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To demonstrate the potential of de-identified clinical data from multiple healthcare systems using different electronic health records (EHR) to be efficiently used for very large retrospective cohort studies.

The utility of healthcare utilization data from US emergency departments (EDs) for rapid monitoring of changes in influenza-like illness (ILI) activity was highlighted during the recent influenza A (H1N1) pandemic. Monitoring has tended to rely on detection algorithms, such as the Early Aberration Reporting System (EARS), which are limited in their ability to detect subtle changes and identify disease trends.

Adoption studies of social media use by clinicians were systematically reviewed, up to July 26th, 2011, to determine the extent of adoption and highlight trends in institutional responses. This search led to 370 articles, of which 50 were selected for review, including 15 adoption surveys. The definition of social media is evolving rapidly; the authors define it broadly to include social networks and group-curated reference sites such as Wikipedia. Facebook [...]

This paper explored pharmacy staff perceptions of the strengths and weaknesses of electronic prescribing (e-prescribing) design in retail pharmacies using the sociotechnical systems framework. This study examined how adoption of e-prescribing technology is affecting clinical practice and patient care.

Applying multiprofessional electronic health records (EHRs) is expected to improve the quality of patient care and patient safety. Both EHR systems and system users depend on semantic interoperability to function efficiently. A shared clinical terminology comprising unambiguous terms is required for semantic interoperability. Empirical studies of clinical terminology, such as predefined headings, in EHR systems are scarce and limited to one profession or one clinical specialty.

To present a framework for combining implicit knowledge acquisition from multiple experts with machine learning and to evaluate this framework in the context of anemia alerts.

Meaningful exchange of information is a fundamental challenge in collaborative biomedical research. To help address this, the authors developed the Life Sciences Domain Analysis Model (LS DAM), an information model that provides a framework for communication among domain experts and technical teams developing information systems to support biomedical research. The LS DAM is harmonized with the Biomedical Research Integrated Domain Group (BRIDG) model of protocol-driven clinical research. Together, these models [...]

Relation extraction in biomedical text mining systems has largely focused on identifying clause-level relations, but increasing sophistication demands the recognition of relations at discourse level. A first step in identifying discourse relations involves the detection of discourse connectives: words or phrases used in text to express discourse relations. In this study supervised machine-learning approaches were developed and evaluated for automatically identifying discourse connectives in biomedical text.

To determine the frequency with which computerized alerts occur and the proportion triggered as a result of prescribers not utilizing e-prescribing system functions.