaide Score Advisory Group
AU - Abou-Hamden, Amal
AU - Al-Saffar, Alex
AU - Al-Sharea, Annas
AU - Arachi, Vasiliki
AU - Arafat, Yasser
AU - Arnold, Matthew
AU - Asmussen, Karl
AU - Ataie, Sara
AU - Ataie, Zahra
AU - Balogh, Zsolt
AU - Barreto, S. George
AU - Bastiampillai, Tarun
AU - Beltrame, John
AU - Bennetts, Jayme
AU - Bessen, Taryn
AU - Bhimani, Nazim
AU - Bidargaddi, Niranjan
AU - Blum, Joshua
AU - Bookun, Riteesh
AU - Bruening, Martin
AU - Canny, Ben
AU - Chan, Erick
AU - Chan, Justin
AU - Cherini, Jacob
AU - Chik, William
AU - Chow, Clara
AU - Clarke, Edward
AU - Clarke, Jonathan
AU - Clay-Williams, Robyn
AU - Dicus, Elena
AU - Dobbins, Christopher
AU - Donnelly, Francis
AU - Dykes, Lukah
AU - Edwards, Suzanne
AU - Eranki, Aditya
AU - Flint, Sarah
AU - Flower, Jason
AU - Freeman, Kerrie
AU - French, Tim
AU - Gluck, Samuel
AU - Godber, Harry
AU - Goh, Rudy
AU - Gould, Paul
AU - Hansra, Amandeep
AU - Hewett, Peter
AU - Hodgson, Russell
AU - Hopkins, Ashley
AU - Horowitz, Michael
AU - Howson, Sarah
AU - Ittimani, Mana
AU - Jacobsen, Jonathan Henry
AU - Jenkins, Alexander
AU - Jersmann, Hubertus
AU - Jiang, Melinda
AU - Jones, Karen
AU - Kaepelli, Reto
AU - Kamalanathan, Harish
AU - Kennington, Billy
AU - Kirkpatrick, Emily
AU - Kovoor, Pramesh
AU - Kuany, Thiep
AU - Loan, Antony
AU - Leslie, Alasdair
AU - Liew, Danny
AU - Litwin, Peter
AU - Luo, Yuchen
AU - Mahajan, Rajiv
AU - Marks, Jarrod
AU - Marshall-Webb, Matthew
AU - McIntyre, Daniel
AU - Muston, Ben
AU - Mutahar, Daud
AU - Nann, Silas
AU - Nematzadeh, Nasim
AU - Ng, Dominic
AU - Padbury, Robert
AU - Patel, Meet
AU - Pleass, Henry
AU - Porter, Anthony
AU - Psaltis, Peter
AU - Ramponi, Fabio
AU - Rogers, Wendy
AU - Royse, Alistair
AU - Schnitzler, Margaret
AU - Seda, Veronika
AU - Sherbon, Tony
AU - Short, Rachel
AU - Singh, Rajvinder
AU - Sivagangabalan, Gopal
AU - Smallbone, Harry
AU - Smith, Julian
AU - Subramaniam, Peter
AU - Sutton, Liz
AU - Tan, Ian
AU - Tan, Sheryn
AU - Teo, Melissa
AU - Thiagalingam, Aravinda
AU - Tivey, David
AU - To, Minh Son
AU - Trochsler, Markus
AU - Turner, Richard
AU - Tyagi, Daksh
AU - Van Den Hengel, Anton
AU - Vanlint, Andrew
AU - Verghese, Santosh
AU - Warren, Leigh
AU - Wilson-Smith, Ashley
AU - Wong, Geoffrey
AU - Zaman, Sarah
AU - Zannettino, Andrew
AU - Zeitz, Kathryn
PY - 2025/1/3
Y1 - 2025/1/3
N2 - Background: The Adelaide Score is an artificial intelligence system that integrates objective vital signs and laboratory tests to predict likelihood of hospital discharge. Methods: A prospective implementation trial was conducted at the Lyell McEwin Hospital in South Australia. The Adelaide Score was added to existing human, artificial intelligence, and other technological infrastructure for the first 28 days of April 2024 (intervention), and outcomes were compared using parametric, non-parametric and health economic analyses, to those in the first 28 days of April 2023 (control). Artificial intelligence evaluated inpatients admitted under 18 surgical and medical teams, and patients of high likelihood of discharge were provided, on working shifts between Thursday to Sunday, to the Supportive Weekend Interprofessional Flow Team (SWIFT) comprising a senior nurse and pharmacist. Results: Two thousand nine hundred and sixty-eight admissions were included across intervention and control periods. Relative to the control group, use of the Adelaide Score in the intervention group resulted in significantly shorter median length of stay (3.1 versus 2.9 days, P = 0.028) and significantly lower seven-day readmission rate (7.1 versus 5.0%, p = 0.02). The 0.2 bed-day reduction in median length of stay produced a cost saving of $735 708.60 across the 28-day period, or $9 564 211.80 across a 52-week year. There was no significant difference between intervention and control groups in median length of stay for patients discharged on weekends, in-hospital mortality, or discharge to non-home destinations. Conclusions: The prospective implementation of the Adelaide Score was associated with improved hospital and cost efficiency, alongside lower readmissions, for patients across surgical and medical services.
AB - Background: The Adelaide Score is an artificial intelligence system that integrates objective vital signs and laboratory tests to predict likelihood of hospital discharge. Methods: A prospective implementation trial was conducted at the Lyell McEwin Hospital in South Australia. The Adelaide Score was added to existing human, artificial intelligence, and other technological infrastructure for the first 28 days of April 2024 (intervention), and outcomes were compared using parametric, non-parametric and health economic analyses, to those in the first 28 days of April 2023 (control). Artificial intelligence evaluated inpatients admitted under 18 surgical and medical teams, and patients of high likelihood of discharge were provided, on working shifts between Thursday to Sunday, to the Supportive Weekend Interprofessional Flow Team (SWIFT) comprising a senior nurse and pharmacist. Results: Two thousand nine hundred and sixty-eight admissions were included across intervention and control periods. Relative to the control group, use of the Adelaide Score in the intervention group resulted in significantly shorter median length of stay (3.1 versus 2.9 days, P = 0.028) and significantly lower seven-day readmission rate (7.1 versus 5.0%, p = 0.02). The 0.2 bed-day reduction in median length of stay produced a cost saving of $735 708.60 across the 28-day period, or $9 564 211.80 across a 52-week year. There was no significant difference between intervention and control groups in median length of stay for patients discharged on weekends, in-hospital mortality, or discharge to non-home destinations. Conclusions: The prospective implementation of the Adelaide Score was associated with improved hospital and cost efficiency, alongside lower readmissions, for patients across surgical and medical services.
KW - artificial intelligence
KW - cost saving
KW - efficiency
KW - the Adelaide score
UR - http://www.scopus.com/inward/record.url?scp=85214108474&partnerID=8YFLogxK
U2 - 10.1111/ans.19383
DO - 10.1111/ans.19383
M3 - Article
AN - SCOPUS:85214108474
SN - 1445-1433
JO - ANZ Journal of Surgery
JF - ANZ Journal of Surgery
ER -