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The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH.

Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately. We set out to determine the prevalence of certain types of Boolean logic errors in CDS statements.

Clinical decision support (CDS) alerts built into the electronic health record (EHR) have the potential to reduce the risk of drug-induced long QT syndrome (diLQTS) in susceptible patients. However, the degree to which providers incorporate this information into prescription behavior and the impact on patient outcomes is often unknown.

This study examines the validity of optical mark recognition, a novel user interface, and crowdsourced data validation to rapidly digitize and extract data from paper COVID-19 assessment forms at a large medical center.

The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types.

Substantial research has been performed about the impact of computerized physician order entry on medication safety in the inpatient setting; however, relatively little has been done in ambulatory care, where most medications are prescribed.

We characterize physician workflow in two distinctive emergency departments (ED). Physician practices mediated by electronic health records (EHR) are explored within the context of organizational complexity for the delivery of care.

Limited research exists on patient knowledge/cognition or "getting inside patients' heads." Because patients possess unique and privileged knowledge, clinicians need this information to make patient-centered and coordinated treatment planning decisions. To achieve patient-centered care, we characterize patient knowledge and contributions to the clinical information space.

Handoffs or care transitions from the operating room (OR) to intensive care unit (ICU) are fragmented and vulnerable to communication errors. Although protocols and checklists for standardization help reduce errors, such interventions suffer from limited sustainability. An unexplored aspect is the potential role of developing personalized postoperative transition interventions using artificial intelligence (AI)-generated risks.