Explore completeness, accuracy, consistency, timeliness and fitness-for-purpose across healthcare use cases.
Explore completeness, accuracy, consistency, timeliness and fitness-for-purpose across healthcare use cases.
A value can be syntactically valid yet unsuitable for a clinical, operational, analytical or AI use case. Healthcare data quality should therefore be measured against the decisions and workflows the data supports.
Completeness, accuracy, consistency, validity, uniqueness and timeliness provide a starting framework. The importance and thresholds for each dimension should reflect the use case.
Continuous profiling, rules and monitoring help organizations identify recurring issues closer to their source instead of repeatedly correcting them in downstream reports and models.
Quality improves faster when a failed rule can be connected to the data element, source, lineage and responsible steward or owner.
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