Quality measurement, a key component to improve healthcare, has traditionally relied on administrative data and time-consuming manual chart abstraction. This was no exception to Mayo Clinic. With the greatly increasing number of externally reported measures and internal quality initiatives, Mayo’s quality organization was challenged with easing the manual work effort burden placed on staff. The Quality Management Services (QMS) department had more than doubled its chart abstraction staff to focus primarily on manual metric creation. For the effort to be successful, Mayo’s leadership agreed that automating quality measures would provide the only viable way to free up staff time, allowing their focus to remain more on important quality improvement and analytic work rather than wasting precious time on manual measure creation.

Assessing the data and collection sources across Mayo Clinic is no small task. This project involved capturing and assessing the data quality of 500 unique data elements used in 275 quality measures collected across 40 source systems across Mayo’s three clinics located in Rochester MN, Scottsdale AZ and Jacksonville FL. The outcome: Mayo was able to successfully develop a quality measure assessment methodology and centralized measure metadata repository that today continues to serve as the foundation to their current e-Measure and data analytics programs.