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To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs.

Uncovering the dominant molecular deregulation among the multitude of pathways implicated in aggressive prostate cancer is essential to intelligently developing targeted therapies. Paradoxically, published prostate cancer gene expression signatures of poor prognosis share little overlap and thus do not reveal shared mechanisms. The authors hypothesize that, by analyzing gene signatures with quantitative models of protein-protein interactions, key pathways will be elucidated and shown to be shared.

A review of 2010 research in translational bioinformatics provides much to marvel at. We have seen notable advances in personal genomics, pharmacogenetics, and sequencing. At the same time, the infrastructure for the field has burgeoned. While acknowledging that, according to researchers, the members of this field tend to be overly optimistic, the authors predict a bright future.

We have reported that implementation of an electronic health record (EHR) based quality improvement system that included point-of-care electronic reminders accelerated improvement in performance for multiple measures of chronic disease care and preventive care during a 1-year period. This study examined whether providing pre-visit paper quality reminders could further improve performance, especially for physicians whose performance had not improved much during the first year.

Healthcare providers (HCPs) use online medical information for self-directed learning and patient care. Recently, the mobile internet has emerged as a new platform for accessing medical information as it allows mobile devices to access online information in a manner compatible with their restricted storage. We investigated mobile internet usage parameters to direct the future development of mobile internet teaching websites. Nephrology On-Demand Mobile (NOD(M)) (http://www.nephrologyondemand.org) was made accessible to all [...]

Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete.

Due to the high cost of manual curation of key aspects from the scientific literature, automated methods for assisting this process are greatly desired. Here, we report a novel approach to facilitate MeSH indexing, a challenging task of assigning MeSH terms to MEDLINE citations for their archiving and retrieval.

This paper describes the approaches the authors developed while participating in the i2b2/VA 2010 challenge to automatically extract medical concepts and annotate assertions on concepts and relations between concepts.

Clinical decision support systems can prevent knowledge-based prescription errors and improve patient outcomes. The clinical effectiveness of these systems, however, is substantially limited by poor user acceptance of presented warnings. To enhance alert acceptance it may be useful to quantify the impact of potential modulators of acceptance.