Next-Gen Healthcare Claims Optimization: DL-Based ResAttBiL Integrated with CDC, Modular Design, and Data Observability

Legacy healthcare claims systems modernization to an intelligent Medical Claims Engine (MCE) responds to increasing complexity of healthcare data and operational needs. This modular architecture contains six primary domains: claim acquisition, data preprocessing, feature engineering, Residual Attention-Bidirectional Long Short Term Memory (ResAtt-BiL) modelling, storage in Amazon Web Services (AWS) S3 cloud, and analytics reporting. Each module is designed to improve workflows, increase data integrity, and improve predictive accuracy. The ResAtt-BiL model combines the deep feature extraction strengths of ResNet, the bidirectional sequence learning of BiLSTM and attention to focus on relevant data, and provides strong classification and fraud detection. Amazon Web Services (AWS) S3 cloud storage allows for scalable, encrypted, highly available cloud storage, while integrating seamlessly and ensuring data compliance. Analytics reporting converts data into consumable insights, by converting transaction data into real-time dashboards and forecasting models. Real-time telemetry and Change Data Capture (CDC) improving observability of the data and will help maintain its freshness. Continuous Integration/ Continuous Development (CI/CD) pipelines and the Agile Development methodology are used to drive iterative advancement and cross-development collaboration. This overall architecture serves as a new frontier in healthcare claims automation, and improves the speed, transparency, and affordability of health systems.