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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.
Systematically assessed improvements in patient safety and experience of care associated with implementation of four decision support function embedded in an electronic health record: 1) the influence of weight based dosing on pediatric adverse drug events; 2) the influence of a test result tracking system on appropriate followup of ordered tests; 3) the influence of automated reminders on symptom monitoring and medications for children with asthma and attention deficit disorder.