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A major focus of health care today is a strong emphasis on improving the health and quality of care for entire patient populations. One common approach utilizes electronic clinical alerts to prompt clinicians when certain interventions are due for individual patients being seen. However, these alerts have not been consistently effective, particularly for less visible (though important) conditions such as hearing loss (HL) screening.

To examine medication errors potentially related to computerized prescriber order entry (CPOE) and refine a previously published taxonomy to classify them.

Infobuttons appear as small icons adjacent to electronic health record (EHR) data (e.g., medications, diagnoses, or test results) that, when clicked, access online knowledge resources tailored to the patient, care setting, or task. Infobuttons are required for "Meaningful Use" certification of US EHRs. We sought to evaluate infobuttons' impact on clinical practice and identify features associated with improved outcomes.

To examine changes in patient outcome variables, length of stay (LOS), and mortality after implementation of computerized provider order entry (CPOE).

Our objective was to compare the change in research informed knowledge of health professionals and their intended practice following exposure to research information delivered by either Twitter or Facebook.

To report on the state of the science of clinical decision support (CDS) for hospital bedside nurses.

To understand the different types and causes of prescribing errors associated with computerized provider order entry (CPOE) systems, and recommend improvements in these systems.

We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality.