Measuring and managing data 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-two rule templates and associated knowledge tables used by the rule templates. This work provides a scalable framework with which data quality rules can be organized, shared among facilities and reused. The ten most frequent data quality problems based on the initial rules results are identified. 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 rules-based data quality assessment and monitoring to be possible and scalable.