Electronic Health Records (EHR): Benefits, Limits & RWD

Electronic Health Records (EHR): What they are and how to use them in real-world data

Electronic health records (EHRs)—also called electronic medical records, or EMRs—became the U.S. standard after the 2009 ARRA/HITECH legislation incentivized digital adoption. For life sciences teams, EHR data is invaluable for clinical detail—especially vitals and in-office tests—but it varies by system and provider, often contains attachment-style lab/imaging reports, and typically arrives with 1–3 months of latency. Link EHR with claims and lab data to follow the full patient journey and to measure treatment patterns over time.

How we got here: From paper charts to ubiquitous EHRs

Since 2009, the American Recovery and Reinvestment Act (ARRA) and the Health Information Technology for Economic and Clinical Health (HITECH) Act pushed the transition from paper to digital by incentivizing EHR adoption and reducing Medicare reimbursements for organizations that failed to comply. This digitization improved accuracy and accessibility of records and unlocked a rich new source of real-world data (RWD) for research and analytics.

Terminology: EHR is the preferred industry term, but EMR is still used. In this guide, EHR is primary, with EMR referenced for clarity.

Key benefits of EHR data

Unlike claims, EHR data includes encounters for both insured and uninsured patients and captures rich clinical context beyond billing. Typical elements include:

• Demographics (e.g., age, gender)

• Medical history & diagnoses

• Medications prescribed during the encounter

• Orders for lab tests and procedures

• In-office tests (e.g., flu, strep, COVID)

• Patient vitals, one of the few RWD sources with this level of detail:

  • Height

  • Weight

  • Temperature

  • Blood pressure

  • Body mass index (BMI)

This level of detail enables better cohort identification, disease monitoring, and insights into provider decision-making.

Important considerations (read before you analyze)

EHR systems vary widely. Configurations differ by health system and practice, which can lead to:

• Reason for visit not being standardized

• Encounter types including administrative activity rather than direct patient care

• Provider preference for certain code sets (e.g., ICD, NDC, LOINC) causing site-level variation

• EHRs recording what was ordered, not necessarily what was completed or filled

• Lab/imaging results stored as attachments rather than structured data fields

• Latency of 1–3 months from data generation to delivery

Tip: Use QA rules to identify encounter types, normalize codes, and reconcile attachment-based reports for analysis.

EHR use cases for life sciences

EHR data is especially valuable when you need:

• Longitudinal patient journey mapping across encounters

• Provider prescribing behavior analysis at the point of care

• Chronic disease management tracking — e.g., using FEV1/FVC results to assess COPD likelihood and treatment response

Advance the science with EHR data from HealthVerity

HealthVerity provides EHR data covering nearly 140 million patient journeys, fully HIPAA-compliant and standardized into a single common data model. This allows you to integrate multiple EHR sources into one uniform schema.

EHR datasets can be linked deterministically with medical claims and lab results for the most complete longitudinal patient view.

Why it matters: Unified schemas and cross-source linkage accelerate onboarding and support regulatory-grade RWE and HEOR.