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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.

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

To develop a secure, efficient, and easy-to-use data collection platform to measure the prevalence of sepsis in Wales over 24 hours.

Recent massive investment in electronic health records (EHRs) was predicated on the assumption of improved patient safety, research capacity, and cost savings. However, most US health systems and health records are fragmented and do not share patient information. Our study compared information available in a typical EHR with more complete data from insurance claims, focusing on diagnoses, visits, and hospital care for depression and bipolar disorder.

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.

To describe the characteristics and online practices of patients and "care partners" who share explicit access to a patient portal account at a large integrated health system that implemented shared access functionality in 2003.

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 illustrate ways in which clinical decision support systems (CDSSs) malfunction and identify patterns of such malfunctions.