Using Data Science to Predict Readmissions in Heart Failure

  • Donald U. Apakama
  • Benjamin H. SlovisEmail author
Heart Failure (AM Chang, Section Editor)
Part of the following topical collections:
  1. Heart Failure


Purpose of Review

This review describes the current literature on the use of data science to predict readmissions of patients with heart failure. We examine the chronology of heart failure management from the emergency department, inpatient unit, transition of care, and home care. We examine the software and hardware which may improve readmission rates of this common and complex disease process.

Recent Findings

There are multiple novel applications of data science which have been used to predict readmissions of heart failure patients. In the emergency department, efforts are focused on identifying patients who can be safely discharged after a brief period of stabilization; while inpatient endeavors have attempted to predict those patients at risk for decline after discharge. Overall, prediction rules have had mixed results. Outpatient telemonitoring with invasive devices seems to hold promise. New technologies may be the key to future improvements in readmission rates.


Heart failure holds a high morbidity and mortality, and hospitalizations are common. A number of technological interventions have been developed to prevent readmissions in this complex population. Improvements in technology may lead to reductions in heart failure admissions, reduced mortality, and improved quality of care.


Heart failure Data science Informatics Readmissions Telemonitoring Decision support 


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflicts of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Emergency MedicineThe Icahn School of Medicine at Mount SinaiNew YorkUSA
  2. 2.Department of Emergency MedicineThomas Jefferson UniversityPhiladelphiaUSA

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