Ten Characteristics of Data Quality
AHIMA's data quality management model identifies ten characteristics that together define high quality health data: data accuracy, accessibility, comprehensiveness, consistency, currency, definition, granularity, precision, relevancy, and timeliness. The RHIA exam frequently presents a scenario and asks which characteristic is being violated, so memorizing definitions alone is not enough; candidates must apply them.
Applying the Characteristics
- Accuracy: data is correct and free of errors, such as a diagnosis code that matches the documented condition.
- Accessibility: data is easily obtainable while remaining appropriately protected.
- Comprehensiveness: all required data elements are present, such as a complete history and physical.
- Consistency: data is reliable across the record and across systems, meaning the same fact is not contradicted elsewhere in the chart.
- Currency: data is up to date, reflecting the most recent clinical status.
- Definition: each data element has a clear meaning and set of allowable values.
- Granularity: data is collected at the appropriate level of detail for its intended use.
- Precision: data values are exact and not overly broad.
- Relevancy: data collected serves a purpose and is not extraneous.
- Timeliness: data is recorded within an acceptable time frame to support decision making.
Common Exam Scenario
A frequently tested scenario involves a physician documenting a diagnosis three weeks after discharge, well after the coding and billing process has concluded. This is a timeliness problem. Another common scenario involves a lab value recorded as "abnormal" without a specific numeric result, which violates precision and granularity.
Data Quality Monitoring
HIM departments operationalize this model through data quality audits, concurrent chart reviews, and physician query programs. Metrics such as delinquent record rates, query response rates, and coding accuracy rates all map back to one or more of the ten characteristics.
Connection to Governance
The data quality model does not operate in isolation. It works alongside data governance structures, since a data steward is typically the person accountable for monitoring and remediating quality issues within a specific domain.
Exam Tip
When answering scenario questions, first identify which of the ten characteristics is described before selecting an answer, since incorrect options frequently substitute a plausible but wrong characteristic, such as confusing consistency with accuracy.