Profiling Readmissions Using Hidden Markov Model - the Case of Congestive Heart Failure

Reducing costly hospital readmissions of patients with Congestive Heart Failure (CHF) is important. We analyzed 4,661 CHF patients (from 2007 to 2017) using Hidden Markov Models in order to profile CHF readmission risk over time. This method proved practical in identifying three patient groups with...

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Bibliographic Details
Published in:Information systems management 2021-07, Vol.38 (3), p.237-249
Main Authors: Ben-Assuli, Ofir, Heart, Tsipi, Vest, Joshua R., Ramon-Gonen, Roni, Shlomo, Nir, Klempfner, Robert
Format: Article
Language:English
Subjects:
Applying machine learning
Artificial intelligence
congestive heart failure
Cost analysis
Heart failure
Hidden Markov Models (HMM)
Markov analysis
Markov chains
Patient admissions
Physicians
Predictive analytics
readmission
utilizing predictive analytics
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Summary:Reducing costly hospital readmissions of patients with Congestive Heart Failure (CHF) is important. We analyzed 4,661 CHF patients (from 2007 to 2017) using Hidden Markov Models in order to profile CHF readmission risk over time. This method proved practical in identifying three patient groups with distinctive characteristics, which might guide physicians in tailoring personalized care to prevent hospital readmission. We thus demonstrate how applying appropriate AI analytics can save costs and improve the quality of care.
ISSN:1058-0530
1934-8703
DOI:10.1080/10580530.2020.1847362