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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 help cancer registrars efficiently and accurately identify reportable cancer cases.

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.

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

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.

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.