AI-powered patient data interoperability for healthcare: A framework for enhanced clinical decision-making

Gunakala, Kiran Kumar (2025) AI-powered patient data interoperability for healthcare: A framework for enhanced clinical decision-making. World Journal of Advanced Research and Reviews, 26 (2). pp. 1080-1087. ISSN 2581-9615

AI-powered patient data interoperability represents a transformative approach to addressing the fragmentation of healthcare information systems. This comprehensive framework leverages SAP Business Technology Platform services to facilitate seamless integration of Electronic Health Records, IoT medical devices, and AI-driven diagnostics. The current healthcare landscape is characterized by significant interoperability challenges, with only 23% of hospitals able to exchange patient data seamlessly despite 96% having certified EHR technology. This fragmentation leads to measurable patient harm, including increased mortality risks, higher rates of inappropriate medication use, and elevated healthcare utilization. The proposed technical architecture combines SAP Integration Suite, SAP AI Core, SAP Event Mesh, and SAP Kyma to create a robust foundation for automated patient monitoring and real-time clinical decision support. Implementation of this framework across healthcare organizations has demonstrated substantial benefits, including faster diagnosis and treatment initiation, enhanced patient safety through reduction of medication errors, improved operational efficiency through decreased documentation time, and significant cost savings through reduced readmissions and emergency department utilization. The integration of these technologies enables a proactive approach to patient care, facilitating earlier intervention for deteriorating patients and supporting comprehensive care coordination across the healthcare continuum.