Rule templates and linked knowledge sources for rule-based information quality assessment in healthcare

Measuring and managing information quality in healthcare has remained largely uncharted territory with few notable exceptions. A rules-based approach to data error identification was explored through compilation of over 6,000 data quality rules used with healthcare data. The rules were categorized based on topic and logic yielding twenty rule templates and associated knowledge tables used by the rule templates. Knowledge sources for the knowledge tables were sought and identified for eleven of the twenty rule templates and have to be created for the remaining nine. This work provides a framework with which data quality rules can be organized and shared as rule templates and knowledge tables. While there is significant additional work to be done in this area, the exploration of the rule template and associated knowledge tables approach here shows the approach to be possible and scalable.