Standardization and Interoperability: Federated Learning’s Impact on EHR Systems and Health Informatics

Federated learning represents a transformative advancement in health informatics, addressing the critical need for seamless data exchange in the fragmented US healthcare landscape. By improving patient outcomes and operational efficiencies, federated learning paves the way for leveraging big data analytics on a nationwide scale.

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Publication Details

Journal
Advances in Health Information Science and Practice
Published
2025-01-01
DOI
https://doi.org/10.63116/ubym3803
Citations
2
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
4.96
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article

Standardization and Interoperability: Federated Learning’s Impact on EHR Systems and Health Informatics

Herat Joshi, Shenson Joseph
2 citations
Advances in Health Information Science and Practice
Machine Learning in Healthcare
4.96
article

Standardization and Interoperability: Federated Learning’s Impact on EHR Systems and Health Informatics

Herat Joshi, Shenson Joseph
article en
2 citations

Abstract

Federated learning represents a transformative advancement in health informatics, addressing the critical need for seamless data exchange in the fragmented US healthcare landscape. By improving patient outcomes and operational efficiencies, federated learning paves the way for leveraging big data analytics on a nationwide scale.

Advances in Health Information Science and PracticeVol. 1(1)
Twitter (United States) (US), University of North Dakota (US), National Great Rivers Research and Education Center (US)
Openalex Percentile: Top 6%
Machine Learning in Healthcare
4.96
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