Now that EHR adoption has increased, data quality issues are receiving more attention. Unfortunately, data quality is difficult to define. Weiskopf and Weng make this point exceptionally well in their paper Methods and Dimensions of Electronic Health Record Data Quality Assessment: Enabling Reuse for Clinical Research. Data quality is the result of a series of factors ranging from specific architecture and design choices (e.g., schema, UI/data entry, validation methods) to semantic controls (e.g., terminologies, ontologies, standard data sets). With this in mind, I have created a page dedicated to data quality issues as a complement to current Architecture and Design resources.
The resources selected will focus primarily on four areas: data capture and validation; standard data sets and data set definitions; reviews and analyses of data inaccuracies in clinical systems; and methods for defining data quality.
The EHR Data Quality resource page is available now, and it will be updated monthly on the 12th starting in December. Look for the page as a sub-item under “Design” on the main menu.