Jon D. Duke

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Drug-drug interactions (DDIs) are a common cause of adverse drug events. In this paper, we combined a literature discovery approach with analysis of a large electronic medical record database method to predict and evaluate novel DDIs. We predicted an initial set of 13197 potential DDIs based on substrates and inhibitors of cytochrome P450 (CYP) metabolism(More)
The entire drug safety enterprise has a need to search, retrieve, evaluate, and synthesize scientific evidence more efficiently. This discovery and synthesis process would be greatly accelerated through access to a common framework that brings all relevant information sources together within a standardized structure. This presents an opportunity to(More)
The vision of creating accessible, reliable clinical evidence by accessing the clincial experience of hundreds of millions of patients across the globe is a reality. Observational Health Data Sciences and Informatics (OHDSI) has built on learnings from the Observational Medical Outcomes Partnership to turn methods research and insights into a suite of(More)
BACKGROUND Medication-related decision support can reduce the frequency of preventable adverse drug events. However, the design of current medication alerts often results in alert fatigue and high over-ride rates, thus reducing any potential benefits. METHODS The authors previously reviewed human-factors principles for relevance to medication-related(More)
OBJECTIVE Drug-drug interaction (DDI) alerting is an important form of clinical decision support, yet physicians often fail to attend to critical DDI warnings due to alert fatigue. We previously described a model for highlighting patients at high risk of a DDI by enhancing alerts with relevant laboratory data. We sought to evaluate the effect of this model(More)
Patients on multiple medications are at increased risk for adverse drug events. While physicians can reduce this risk by regularly reviewing the side-effect profiles of their patients' medications, this process can be time-consuming. We created a decision support system designed to expedite reviewing potential adverse reactions through information(More)
OBJECTIVE Regenstrief Institute developed one of the seminal computerized order entry systems, the Medical Gopher, for implementation at Wishard Hospital nearly three decades ago. Wishard Hospital and Regenstrief remain committed to homegrown software development, and over the past 4 years we have fully rebuilt Gopher with an emphasis on usability, safety,(More)
Relationships between industry and university-based researchers have been commonplace for decades and have received notable attention concerning the conflicts of interest these relationships may harbor. While new efforts are being made to update conflict of interest policies and make industry relationships with academia more transparent, the development of(More)
The safe prescribing of medications via computerized physician order entry routinely relies on clinical alerts. Alert compliance, however, remains surprisingly low, with up to 95% often ignored. Prior approaches, such as improving presentational factors in alert design, had limited success, mainly due to physicians' lack of trust in computerized advice.(More)