Designing an Intelligent Healthcare Data Interoperability Framework Using SAP Business Technology Platform and Artificial Intelligence

Abstract

Healthcare organizations operate many systems that were never designed to work together: electronic health records, laboratory and imaging systems, pharmacy applications, connected medical devices, patient mobile apps, payer platforms, and enterprise resource planning (ERP) systems. The resulting fragmentation delays care, duplicates effort, and weakens both clinical and operational decisions. This article proposes an intelligent healthcare data interoperability framework that uses SAP Business Technology Platform (BTP) as the governed integration backbone and artificial intelligence (AI) to accelerate and improve the hardest parts of interoperability, including terminology mapping, document understanding, data quality, and process automation. The framework addresses interoperability at four levels: connectivity, structure, meaning, and process. It combines standards-based exchange, API and event-driven integration, scalable and distributed processing, and secure, consent-aware data sharing, with generative AI services operating under guardrails. Building on research into wireless sensor networks for monitoring, privacy-preserving access control for electronic health records, cloud-based patient engagement, predictive clinical decision support, scalable healthcare data processing, event-driven claims processing, SAP-based decision intelligence, large language model (LLM) integration with SAP BTP, retrieval-augmented generation (RAG), and LLM guardrails, the article describes the framework’s architecture, AI capabilities, governance, use cases, evaluation metrics, and implementation roadmap.