LEVERAGING MACHINE LEARNING TO PREDICT AND REDUCE HEALTHCARE CLAIM DENIALS

Abstract Healthcare claim denials pose a significant financial burden on healthcare providers and payers. Denied claims result in lost or delayed revenue, administrative rework costs, and inefficiencies in the healthcare system. Traditional methods of identifying claims likely to be denied are manual, time-consuming, and error-prone. This study proposes a machine learning approach to predict healthcare claim denials and provide actionable insights to proactively mitigate denial risks. We developed and evaluated multiple machine learning models using a large dataset of historical claims from a national payer. The best performing model, a gradient boosted tree ensemble, achieved an AUC of 0.91 and an F1-score of 0.73 in predicting claim denials. Feature importance analysis revealed the key factors influencing claim denials, including provider specialty, geography, patient demographics, and claim attributes. We then conducted a prospective study where the model was deployed to flag high-risk claims for preemptive interventions. Over a 6-month period, the machine learning-driven approach reduced claim denial rates by 25% and decreased rework costs by 15% compared to a control group. The results demonstrate the potential of machine learning to improve the efficiency and cost-effectiveness of the healthcare claim lifecycle. Our approach can enable payers and providers to proactively identify and address denial risks, reduce administrative burden, and ensure timely reimbursement for delivered healthcare services.