Module 12 · Part 3 · Data Collection
Verifying Clinical Data
Understand when and how to verify patient-reported data against clinical records, and build a data quality framework for your registry.
Patient Registries 101 · Dr. Danielle Boyce · EpilepsyLive
Patient-reported vs. clinician verified data
- Most patient registries start with self reported data because it is scalable and low-cost.
Diagnosis confirmation
- For rare diseases, self reported diagnosis is unreliable.
- Methods
- Medical record review by a study coordinator or clinician
- Genetic test report review (for genetic diseases)
- Clinician attestation (treating physician confirms diagnosis)
- Death certificate review (for mortality data)
Genetic/molecular data
- Mutation or variant data should be extracted directly from clinical genetic test reports, not transcribed by participants.
- Build a document upload feature so participants can upload their genetic test report, and have a study coordinator extract the variant data into standardized fields (HGVS notation, gene symbol, var…
Key clinical measurements
- For outcomes analysis, key measurements (functional scores, lab values, imaging findings) should be extracted from the medical record or entered by the treating clinician, not relying on participan…
Edit checks and validation rules
- Build validation into your data collection forms
- Range checks (age cannot be negative; weight cannot be 500kg)
- Logic checks (symptom onset cannot be before birth date)
- Completeness checks (flag records with missing required fields)
- Consistency checks (diagnosis date should precede treatment start date)
Missing data management
- Design your analysis plan before data collection, including a missing data strategy
- What percentage of missingness in a field triggers exclusion from analysis?
- Will you use imputation? Which methods?
- How will you handle "not applicable" vs. "unknown" vs. genuinely missing?
Duplicate detection
- Rare disease communities are small.
- Match on date of birth, sex, and diagnosis date
- Consider a unique participant identifier (with appropriate privacy protections)
- Coordinate with other registries in your disease space
Audit trail
- Maintain a complete audit trail of
- Who entered each data element
- When data was entered and modified
- What changes were made
- Source documentation for verified data
- This is required for regulatory submissions and is good research practice.
Study coordinator verification workflow
- For registries with clinical site participation, a typical verification workflow
- Participant enrolls and completes self report
- Participant signs medical records release authorization
- Study coordinator at clinical site receives notification
- Coordinator reviews medical record and completes clinical data form
- Discrepancies between patient-reported and record-sourced data are flagged
Study coordinator verification workflow (cont.)
- Data manager reviews and resolves discrepancies
- Record is marked as "clinician verified"
Data quality metrics to track
- Completeness rate: % of required fields with non-missing values, by element and by time period
- Timeliness: Median days from enrollment to complete baseline data
- Verification rate: % of enrolled participants with clinician verified diagnosis
- Inconsistency rate: % of records with at least one logic or range check failure
- Attrition rate: % of participants who complete each follow up assessment
- Report these metrics to your SAB quarterly.
Key resources
- AHRQ Registry User's Guide Chapter 5: Data Collection
- FDA Data Standards for Clinical Trials
- CDISC CDASH (Clinical Data Acquisition Standards Harmonization)
- TransCelerate Data Standards
- ← Module 11 | Module 13: Recruitment Strategies →