Quality scorecards
Turn document quality into a readable score and breakdown.
Summarize quality dimensions while keeping the contributing checks visible for deeper review.
94quality score
Hgear Data Quality
Assess healthcare data quality and readiness.
Assess completeness, consistency and conformance with C-CDA scorecards, and review quality gaps before data reaches downstream workflows.
CompletenessConsistencyConformanceReadiness scorecards
Measurement tools
Review C-CDA quality scores and the checks behind them.
Scorecards and issue breakdowns show where quality is strong, where it is incomplete, and what deserves review first.
Readiness signals
Track the gaps that slow implementation down.
Group completeness and consistency findings into signals teams can review before data moves deeper into production workflows.
92% Completeness88% Consistency96% Conformance7 items to review
How it works
From a document to a score you can act on.
Every score is backed by the individual checks that produced it, so a number is never the end of the conversation.
Load a C-CDA document
Start with a continuity-of-care or discharge document, using synthetic or de-identified data for evaluation.
Score each quality dimension
Completeness, consistency and conformance are scored from the sections, codes, dates and structure in the document.
Review the checks behind each score
Open any dimension to see which checks passed, which failed and where in the document the problem lives.
Prioritize gaps before downstream use
Group findings into readiness signals so teams know what to fix first, and measure again after the fix.
Quality checks & scorecards
Understand the checks behind each quality score.
Completeness, consistency and conformance are measured by concrete checks, each tied to a location in the document.
- Missing or empty sections flagged for completeness
- Codes, units and dates checked for consistency
- Structure checked against the C-CDA standard for conformance
DimensionChecksScore
CompletenessRequired sections present92%ConsistencyCodes and dates agree84%ConformanceStructure matches the standard96%ReadinessFit for downstream use78%Why measure quality, not just validity.
A clinical document can be structurally valid and still unusable: a medications section that is present but empty, allergy codes from the wrong code system, encounter dates that contradict each other. Validation answers whether the data follows the standard. Data quality assessment answers whether it is complete, consistent and ready for the workflow that depends on it.
Hgear scores C-CDA documents across completeness, consistency and conformance, shows the checks behind every score and groups findings into readiness signals. Measured across sources and over time, those signals tell integration, quality and analytics teams where to focus before incomplete data reaches care management, reporting or AI pipelines.
Workflow integration
Carry quality signals into your workflow.
Use measurement during intake, review and repeatable processing to keep quality visible over time.
01
Scorecards
Summarize quality dimensions per document
02
Issue breakdowns
See which checks drive each score
03
Repeatable checks
Measure every source on every run
Common questions
Healthcare Data Quality FAQ
Which healthcare data quality dimensions should I review?
Review completeness, consistency and conformance against the requirements of your intended workflow. Missing values, contradictory information and structural problems require different actions. A summary score is most useful when the underlying checks and affected data are available for review.
Explore the C-CDA quality scorecardHow is data quality assessment different from FHIR validation?
FHIR validation checks conformance to selected structural, profile and terminology rules. Data quality assessment also asks whether information is complete and consistent enough for its intended use. Hgear's C-CDA scorecard focuses on document quality; evaluate FHIR resource conformance with the validation workflow.
Compare the FHIR validation workflowWhat should a team do after finding a quality gap?
Review the contributing check, identify the source field or document section, and agree on the expected value with the responsible team. Correct the source or mapping where appropriate and rerun the assessment. Track recurring issues separately from one-off exceptions; a score alone does not establish production readiness.
Review source-to-FHIR transformationClose the loop
Transform the next dataset with the same clarity.
Move from quality signals back into transformation with a workflow that keeps source, structure and readiness connected.