Integrity and Quality
Data Integrity Tests​
Data Integrity tests check that the data is complete and correctly formatted. The goal of these tests is to ensure that the data pipeline does not break and that the data is complete.
"Complete" here means that all expected rows are present, regardless of whether a rate is present.
Examples:
- no duplicate ROIDs
- no missing payers, providers, networks, codes
Data Quality Tests​
Data Quality tests check that the rates and coverage are accurate. The goal of these tests is to ensure that (1) rates (canonical, gross, medicare, historic, base) and traceability are accurate and (2) coverage is as expected.
"Accurate" is a subjective measure, but it generally means that the rates are consistent with (1) source data, (2) subject matter expertise, and (3) previous versions.