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This project will redesign approaches for collecting and using allergy information with the goal of improving healthcare quality and safety, including completeness and accuracy of allergy data.
This project will study the impact of errors in medical documents on quality of care and develop innovative natural language processing methods to automatically detect errors so that physicians can correct the documents before finalizing them in the electronic health record.
The research team developed and evaluated a natural language processing allergy module that was used to study different types of allergies in an electronic health record.
The overall goal of this study was to develop and assess a natural language processing application to facilitate medication reconciliation at the point of care.
This study investigated the feasibility of extracting medication information from non-structured electronic clinical sources within an electronic health record.
This demonstration project was conducted to evaluate whether the Unified Health Resource, a combined personal health record, an electronic medical record (EMR), and a communication system, led to more patient-centered care in rural communities in the Intermountain West.
Assessed the value of a computerized clinic order entry tool in rural primary care practices for appropriateness of antimicrobial therapy for acute respiratory infections, frequency of hemoglobin A1c in diabetics, incidence of outpatient adverse drug events, and influenza vaccine immunizations.