This article looks at the growing problems in the healthcare revenue cycle, especially the big money losses that come from claim rejections. It emphasizes the need for predictive, real-time analytics to diminish avoidable rejections and improve overall operational efficiency. The novelty of this study lies in the operational integration of a machine-learning–based denial prediction model directly within a real-time Power BI analytics environment, enabling proactive intervention prior to claim submission rather than retrospective denial analysis. This research investigated Medicare and CMS utilization/payment records utilizing Power BI for real-time insights, together with machine-learning models, especially a Python-based Random Forest technique, to forecast high-risk claims. Interactive Power BI dashboards showed predicted results so that decisions could be made quickly. Results show that about 90% of rejections follow patterns that may be predicted. These patterns are generally caused by missing authorizations, code mistakes, or late submissions. Combining predictive analytics with real-time dashboards greatly enhanced revenue performance and cut down on the number of denials, showing that this strategy has demonstrated measurable improvements compared to retrospective denial review approach. This study demonstrates that modern analytics combined with interactive visual tools may establish a proactive denial-prevention ecosystem, benefiting not just healthcare revenue cycle management but also other industries dependent on swift mistake detection.

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