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

Area of Science:

• Health Informatics
• Machine Learning
• Data Science

Background:

• Electronic health records (EHRs) face challenges with data fragmentation, hindering standardization and interoperability.
• Federated learning offers a solution by enabling collaborative model development while preserving patient data privacy.
• This study addresses statistical, system, and privacy challenges in applying federated learning to biomedical data.

Purpose of the Study:

• To investigate the application and impact of federated learning in the biomedical domain.
• To address statistical challenges, system complexities, and privacy issues associated with federated learning in healthcare.
• To evaluate federated learning's effectiveness in improving EHR standardization and interoperability.

Main Methods:

• A comprehensive literature search was conducted following PRISMA guidelines across multiple databases (PubMed/Medline, Scopus, Web of Science, etc.).
• Search terms included "electronic health records," "federated learning," and related concepts, covering publications from January 2020 to April 2024.
• Data extraction focused on cohort characteristics, modeling approaches, and federated learning frameworks.

Main Results:

• Analysis of 15 articles revealed federated learning enhances data sharing and analysis in healthcare, improving EHR standardization and interoperability.
• Federated learning models often matched or exceeded localized models, particularly with limited or fragmented data, and excelled in predicting rare diseases.
• Techniques like federated averaging and differential privacy ensured robust performance and maintained data security.

Conclusions:

• Federated learning is a transformative advancement in health informatics, crucial for fragmented healthcare systems.
• It improves patient outcomes and operational efficiencies by enabling nationwide big data analytics.
• Federated learning facilitates seamless data exchange, overcoming critical barriers in the US healthcare landscape.