(1) Detection of data errors introduced during the process of extracting, transforming and loading (ETL) clinical data from source systems into the clinical data warehouse. 
(2) Providing a quick summary view describing the distribution of data across thousands of patient variables, and over multiple years, using interactive data visualization tools. 
(3) Capturing, and making available, notes or annotations about the data so that researchers using the system have contextual information about the data provided by the system. 
(4) Detecting technical problems in the system and making that information available to system administrators and users.

(1) statistically comparing the distribution of patient counts, over multiple years and multiple variables (ontology elements), across all the health care providers in the network. The data quality issues are then summarized in a tabular format on DTEQTIV. 
(2) Providing an interactive data visualization module [Pic.1.] that uses pre-computed patient counts across thousands of patient variables to show patient population data distributions (across multiple years, and across multiple health care providers). 
(3) Allowing users and data administrators to add comments and annotations to the data variables to provide contextual information about the data that is displayed. 
(4) Automating network testing using queries that are run twice a day on the network. The results, and errors, are available on the dashboard.