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Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text [...]

To systematically review studies assessing the effects of health information technology (health IT) on patient safety outcomes.

Quantify the variability of patients' problem lists - in terms of the number, type, and ordering of problems - across multiple physicians and assess physicians' criteria for organizing and ranking diagnoses.

Describe the change in mobile technology used by an urban Latino population between 2011 and 2014, and compare findings with national estimates.

To examine whether patients invited to review their clinicians' notes continue to access them and to assess the impact of reminders on whether patients continued to view notes.

Increasing the use of generic medications could help control medical costs. However, educational interventions have limited impact on prescriber behavior, and e-prescribing alerts are associated with high override rates and alert fatigue. Our objective was to evaluate the effect of a less intrusive intervention, a redesign of an e-prescribing interface that provides default options intended to "nudge" prescribers towards prescribing generic drugs.

Reinventing data extraction from electronic health records (EHRs) to meet new analytical needs is slow and expensive. However, each new data research network that wishes to support its own analytics tends to develop its own data model. Joining these different networks without new data extraction, transform, and load (ETL) processes can reduce the time and expense needed to participate. The Informatics for Integrating Biology and the Bedside (i2b2) project supports [...]

A software tool is developed to facilitate data entry and to monitor research projects in under-resourced countries in real-time.

The Information Systems Department at Memorial Sloan Kettering Cancer Center developed the DARWIN Cohort Management System (DCMS). The DCMS identifies and tracks cohorts of patients based on genotypic and clinical data. It assists researchers and treating physicians in enrolling patients to genotype-matched IRB-approved clinical trials. The DCMS sends automated, actionable, and secure email notifications to users with information about eligible or enrolled patients before their upcoming appointments. The system also [...]