Architecting Scalable BI Ecosystems for Clinical Billing Integrity: A Unified Data Architecture for Multi-Channel Healthcare Claims Management

Abstract

Healthcare billing integrity is fundamental to ensuring financial sustainability, regulatory compliance, and operational efficiency within modern healthcare systems. However, the increasing fragmentation of healthcare data across clinical encounter records, pharmacy dispensing systems, and insurance claims platforms has created significant challenges for maintaining consistent, accurate, and auditable billing processes. Disconnected information systems often result in data inconsistencies, duplicate records, delayed claim reconciliation, and limited visibility into billing workflows. Although previous studies have explored healthcare analytics and fraud detection, relatively limited attention has been given to a scalable Business Intelligence (BI) architecture that unifies heterogeneous healthcare data before advanced analytical methods are applied. This paper presents MultiStream-BI, a scalable BI ecosystem designed to integrate clinical encounter data, pharmacy dispensing records, and insurance claims into a unified data architecture for clinical billing integrity. The proposed architecture employs a multi-channel data ingestion framework, standardized data normalization, a cross-stream reconciliation engine, dimensional data warehouse modeling, and an integrated reporting layer for operational and strategic decision support in multi-payer environments. The proposed architecture establishes a reliable data foundation that enhances billing transparency, improves claims reconciliation, strengthens audit readiness, and provides the infrastructure upon which future machine learning-based healthcare fraud detection systems can be developed and deployed.