Financial data analytics in healthcare: A review of approaches to improve efficiency and reduce costs
1 Department of Business Administration, Texas A&M University Commerce, Texas, USA.
2 Etihuku Pty Ltd, Midrand, Gauteng, South Africa.
Review
Open Access Research Journal of Science and Technology, 2024, 12(02), 010–019.
Article DOI: 10.53022/oarjst.2024.12.2.0129
Publication history:
Received on 25 September 2024; revised on 01 November 2024; accepted on 04 November 2024
Abstract:
This review explores the role of financial data analytics in healthcare, focusing on its potential to improve operational efficiency and reduce costs. It examines current approaches such as predictive analytics, machine learning, and artificial intelligence, highlighting how these tools are used in areas like cost management, resource allocation, and revenue cycle optimization. While financial data analytics offers numerous benefits, including better decision-making and enhanced resource utilization, several challenges persist, such as data privacy concerns, system integration issues, high technology costs, and a shortage of skilled personnel. The paper also identifies opportunities for future advancements, including the adoption of cloud-based analytics, improved interoperability, and enhanced workforce training. The review concludes with recommendations for addressing these challenges and maximizing the impact of financial data analytics to promote more efficient, cost-effective healthcare delivery.
Keywords:
Financial data analytics; Healthcare efficiency; Cost reduction; Predictive analytics; Machine learning
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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