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Research and review articles are invited for publication in September - October 2026 (Volume 19, Issue 1) Submit manuscript

Reducing hidden revenue losses in healthcare through predictive financial analytics

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  • Reducing hidden revenue losses in healthcare through predictive financial analytics

Ejiofor Chukwuelue *

Business and Analytics, Hult International Business School, USA.
Review Article
Magna Scientia Advanced Biology and Pharmacy, 2025, 16(02), 034-049
Article DOI: 10.30574/msabp.2025.16.2.0074
DOI url: https://doi.org/10.30574/msabp.2025.16.2.0074
Received 27 October 2025; revised on 06 December 2025; accepted on 08 December 2025
Healthcare organizations increasingly operate under tightening financial margins, complex reimbursement structures, and rising administrative burdens. These pressures expose institutions to substantial hidden revenue losses leakages that often go undetected within fragmented billing workflows, delayed claims processing, coding inconsistencies, authorization failures, and missed charge capture opportunities. Traditional financial oversight methods, which rely heavily on retrospective audits and manual review, struggle to identify subtle, recurring patterns of financial waste embedded in large, dynamic datasets. As a result, hospitals face preventable revenue erosion that directly impacts financial stability, operational capacity, and long-term sustainability. Predictive financial analytics offers a transformative approach to this challenge by leveraging machine learning models, anomaly-detection engines, and real-time data integration to uncover early indicators of revenue leakage. These tools analyze high-granularity financial and clinical data streams to detect coding drift, claims denial predictors, reimbursement variances, and patterns of operational inefficiency that traditional methods overlook. Predictive models also enable scenario simulation, forecasting, and prioritization of high-risk encounters, allowing financial teams to intervene before revenue is lost. When embedded into revenue cycle management workflows, predictive analytics supports automated alerts, intelligent work queues, and proactive decision support for coders, billers, and financial administrators. This integration narrows the gap between clinical documentation and financial outcomes, improves reimbursement accuracy, accelerates claims resolution, and strengthens compliance. Ultimately, predictive financial analytics shifts healthcare revenue management from reactive troubleshooting to continuous, data-driven optimization, providing organizations with the strategic intelligence necessary to safeguard revenue, enhance financial resilience, and support sustainable patient care delivery.
Predictive Financial Analytics; Revenue Cycle Optimization; Healthcare Financial Management; Machine Learning in Healthcare Finance; Claims Denial Prediction; Revenue Leakage Detection
https://msabp.magnascientiapub.com/sites/default/files/fulltext_pdf/MSABP-2025-…

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Ejiofor Chukwuelue. Reducing hidden revenue losses in healthcare through predictive financial analytics. Magna Scientia Advanced Biology and Pharmacy, 2025, 16(2), 034-049. Article DOI: https://doi.org/10.30574/msabp.2025.16.2.0074

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