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Assessing clinical usefulness of readmission risk prediction model

Readmission manifests signs of degraded quality of care and increased healthcare cost. Such adverse event may be attributed to premature discharge, unsuccessful treatments, or worsening comorbidities. Predictive modeling provides useful information to identify patients at a higher risk for readmissi...

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Main Authors:	Teo, Kareen, Yong, Ching Wai, Chuah, Joon Huang, Hasikin‬, Khairunnisa, Salim, Maheza Irna Mohd, Hum, Yan Chai, Lai, Khin Wee
Format:	Conference or Workshop Item
Published:	Springer Science and Business Media Deutschland GmbH 2022
Subjects:	R Medicine TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/43429/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129328361&doi=10.1007%2f978-3-030-90724-2_42&partnerID=40&md5=923c8312d80c502365be89bab829a3b7
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id	my.um.eprints.43429
record_format	eprints
spelling	my.um.eprints.434292025-02-12T08:01:49Z http://eprints.um.edu.my/43429/ Assessing clinical usefulness of readmission risk prediction model Teo, Kareen Yong, Ching Wai Chuah, Joon Huang Hasikin‬, Khairunnisa Salim, Maheza Irna Mohd Hum, Yan Chai Lai, Khin Wee R Medicine TK Electrical engineering. Electronics Nuclear engineering Readmission manifests signs of degraded quality of care and increased healthcare cost. Such adverse event may be attributed to premature discharge, unsuccessful treatments, or worsening comorbidities. Predictive modeling provides useful information to identify patients at a higher risk for readmission for targeted interventions. Though many studies have proposed readmission risk predictive models and validated their discriminative ability with performance metrics, few examined the net benefit realized by a predictive model. We compared traditional logistic regression against modern neural network to predict unplanned readmission. An added value of 7 on discriminative ability is observed for modern machine learning model compared to regression. A cost analysis is provided to assist physicians and hospital management for translating the theoretical value into real cost and resource allocation after model implementation. The neural network model is projected to contribute 15× more savings by reducing readmissions. Aside from constructing better performing models, the results of our study demonstrate the potential of a clinically helpful prediction tool in terms of strategies to reduce cost associated with readmission. © 2022, Springer Nature Switzerland AG. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed Teo, Kareen and Yong, Ching Wai and Chuah, Joon Huang and Hasikin‬, Khairunnisa and Salim, Maheza Irna Mohd and Hum, Yan Chai and Lai, Khin Wee (2022) Assessing clinical usefulness of readmission risk prediction model. In: 6th Kuala Lumpur International Conference on Biomedical Engineering, BioMed 2021, 28-29 July 2021, Virtual, Online. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129328361&doi=10.1007%2f978-3-030-90724-2_42&partnerID=40&md5=923c8312d80c502365be89bab829a3b7
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	R Medicine TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	R Medicine TK Electrical engineering. Electronics Nuclear engineering Teo, Kareen Yong, Ching Wai Chuah, Joon Huang Hasikin‬, Khairunnisa Salim, Maheza Irna Mohd Hum, Yan Chai Lai, Khin Wee Assessing clinical usefulness of readmission risk prediction model
description	Readmission manifests signs of degraded quality of care and increased healthcare cost. Such adverse event may be attributed to premature discharge, unsuccessful treatments, or worsening comorbidities. Predictive modeling provides useful information to identify patients at a higher risk for readmission for targeted interventions. Though many studies have proposed readmission risk predictive models and validated their discriminative ability with performance metrics, few examined the net benefit realized by a predictive model. We compared traditional logistic regression against modern neural network to predict unplanned readmission. An added value of 7 on discriminative ability is observed for modern machine learning model compared to regression. A cost analysis is provided to assist physicians and hospital management for translating the theoretical value into real cost and resource allocation after model implementation. The neural network model is projected to contribute 15× more savings by reducing readmissions. Aside from constructing better performing models, the results of our study demonstrate the potential of a clinically helpful prediction tool in terms of strategies to reduce cost associated with readmission. © 2022, Springer Nature Switzerland AG.
format	Conference or Workshop Item
author	Teo, Kareen Yong, Ching Wai Chuah, Joon Huang Hasikin‬, Khairunnisa Salim, Maheza Irna Mohd Hum, Yan Chai Lai, Khin Wee
author_facet	Teo, Kareen Yong, Ching Wai Chuah, Joon Huang Hasikin‬, Khairunnisa Salim, Maheza Irna Mohd Hum, Yan Chai Lai, Khin Wee
author_sort	Teo, Kareen
title	Assessing clinical usefulness of readmission risk prediction model
title_short	Assessing clinical usefulness of readmission risk prediction model
title_full	Assessing clinical usefulness of readmission risk prediction model
title_fullStr	Assessing clinical usefulness of readmission risk prediction model
title_full_unstemmed	Assessing clinical usefulness of readmission risk prediction model
title_sort	assessing clinical usefulness of readmission risk prediction model
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2022
url	http://eprints.um.edu.my/43429/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129328361&doi=10.1007%2f978-3-030-90724-2_42&partnerID=40&md5=923c8312d80c502365be89bab829a3b7
_version_	1825160592725377024
score	13.239859

Assessing clinical usefulness of readmission risk prediction model

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