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...

Full description

Saved in:
Bibliographic Details
Published inInformation systems management Vol. 38; no. 3; pp. 237 - 249
Main Authors Ben-Assuli, Ofir, Heart, Tsipi, Vest, Joshua R., Ramon-Gonen, Roni, Shlomo, Nir, Klempfner, Robert
Format Journal Article
LanguageEnglish
Published Boston Taylor & Francis 03.07.2021
Taylor & Francis Ltd
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
Online AccessGet full text

Cover

More Information
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