The widespread adoption of electronic health records (EHRs) over the past two decades has generated an unprecedented volume of routinely collected clinical data, opening substantial new research opportunities alongside a distinct set of methodological challenges that differ meaningfully from traditional prospective clinical research.
What EHR-Based Research Makes Possible
Scale Not Achievable Through Traditional Trials
EHR data enables research at a scale — hundreds of thousands or millions of patient records — that would be prohibitively expensive and time-consuming to achieve through traditional prospectively designed studies, allowing researchers to detect rare adverse events, examine outcomes across diverse real-world patient populations, and study conditions too uncommon for adequately powered traditional trials.
Real-World Evidence
A growing and increasingly influential category of research, often termed “real-world evidence,” uses EHR data to examine how treatments perform in routine clinical practice, among the broader, more diverse patient populations typically excluded from tightly controlled clinical trials due to strict eligibility criteria. Regulatory bodies have increasingly recognized real-world evidence as a valuable complement to traditional trial evidence, particularly for monitoring long-term safety and effectiveness after initial approval.
Identifying Care Gaps and Disparities
Research using EHR data has proven particularly valuable for identifying systematic gaps in care delivery and disparities in treatment or outcomes across different patient populations, since routinely collected data can reveal patterns across entire health systems that would be difficult to detect through smaller, targeted studies.
Methodological Challenges Unique to EHR Research
Data Quality and Completeness
Unlike prospectively designed research where data collection follows a predetermined protocol, EHR data reflects routine clinical documentation practices, which research consistently shows to be inconsistent — missing values, variable documentation detail between clinicians, and information recorded in unstructured free-text notes that requires additional processing to extract systematically.
Confounding by Indication
A persistent and significant methodological challenge in EHR-based treatment comparison research is confounding by indication — patients who receive a particular treatment often differ systematically from those who don’t, in ways related to the very outcome being studied, making it genuinely difficult to isolate a treatment’s true effect from the underlying reasons it was prescribed to a particular patient in the first place.
Interoperability Limitations
Research drawing on EHR data across multiple healthcare systems frequently encounters interoperability challenges, since different EHR platforms often structure and code clinical information differently, complicating efforts to combine datasets across institutions for larger, more statistically powerful research studies.
Methods Researchers Use to Address These Challenges
Advanced Statistical Techniques
Researchers increasingly apply statistical methods specifically designed to address confounding in observational data — propensity score matching and instrumental variable analysis are frequently used examples — attempting to approximate the balanced comparison groups that randomization achieves naturally in traditional clinical trials.
Natural Language Processing
Given how much clinically relevant information resides in unstructured free-text clinical notes rather than structured data fields, research increasingly applies natural language processing techniques to systematically extract relevant clinical information from free text, substantially expanding the effective scope of what EHR-based research can examine.
Data Standardization Initiatives
Research infrastructure initiatives promoting standardized data models across different EHR systems aim to directly address interoperability challenges, enabling more robust multi-institutional research collaborations than would otherwise be feasible given the underlying platform diversity.
Privacy and Ethical Research Considerations
EHR-based research operates under significant privacy and ethical obligations, typically requiring de-identification of patient data and, depending on jurisdiction and specific research design, formal ethics committee review and approval even when a study involves only secondary analysis of already-collected clinical data rather than new data collection from patients directly.
The Complementary Role of EHR Research
Rather than replacing traditional prospective clinical trials, the strongest current research perspective positions EHR-based research as a valuable complement — offering scale, real-world generalizability, and long-term safety monitoring capacity that trials cannot easily provide, while trials retain unique strength in establishing causation through randomization that observational EHR research cannot fully replicate.
Federated Learning Research Approaches
Given the significant privacy and interoperability barriers to physically pooling EHR data across institutions, research increasingly explores federated learning approaches, where analytical models are trained across multiple institutions’ data without the underlying patient-level data ever leaving each institution’s own secure systems. Early research applying federated approaches to EHR-based studies shows promise for achieving larger, more diverse effective sample sizes while addressing some of the privacy and data-sharing barriers that have historically limited multi-institutional EHR research collaboration.
Research on Algorithmic Bias in EHR-Derived Models
Because EHR data reflects existing patterns of healthcare access and clinical decision-making, research has found that predictive models trained on this data can inadvertently encode and perpetuate existing healthcare disparities — for example, a well-documented case involved an algorithm that used healthcare cost as a proxy for health need, which research found systematically underestimated the needs of Black patients due to existing disparities in healthcare spending patterns unrelated to actual clinical need. This finding has driven substantial research interest in auditing EHR-derived predictive models specifically for these kinds of embedded biases before clinical deployment.
Research Gaps Worth Addressing
- Continued development of statistical and machine learning methods to better address confounding in observational EHR research
- Expanded natural language processing research tailored to extracting clinically meaningful information from diverse note-taking styles
- Research on data standardization approaches that improve multi-institutional research feasibility
- Research on the specific conditions under which EHR-based findings do and do not replicate traditional trial results
Contributing to This Field
Health informatics and real-world evidence research fall within the scope of Medicine as published by journals like IJMS. If you have original research or review papers using EHR-based methods, review the IJMS Scope and submit through the Paper Submission page.
Final Thoughts
Electronic health record data has opened substantial new research possibilities at a scale traditional prospective research cannot match, but realizing this potential requires careful methodological attention to the data quality and confounding challenges unique to observational, routinely collected clinical data. As data standardization and privacy-preserving analytical methods continue to mature, EHR-based research is likely to play an increasingly central, rather than supplementary, role in the broader medical evidence base.
For further reading on health data research standards, see the U.S. National Library of Medicine’s resources on health informatics.