The Digital Evolution of the Healthcare Revenue Cycle: AI and Analytics at the Forefront
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Research Paper | Health and Medical Sciences | United States of America | Volume 14 Issue 3, March 2025 | Popularity: 5.4 / 10


     

The Digital Evolution of the Healthcare Revenue Cycle: AI and Analytics at the Forefront

Saifuddin Shaik Mohammed


Abstract: The healthcare revenue cycle is undergoing a digital transformation due to recent advancements in data analytics and generative AI. These technologies help automate routine financial workflows, improve claim processing, and make a data-driven approach to improve efficiency and reduce revenue leakage. Data analytics helps make predictive insights that help identify the trends and issues healthcare providers face within their organization's revenue cycle. Meanwhile, generative AI automates financial workflows, documentation, and coding and reduces administrative burdens, which leads to better efficiency. Though there are a lot of benefits, there are still a few challenges, such as data privacy, regulatory compliance, and the need for strong AI governance. This paper explores how data analytics and generative AI in revenue cycle management lead to a better digital transformation, probing their impact on financial performance, operational efficiency, and patient experience.


Keywords: Healthcare Revenue Cycle Management, Generative AI, Data Analytics, Financial Optimization, AI in Healthcare


Edition: Volume 14 Issue 3, March 2025


Pages: 1178 - 1182


DOI: https://www.doi.org/10.21275/SR25324011728


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Saifuddin Shaik Mohammed, "The Digital Evolution of the Healthcare Revenue Cycle: AI and Analytics at the Forefront", International Journal of Science and Research (IJSR), Volume 14 Issue 3, March 2025, pp. 1178-1182, https://www.ijsr.net/getabstract.php?paperid=SR25324011728, DOI: https://www.doi.org/10.21275/SR25324011728

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