Assessing clinical usefulness of readmission risk prediction model
Document Type
Conference Item
Publication Date
1-1-2022
Abstract
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.
Keywords
Cost benefit analysis, Forecasting, Patient treatment, Risk assessment, Adverse events, Comorbidities, Cost saving, Discriminative ability, Health care costs, Performance metrices, Predictive models, Quality of care, Readmission, Risk prediction models, Hospitals
Divisions
biomedengine,sch_ecs
Funders
Universiti Malaya [Grant no. IF015-2021]
Publication Title
IFMBE Proceedings
Volume
86
Publisher
Springer Science and Business Media Deutschland GmbH
Event Title
6th Kuala Lumpur International Conference on Biomedical Engineering, BioMed 2021
Event Location
Virtual, Online
Event Dates
28-29 July 2021
Event Type
conference