Abstract
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.
Original language | English |
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Pages (from-to) | 237-249 |
Number of pages | 13 |
Journal | Information Systems Management |
Volume | 38 |
Issue number | 3 |
DOIs | |
State | Published - 2021 |
Bibliographical note
Publisher Copyright:© 2020 Taylor & Francis.
Funding
This research was supported by a Grant from the GIF(#I-2499- 201.2/2018), the German-Israeli Foundation for Scientific Research and Development.
Funders | Funder number |
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German-Israeli Foundation for Scientific Research and Development |
Keywords
- Applying machine learning
- Hidden Markov Models (HMM)
- congestive heart failure
- readmission
- utilizing predictive analytics