Implementing evidence-based care in clinical settings is difficult. The priority to deliver high-quality care with limited time and finite resources often means that the implementation process itself may be fraught with shortcuts, contributing to the inability to achieve outcomes consistent with those reported in clinical trials. To objectively measure, analyze, and improve is the nature of continuous quality improvement (QI),1  and yet these process factors are not typically ascertained and integrated as important independent variables in predictive models of patients’ outcomes. Instead, multivariable models designed to predict a patient’s outcome, for example, risk of stroke in patients with atrial fibrillation (AF), typically control for patient-level physiological factors, such as blood pressure, race, and age, and fail to account for unit-based factors that affect guideline implementation—sometimes for the better, sometimes for the worse.

The measurement and evaluation of how we work contributes not only to our understanding of workflow efficiency,...

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