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We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality.

Electronic trigger detection tools hold promise to reduce Adverse drug event (ADEs) through efficiencies of scale and real-time reporting. We hypothesized that such a tool could automatically detect medication dosing errors as well as manage and evaluate dosing rule modifications.

Government agencies are rapidly developing web portals to proactively publish "open" data that are searchable, available in nonproprietary formats, and with unlimited use and distribution rights. In this dynamic environment, we aimed to understand the experiences of 2 early leaders in open health data, the US Department of Health and Human Services and the New York State Department of Health.

Our objective was to compare the change in research informed knowledge of health professionals and their intended practice following exposure to research information delivered by either Twitter or Facebook.

To develop a descriptive model of structural characteristics of mHealth in the context of newborn nutrition, and to assess the effects of illustrative interventions through a mixed-methods study consisting of an impact evaluation and a qualitative assessment.

Although omics datasets represent valuable assets for hypothesis generation, model testing, and data validation, the infrastructure supporting their reuse lacks organization and consistency. Using nuclear receptor signaling transcriptomic datasets as proof of principle, we developed a model to improve the discoverability, accessibility, and citability of published omics datasets. Primary datasets were retrieved from archives, processed to extract data points, then subjected to metadata enrichment and gap filling. The resulting secondary [...]

To examine changes in patient outcome variables, length of stay (LOS), and mortality after implementation of computerized provider order entry (CPOE).

To report on the state of the science of clinical decision support (CDS) for hospital bedside nurses.

Quantifying alert override has been the focus of much research in health informatics, with override rate traditionally viewed as a surrogate inverse indicator for alert effectiveness. However, relying on alert override to assess computerized alerts assumes that alerts are being read and determined to be irrelevant by users. Our research suggests that this is unlikely to be the case when users are experiencing alert overload. We propose that over time [...]

We have developed an interface to serve patient data from Informatics for Integrating Biology and the Bedside (i2b2) repositories in the Fast Healthcare Interoperability Resources (FHIR) format, referred to as a SMART-on-FHIR cell. The cell serves FHIR resources on a per-patient basis, and supports the "substitutable" modular third-party applications (SMART) OAuth2 specification for authorization of client applications. It is implemented as an i2b2 server plug-in, consisting of 6 modules: authentication [...]