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Introduction: Population health management – and specifically chronic disease management – depend on the ability of providers to identify patients at high risk of developing costly and harmful conditions such as diabetes, heart failure, and chronic kidney disease (CKD). The advent of big data analytics could help identify high-risk patients which is really beneficial to healthcare practitioners and patients to make informed decisions in a timelier manner with much more evidence in hand. It would allow doctors to extend effective treatment but also reduces the costs of extending improved care to patients.

Purpose: The purpose of this study was to identify current applications of big data analytics in healthcare for chronic disease management and to determine its real-world effectiveness in improving patient outcomes and lessening financial burdens.

Methodology: The methodology for this study was a literature review. Six electronic databases were utilized and a total of 49 articles were referenced for this research.

Results: Improvement in diagnostic accuracy and risk prediction and reduction of hospital readmissions has resulted in significant decrease in health care cost. Big data analytic studies regarding care management and wellness programs have been largely positive. Also, Big data analytics guided better treatment leading to improved patient outcomes.

Discussion/Conclusion: Big data analytics shows initial positive impact on quality of care, patient outcomes and finances, and could be successfully implemented in chronic disease management.


This is the authors' manuscript. The version of record is available from the publisher at Copyright © 2018 Wolters Kluwer Health. All rights reserved.