EHR Data Quality for Real-World Evidence

Every analysis depends on data quality. Real‑world evidence (RWE) derived from electronic health records (EHRs) only earns trust when inputs are complete enough, consistent enough, and plausible enough for the question at hand. What follows is a practical playbook to assess and improve EHR data fitness for use, with simple checks, clear documentation, and realistic remediation steps. If you’re new to RWE more broadly, orient first with real‑world evidence in healthcare decision‑making to understand where EHR fits among claims, registries, and patient‑reported data.

Define fitness for use

Fitness is not perfection. It means the data are good enough for a specific purpose within known limits. Write that purpose down: cohort, timeframe, outcomes, exposures, covariates, and required subgroups. When outcomes are patient‑centered or will inform decisions, align with choosing outcomes that matter so you don’t optimize data quality for the wrong metric.

Map sources and flows

Create a one‑page diagram of where data originate and how they flow into your analytic environment:

• Source systems: EHR modules (encounters, orders, labs, meds), ancillary systems, and external feeds (HIE, registries)
• Interfaces: HL7, FHIR, batch files; refresh cadence; and known lags
• Identity: patient matching, encounter deduplication, and address normalization
• Transformations: code mapping, unit normalization, time‑windowing

This map becomes the index for quality checks and a living reference for collaborators.

Core checks: completeness, consistency, plausibility, and timeliness

Start with simple, automatable metrics. Trend them monthly and stratify by site, clinic, payer, language, and neighborhood to spot equity‑relevant patterns.

1. Completeness

• Required fields: encounter date, patient ID, birth date, sex, diagnosis codes, procedure codes, orders, results
• Outcome‑specific fields: e.g., A1c and medication fills for diabetes; blood pressure for hypertension; gestational age and blood pressure for maternal outcomes
• Missingness patterns: overall rates and co‑missingness (which fields go missing together)

2. Consistency

• Code systems: ICD/ICD‑10 variants, SNOMED, LOINC, RxNorm usage; proportion mapped vs. “other”
• Units and ranges: mg/dL vs. mmol/L; systolic/diastolic order; negative values where impossible
• Value sets: consistent use of problem list vs. encounter diagnoses; medication generic/brand mappings

3. Plausibility

• Range checks: humanly plausible vitals and labs; gestational age within possible bounds
• Temporal logic: diagnosis dates before procedures; labs after orders; birthdates make sense for pediatric vs. adult cohorts
• Duplicates and near‑duplicates: same patient, same timestamp, same value

4. Timeliness

• Data arrival lags by source; backfill windows after upgrades
• Stability of refresh cadence (standard deviation of arrival times)

Document limitations in plain English

Write a short “data notes” section for each analysis:

• What’s missing or unreliable (e.g., social history, smoking status, interpreter need)
• Known site‑level idiosyncrasies (e.g., clinic A records home BPs only in scanned PDFs)
• Implications for interpretation and subgroup analyses

This mirrors the transparency encouraged in the primer on bias and confounding in plain language. When a limitation materially affects a policy brief, use the clarity structure in AI‑assisted evidence synthesis for policy briefs.

Build lightweight dashboards

You do not need a heavy platform to monitor quality. A few charts go a long way:

• Row counts by table over time with annotations for system changes
• Missingness heatmaps for key fields
• Distribution plots for vitals and common labs, faceted by site/clinic
• Lag histograms for data arrival by source

Share the dashboard with clinical and data leaders. Invite corrections—people closest to the work often know why a field looks odd.

Remediation: fix causes, not just symptoms

Some fixes are upstream (change how data are captured); others are analytic (derive better proxies). Prioritize changes that prevent future errors.

• Upstream
  • Simplify forms; make essential fields required; clarify units.
  • Provide quick‑reference guides for front‑line staff; cut duplicate entry.
  • Add order sets and smart text that standardize documentation.
• Analytic
  • Derive composite indicators (e.g., “diabetes on therapy” from meds + A1c patterns).
  • Build robust phenotypes with multiple code types and time rules.
  • Use rolling med lists and fill data to impute likely adherence.

Equity: measure and mitigate gaps

Quality problems often hide inequities. Stratify completeness and plausibility by language, race/ethnicity (when collected), payer, and neighborhood. If interpreter need or address instability is missing at higher rates in certain clinics, investigate causes and fix workflows. Align with the fairness practices from AI for population health management if your data feed outreach.

Linkage and hybrid designs

EHR rarely stand alone. Claims extend capture of external care; registries add detailed outcomes; patient‑reported data fill experience gaps. When designing hybrid studies, consider pragmatic‑trial linkages as outlined in pragmatic trials and RWE: better together. Be explicit about linkage methods, match rates, and bias.

Maintenance: treat quality as a process

Embed checks into CI: run data tests with every load; alert on drift; and review a short monthly report. Keep a change log so analysts know when a metric moved because reality changed versus the pipeline changed.

Case vignette: postpartum hypertension surveillance

Goal: monitor severe postpartum hypertension events within 10 days of delivery.

• Map: identify where blood pressure lives (vitals, flowsheets, scanned PDFs) and how delivery is flagged (CPT/ICD, OB module, discharge summaries).
• Checks: require at least two BP readings after discharge; flag implausible values; enforce unit normalization.
• Fixes: add a discharge smart‑set prompting for postpartum outreach and interpreter need; create a flowsheet for home BPs.
• Outcome: timelier identification and outreach, feeding the workflows described in AI for population health management.

Implementation checklist

• Define fitness for use tied to a concrete question.
• Map data sources and flows; note refresh cadence and lags.
• Monitor completeness, consistency, plausibility, and timeliness—stratified by site and subgroup.
• Document limitations in plain English and update with every refresh.
• Fix upstream where possible; build robust phenotypes where needed.
• Treat quality monitoring as an ongoing process, not a one‑time task.

Key takeaways

• EHR data do not need to be perfect—just fit for purpose with known limits.
• Small, consistent checks prevent big mistakes.
• Transparency and equity stratification build trust and guide smarter fixes.

Sources and further reading

• OHDSI and OMOP resources on data quality frameworks
• HL7/FHIR implementation guides and unit normalization references
• Method papers on phenotyping and validation in EHR‑based research