Leveraging Predictive Analytics to Minimize Claim Denials in Healthcare Revenue CycleManagement

Abstract

This paper explores the use of predictive analytics in healthcare Revenue Cycle Management (RCM) to decrease claim denials, which are detrimental to the financial and operational efficiency of healthcare organizations. Employing logistic regression, decision trees, and neural networks, the research focuses on identifying patterns that lead to claim denials, allowing for early corrective actions. The methodology involves detailed data preprocessing, feature selection to pinpoint
relevant variables affecting claim denials, and the careful development and evaluation of predictive models. Analysis of a real-world dataset confirms that predictive analytics can effectively identify claims at risk of denial before they are submitted, significantly reducing denial rates and streamlining billing processes. The findings highlight the potential of analytics to improve the accuracy of claim submissions, thus enhancing financial health and operational efficiency in healthcare organizations and advocating for the integration of advanced analytics to tackle longstanding issues in healthcare RCM.