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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.

We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations.

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.

To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data.

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 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).

As health information technologies become more prevalent in physician workflow, it is increasingly important to understand how physicians are using and interacting with these systems. This includes understanding how physicians search for information presented through health information technology systems. Eye tracking technologies provide a useful technique to understand how physicians visually search for information. However, analyzing eye tracking data can be challenging and is often done by measuring summative metrics [...]

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.