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Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals

Singapore is one of the first known countries to implement an individual-centric discharge process across all public hospitals to manage frequent admissions—a perennial challenge for public healthcare, especially in an aging population. Specifically, the process provides daily lists of high-risk pat...

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Autores principales: Ng, Reuben, Tan, Kelvin Bryan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8393960/
https://www.ncbi.nlm.nih.gov/pubmed/34444448
http://dx.doi.org/10.3390/ijerph18168700
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author Ng, Reuben
Tan, Kelvin Bryan
author_facet Ng, Reuben
Tan, Kelvin Bryan
author_sort Ng, Reuben
collection PubMed
description Singapore is one of the first known countries to implement an individual-centric discharge process across all public hospitals to manage frequent admissions—a perennial challenge for public healthcare, especially in an aging population. Specifically, the process provides daily lists of high-risk patients to all public hospitals for customized discharge procedures within 24 h of admission. We analyzed all public hospital admissions (N = 150,322) in a year. Among four models, the gradient boosting machine performed the best (AUC = 0.79) with a positive predictive value set at 70%. Interestingly, the cumulative length of stay (LOS) in the past 12 months was a stronger predictor than the number of previous admissions, as it is a better proxy for acute care utilization. Another important predictor was the “number of days from previous non-elective admission”, which is different from previous studies that included both elective and non-elective admissions. Of note, the model did not include LOS of the index admission—a key predictor in other models—since our predictive model identified frequent admitters for pre-discharge interventions during the index (current) admission. The scientific ingredients that built the model did not guarantee its successful implementation—an “art” that requires the alignment of processes, culture, human capital, and senior management sponsorship. Change management is paramount, otherwise data-driven health policies, no matter how well-intended, may not be accepted or implemented. Overall, our study demonstrated the viability of using artificial intelligence (AI) to build a near real-time nationwide prediction tool for individual-centric discharge, and the critical factors for successful implementation.
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spelling pubmed-83939602021-08-28 Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals Ng, Reuben Tan, Kelvin Bryan Int J Environ Res Public Health Article Singapore is one of the first known countries to implement an individual-centric discharge process across all public hospitals to manage frequent admissions—a perennial challenge for public healthcare, especially in an aging population. Specifically, the process provides daily lists of high-risk patients to all public hospitals for customized discharge procedures within 24 h of admission. We analyzed all public hospital admissions (N = 150,322) in a year. Among four models, the gradient boosting machine performed the best (AUC = 0.79) with a positive predictive value set at 70%. Interestingly, the cumulative length of stay (LOS) in the past 12 months was a stronger predictor than the number of previous admissions, as it is a better proxy for acute care utilization. Another important predictor was the “number of days from previous non-elective admission”, which is different from previous studies that included both elective and non-elective admissions. Of note, the model did not include LOS of the index admission—a key predictor in other models—since our predictive model identified frequent admitters for pre-discharge interventions during the index (current) admission. The scientific ingredients that built the model did not guarantee its successful implementation—an “art” that requires the alignment of processes, culture, human capital, and senior management sponsorship. Change management is paramount, otherwise data-driven health policies, no matter how well-intended, may not be accepted or implemented. Overall, our study demonstrated the viability of using artificial intelligence (AI) to build a near real-time nationwide prediction tool for individual-centric discharge, and the critical factors for successful implementation. MDPI 2021-08-17 /pmc/articles/PMC8393960/ /pubmed/34444448 http://dx.doi.org/10.3390/ijerph18168700 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ng, Reuben
Tan, Kelvin Bryan
Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals
title Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals
title_full Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals
title_fullStr Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals
title_full_unstemmed Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals
title_short Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals
title_sort implementing an individual-centric discharge process across singapore public hospitals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8393960/
https://www.ncbi.nlm.nih.gov/pubmed/34444448
http://dx.doi.org/10.3390/ijerph18168700
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