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First, to evaluate the effect of standard vs pictograph-enhanced discharge instructions on patients' immediate and delayed recall of and satisfaction with their discharge instructions. Second, to evaluate the effect of automated pictograph enhancement on patient satisfaction with their discharge instructions.

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 assess the variability in data distributions among data sources and over time through a case study of a large multisite repository as a systematic approach to data quality (DQ).

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

To determine problem list completeness related to chronic diseases in electronic medical records (EMRs) and explore clinic and physician factors influencing completeness.

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.