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World Journal of Advanced Research and Reviews
Article . 2025 . Peer-reviewed
Data sources: Crossref
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ZENODO
Article . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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AI-powered patient data interoperability for healthcare: A framework for enhanced clinical decision-making

Authors: Gunakala, Kiran Kumar;

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

Abstract

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.

Related Organizations
Keywords

Healthcare interoperability, Artificial intelligence, Patient data integration, Clinical decision support, SAP Business Technology Platform

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
gold