Intensifying The Legitimate and Medicalclaims Using Sophisticated Nlp Techniques

Abstract

Fraud in insurance and healthcare claims has become a major issue for insurance companies and medical insurers alike, causing a number of complex challenges during the claim processing, deferred reimbursement and financial loss. Common claim validation systems, based on rules, are inadequate to detect fraud in unstructured clinical text. In order to improve the accuracy and reliability of claim verification, this paper proposes an intelligent medical claim validation framework, which combines the Natural Language Processing (NLP), Transformer-based language models, Graph Attention Networks (GATs), and ensemble learning. The unstructured clinical documents like physician clinical notes, discharge summaries, diagnostic reports, insurance claim forms, etc. go through the following steps of first pre-processing: text pre-processing, tokenization, normalization and medical entity extraction. Medical Event Graph is constructed based on medical entities and relationships by embedding them semantically in the context with the help of the domain-specific transformer models: ClinicalBERT and BioBERT. Graph Attention Network is a method for finding abnormal treatment sequences, suspicious billing sequences and medical event-related semantic contradictions in a graph structure. It is used in conjunction with graph-based representation to combine the inputs into an ensemble classifier to provide credibility score and classify claims as legitimate, suspicious and fraudulent. The experimental results successfully demonstrate that the proposed framework is more accurate, precise and retrieves more instances than the classical machine learning/rule-based approach and also has less number of false positive prediction. The proposed solution is scalable, intelligent and explainable making it suitable for healthcare organizations and insurance companies to enhance the validation of claims, detect frauds, audit claims effectively and make effective decisions on claims in a lesser time.