How Regulatory Bodies Accept EHR-Derived Data in Pharma Studies

Electronic Health Records (EHRs) are increasingly used as real-world data (RWD) sources for generating real-world evidence (RWE) in pharmaceutical research. However, not all EHR-derived data is considered fit-for-purpose by global regulatory agencies such as the EMA and the USFDA. To gain regulatory acceptance, EHR-based data must meet strict criteria for quality, traceability, reliability, and relevance.

This tutorial outlines how pharma professionals can ensure EHR-derived data complies with regulatory expectations, what documentation to prepare, and which standards to follow when planning submissions using RWE generated from electronic medical records.

Understanding Regulatory Expectations for EHR-Derived Data:

Agencies such as the FDA and EMA are open to the use of EHR data, provided the following criteria are met:

• Data Integrity: The source data must be complete, accurate, and unaltered.
• Traceability: Each data point must be traceable to its origin, including who entered it and when.
• Relevance: Data must be appropriate for the clinical question or regulatory decision.
• Transparency: Clear documentation of data provenance and transformation is required.
• Governance: Use of the EHR system must be under formal oversight with defined policies.

Regulatory bodies apply similar scrutiny to EHR-derived data as they do to data collected in randomized controlled trials (RCTs).

Step 1: Ensure EHR System Validity and Compliance

Only validated, regulated EHR systems should be used for data generation. Key checks include:

• 21 CFR Part 11 compliance for electronic records and signatures
• Audit trails that show who accessed or changed data
• System qualification and change control documentation
• Role-based access with permission logs

Systems that generate the data should undergo formal process validation and adhere to ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate).

Step 2: Data Source Mapping and Documentation

Agencies expect thorough documentation of where data comes from. Your submission must include:

• List of all data fields used and their clinical significance
• Definitions of each variable (e.g., diagnosis codes, lab values)
• Data transformation or derivation logic applied
• Version control for datasets and extraction protocols

It’s also important to describe any limitations in data capture, such as missing values or inconsistent time intervals.

Step 3: Validate Data Quality and Consistency

Before submitting RWE derived from EHRs, conduct quality checks such as:

• Duplicate entry analysis
• Outlier detection (e.g., unrealistic blood pressure readings)
• Range and consistency checks
• Missing data imputation justifications

Agencies often require submission of the data cleaning steps, query logs, and issue resolution summaries. These are typically maintained under GMP documentation requirements.

Step 4: Clarify Patient Selection and Data Linkage Methodology

Patient population definitions must be precise and reproducible. Regulatory reviewers need to know:

• Inclusion and exclusion criteria for the dataset
• ICD/CPT/LOINC codes used for identifying conditions or procedures
• Data linkage rules if combining EHR with claims or registry data
• Patient privacy safeguards, such as de-identification SOPs

Be transparent if linkage required deterministic or probabilistic methods, and provide match accuracy rates.

Step 5: Align with Relevant Regulatory Frameworks

Each regulatory body provides guidance documents for RWD use:

• FDA: Framework for RWE program, 2018; Draft guidance on RWD use in submissions
• EMA: RWE Reflection Paper; Big Data Task Force Recommendations
• Health Canada: Guidance on RWD/RWE submissions
• CDSCO: Emerging interest in RWE for post-marketing studies in India

In all cases, align your submission to the specific regulatory definitions of fitness-for-purpose data.

Step 6: Use Standardized Data Models Where Possible

Adopt harmonized structures such as:

• OMOP CDM: Observational Medical Outcomes Partnership Common Data Model
• HL7 FHIR: Fast Healthcare Interoperability Resources
• Sentinel Data Model: Used by FDA for safety surveillance

These models improve traceability, transparency, and cross-system comparison. They are encouraged for studies submitted as RWE.

Step 7: Address Statistical and Methodological Rigor

Include a clear statistical analysis plan (SAP) that addresses:

• Confounding and bias mitigation strategies
• Propensity score matching or weighting techniques
• Sensitivity analyses for missing or ambiguous data
• Endpoint definitions using standardized clinical logic

Justify your choice of real-world comparators or external controls. Regulatory bodies evaluate RWE with the same rigor as RCTs in many cases.

Step 8: Submit RWE as Part of Regulatory Filing with Transparent Appendices

Whether used in a New Drug Application (NDA), Marketing Authorization Application (MAA), or post-marketing commitment, EHR-derived data must be submitted in a transparent, structured format:

• Include all data transformation protocols
• Provide audit logs and dataset lineage
• Append SAS or R scripts used for analysis
• Submit de-identified patient-level data as applicable

Consider publishing protocols and methods to boost reviewer confidence and transparency.

Conclusion: Charting a Path to Regulatory Acceptance

As regulators grow more open to EHR-derived RWE, pharmaceutical companies must meet heightened expectations for data quality, transparency, and methodological soundness. Follow the guidance outlined above to ensure your EHR-based study data is not just real-world, but real-useful for regulators.

Whether analyzing treatment persistence, adverse event patterns, or comparative effectiveness, EHR-derived RWE can accelerate access to therapies and post-market insights—provided it’s regulatory-grade.

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