Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.

<h4>Background</h4>Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine le...

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Autores principales:	Jennifer A Bishop, Hamza A Javed, Rasheed El-Bouri, Tingting Zhu, Thomas Taylor, Tim Peto, Peter Watkinson, David W Eyre, David A Clifton
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/19881aa63b3f4013ab930da573e10bdd
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spelling	oai:doaj.org-article:19881aa63b3f4013ab930da573e10bdd2021-12-02T20:16:11ZImproving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.1932-620310.1371/journal.pone.0260476https://doaj.org/article/19881aa63b3f4013ab930da573e10bdd2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0260476https://doaj.org/toc/1932-6203<h4>Background</h4>Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis.<h4>Methods and performance</h4>Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients' real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models' inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as 'ready for discharge' after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days).<h4>Conclusion</h4>We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.Jennifer A BishopHamza A JavedRasheed El-BouriTingting ZhuThomas TaylorTim PetoPeter WatkinsonDavid W EyreDavid A CliftonPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0260476 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jennifer A Bishop Hamza A Javed Rasheed El-Bouri Tingting Zhu Thomas Taylor Tim Peto Peter Watkinson David W Eyre David A Clifton Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.
description	<h4>Background</h4>Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis.<h4>Methods and performance</h4>Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients' real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models' inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as 'ready for discharge' after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days).<h4>Conclusion</h4>We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.
format	article
author	Jennifer A Bishop Hamza A Javed Rasheed El-Bouri Tingting Zhu Thomas Taylor Tim Peto Peter Watkinson David W Eyre David A Clifton
author_facet	Jennifer A Bishop Hamza A Javed Rasheed El-Bouri Tingting Zhu Thomas Taylor Tim Peto Peter Watkinson David W Eyre David A Clifton
author_sort	Jennifer A Bishop
title	Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.
title_short	Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.
title_full	Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.
title_fullStr	Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.
title_full_unstemmed	Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.
title_sort	improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/19881aa63b3f4013ab930da573e10bdd
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Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.

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