Using Natural Language Processing (NLP) to Identify Fraudulent Healthcare Claims

Purpose: This white paper describes the need to enhance fraud detection within healthcare using the methods of Natural Language Processing (NLP) in unstructured text: physician notes, patient records, and claim descriptions. To overcome the limitations of traditional rule-based platforms in handling healthcare’s unstructured data complexity and scale is the objective. Methodology: The proposed approach combines with a well-established pre-trained NLP models (BioBERT and ClinicalBERT) with known methods, such as named entity recognition, anomaly detection, and predictive modeling. A phased approach, as part of the implementation strategy, will be used to implement NLP models for clinical IT environments, from data ingestion and transformation through model deployment and live fraud surveillance. Findings: Based on the studies’ results, NLP systems increase fraud detection accuracy by 30 percent, reduce false positives by 20 percent, and allow claims processing under a second. While the white paper’s innovative offering begins with a proposal for a hybrid solution, which combines NLP-driven text analysis with existing rule-based systems, this combination delivers a stronger and more flexible means of fraud detection. The predictive nature of NLP enables healthcare organizations to identify potential fraud risks for providers before the issues grow worse. Unique Contribution to Theory, Practice and Policy: The paper’s experts call upon IT personnel to lead adopting NLP systems, refresh models to meet new fraud threats, and explore collaboration with federated learning and blockchain to enhance protections and compliance standards. Upon implementing these recommendations, healthcare organization will be able to more effectively deal with fraudulent activities and optimize their workflows more efficiently.