An Innovative Digital Quality Ecosystem: Data Quality Assessment (DQA)
The focus of this discussion is enabling trust in information needed to power the next generation of quality measures.
Reproducibility, reliability and validity are all major considerations for anyone working in the digital quality ecosystem, but most quality measurement validation procedures are manual. These analytic processes must use standard data quality measures, ideally as digital artifacts aligned with the clinical quality measures that reference the data elements being assessed.
To enable efficient technology-augmented validation, end-user expectations for data fitness and its use in generating quality reports must be transparent and standardized.
Added on 11/16/2022