AI-Powered Clinical Decision Support through Integration of SAP, Open-Source LLMs, Digital Payments, and Oracle ML Pipelines with BMS Upgrade
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Abstract
The integration of enterprise resource planning (ERP) systems with open-source large language models (LLMs), digital payment infrastructures, and Oracle machine learning (ML) pipelines offers a transformative approach to healthcare analytics and decision-making. This study presents an AI-powered Clinical Decision Support (CDS) framework that bridges SAP’s healthcare modules, Oracle ML-driven predictive pipelines, and secure digital payment systems to enable real-time patient risk assessment, automated claims validation, and data-driven care recommendations. Open-source LLMs are utilized for natural language understanding, clinical note summarization, and context-aware reasoning, improving diagnostic accuracy and patient interaction. The architecture emphasizes interoperability, explainability, and compliance with healthcare data standards such as HL7 and FHIR. Through cloud-native orchestration and intelligent workflow automation, the proposed model enhances clinical efficiency, reduces administrative burden, and promotes financial transparency in healthcare delivery systems. This integration establishes a unified, intelligent ecosystem for proactive diagnosis, treatment optimization, and secure medical transaction processing.
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