Healthcare Fraud Detection: The Critical Role of Data Quality and Consistency (Published)
Healthcare fraud detection has evolved significantly with the advancement of technology and data analytics capabilities. The effectiveness of these sophisticated detection systems fundamentally depends on the quality and consistency of underlying data. Organizations face multiple challenges in maintaining high-quality data across operations, including inconsistent coding practices, incomplete information, and integration complexities. The implementation of comprehensive data quality initiatives has demonstrated substantial improvements in fraud detection accuracy and operational efficiency. Healthcare providers implementing robust data governance frameworks, standardization practices, and advanced analytics have experienced significant reductions in false positives and investigation times. The integration of artificial intelligence and machine learning capabilities has further enhanced the ability to identify complex fraud patterns while improving processing efficiency. Continuous monitoring and improvement of data quality serve as critical factors in strengthening fraud detection capabilities and ensuring financial integrity across healthcare organizations.
Keywords: Healthcare fraud detection, artificial intelligence in healthcare, data quality management, fraud prevention systems, healthcare data governance