Research Data Quality
Check an agreed population with selected quality programs, trace findings to the affected resources and return the results to the data holder. Verify the follow-up after a source correction.
Read each indicator against the population it measures
A quality result needs its denominator and coverage: missing data can make an indicator not evaluable.
Selected cohort
The agreed population and resource types.
Quality policy
Completeness, uniqueness and reference checks.
Measured data quality
Passed, evaluated and not-evaluable counts.
Trace a finding to its resources, correct the source and measure again.
Review what the selected research dataset contains, where it falls short, and what changed after a correction.
Read the indicator against its population
Identifier uniqueness is 10/12 in this twelve-Patient dataset. One recorded finding identifies the duplicate instead of treating a passing FHIR check as proof of dataset quality.
Trace the duplicate to both Patients
The finding lists the two affected Patients and the identifier field. Their source context and the applied quality rule stay attached to the investigation.
Review the result after the source correction
The completed recheck records 12/12 unique identifiers under the same quality policy. Review the measured population and the indicators that still have no applicable data.
After the source correction, identifier uniqueness improves from 10/12 to 12/12 under the same quality policy. Twenty other indicators remain not evaluable because their resource types are absent.
MII-oriented quality programs
Give the data holder a finding they can investigate.
Select a population and versioned quality programs, inspect the affected resources, then return the recorded results to the team responsible for the source.
Define the population
Keep the source, resource types, filters and selected program versions with the run. A visible Browse page is not the whole selected dataset.
Explain the result
Open a quality finding with its rule and affected resources. Read numerator, denominator and not-evaluable cases together; a missing population is not a perfect score.
Return the findings
Download recorded results as CSV ZIP or Excel return tables. The receiving team can review indicators, coverage and findings before arranging a source correction and follow-up run.
The native MII-oriented workflow includes selected CQL-derived resource functions. Whole Measures, original R execution and customer acceptance have separate qualification requirements. The return-table format remains provisional.
Related workflows and guides
Evaluate one research data delivery.
Bring a permitted test cohort, the profile versions, and a question about completeness, uniqueness, or references. Compare the findings with your current checks.