Are Aggregated Electronic Health Record Datasets Good for Research?

Area of Science:

• Health Informatics
• Biomedical Data Science
• Real-World Data Research

Background:

• Large-scale electronic health record (EHR) data platforms are increasingly pooling information across multiple healthcare organizations.
• Platforms like NIH's All of Us, TriNetX, and Epic Cosmos exemplify this trend.
• Aggregating EHR data across disparate systems introduces unique challenges beyond single-entity data analysis.

Purpose of the Study:

• Define aggregated EHR data and differentiate it from other real-world data sources.
• Highlight the specific benefits and challenges associated with working with aggregated EHR data.
• Provide "good practices" to mitigate identified challenges and discuss the appropriateness of data pooling.

Main Methods:

• Literature review and conceptual analysis of aggregated EHR data platforms.
• Comparative analysis of aggregated EHR data versus other real-world data sources.
• Identification and synthesis of best practices for managing aggregated EHR data.

Main Results:

• Aggregated EHR data offers unique advantages for large-scale research but requires careful consideration of specific challenges.
• Key challenges include data heterogeneity, standardization issues, and privacy concerns across multiple institutions.
• Proposed "good practices" aim to address these challenges, enhancing data quality and research validity.

Conclusions:

• Pooling EHR data across disparate healthcare systems is feasible but necessitates strategic approaches to overcome inherent complexities.
• Adherence to best practices is crucial for maximizing the benefits of aggregated EHR data while minimizing risks.
• Further discussion is needed on the optimal conditions and methodologies for pooling such data.