Abstract: Patient payment collection remains a persistent problem that creates inefficiencies, lost dollars, and additional administrative burdens. Traditional practices have not been adequate, and therefore the requirement is for analytics-based approaches towards improving financial performance. This paper addresses the use of analytics for driving optimized patient payment collections through predictive modeling, automation, and business intelligence tools. Key industry trends and operational issues within hospitals and health networks are investigated, supported by real-world applications and case studies demonstrating effective implementation. The study adopts a rigorous methodological framework to model analytical results based on Python because of constraints with accessing confidential patient information. With data-driven solutions, healthcare organizations can enhance transparency in payments, predict payment behavior, and automate collection activities. Besides, AI-powered reminders and automated billing systems increase efficiency and reduce late payments. The article also describes how patient segmentation can be applied to tailor financial assistance programs so that timely payments can be made without compromising patient satisfaction. Lastly, this research highlights the transformative capability of analytics in revenue cycle optimization, providing healthcare providers with actionable insights to increase financial stability and operational efficiency.
Keywords: Predictive Analytics, Automation in Healthcare, Business Intelligence Tools, Revenue Cycle Optimization, Payment Transparency, Patient Segmentation, Automated Billing Solutions.
Title: Enhancing Patient Payment Collections: The Role of Analytics in Healthcare Revenue Optimization
Author: Rohan Desai
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 13, Issue 1, January 2025 - March 2025
Page No: 4-12
Research Publish Journals
Website: www.researchpublish.com
Published Date: 20-February-2025
