The Impact of Big Data and Predictive Analytics on U.S. Healthcare Delivery: Opportunities, Challenges, and Future Directions
1 Saint Louis University School of Professional Studies, Department of Information Systems, Saint Louis, Missouri, United States.
2 Saint Louis University School of Medicine, Department of Health and Clinical Outcomes Research, Saint Louis, Missouri, United States.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(01), 2275–2287
Article DOI: 10.30574/wjarr.2024.24.1.3266
Publication history:
Received on 15 September 2024; revised on 23 October 2024; accepted on 26 October 2024
Abstract:
Healthcare, like many other industries, has been significantly influenced by big data and predictive analytics. The vast volume, velocity, and variety of information within big data sets have transformed the way we approach patient care and medical innovation. From predicting disease outbreaks to delivering personalized treatment plans, big data analytics offers immense potential for revolutionizing healthcare. This review explores the significant impact of big data and predictive analytics on the U.S. healthcare system. It delves into hospitals' ability to effectively leverage complex information and assess the potential benefits they may reap from successful implementation. The introduction defines big data and predictive analytics in the context of healthcare and outlines the paper's objectives. It further discusses the evolution of big data and analytics in healthcare, its key applications, and the challenges it presents. Additionally, we explore potential solutions and ethical considerations surrounding big data analytics in healthcare. Overall, this paper underscores the transformative power of big data and predictive analytics in revolutionizing U.S. healthcare delivery and improving patient outcomes.
Keywords:
Big Data; Predictive Analytics; Healthcare; Machine Learning; Artificial Intelligence; Patient outcomes.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0