Abstract: This study developed a taxonomy of data defects and used it when automatically detecting defects to assess Medicaid data quality. Five major categories and seventeen subcategories of defects were identified in the taxonomy. Defect density exceeded 10% in five tables. The majority of the data defects belonged to Format Mismatch, Invalid Code, Dependency-Contract Violation, and Implausible Value types. The results suggest that learning health organizations can potentially benefit from data quality improvement.

Describe the new knowledge and additional skills the participant will gain after attending your presentation.: * Describe the concepts of "data quality" and "data defect"
* Understand the main- and sub-categories of data defects
* Comprehend the scope of data quality problems in the Medicaid datasets
* Develop an understanding of potential solutions to deal with data quality problems to support learning health systems

Authors:

Gunes Koru (Presenter)
University of Maryland Baltimore County

Yili Zhang, University of Maryland Baltimore County
Abir Rahman, University of Maryland Baltimore County