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To assess the relationship between (1) task demands and workload, (2) task demands and performance, and (3) workload and performance, all during physician-computer interactions in a simulated environment.

The sexual transmission of enteric diseases poses an important public health challenge. We aimed to build a prediction model capable of identifying individuals with a reported enteric disease who could be at risk of acquiring future sexually transmitted infections (STIs).

Maintaining patient privacy is a challenge in large-scale observational research. To assist in reducing the risk of identifying study subjects through publicly available data, we introduce a method for obscuring date information for clinical events and patient characteristics.

Communication inequalities deepen health disparities even when internet access is achieved. The goal of this study is to understand how a range of barriers may inhibit individuals from low socioeconomic position (SEP) from engaging with online health information even when it is freely available.

Traditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled training datasets. We demonstrate the feasibility of utilizing semi-automatically labeled training sets to create phenotype models via machine learning, using a comprehensive representation of the patient medical record.

To describe the creation and evaluate the usage of the first medical wiki linked to dedicated mobile applications.

The management of HIV infection requires extensive, longitudinal information record-keeping and coordination to ensure optimal monitoring and outcomes of care and treatment.

To assess the public's preferences regarding potential privacy threats from devices or services storing health-related personal data.

This study assessed whether having an electronic health record (EHR) super-user, nurse champion for meaningful use (MU), and quality improvement (QI) team leading MU implementation is positively associated with MU Stage 1 demonstration.