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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.
Developed a technology plan to improve access to maternal-fetal medicine services throughout the State and guided the implementation of telemedicine capabilities to provide real-time remote diagnostic ultrasound and consultative services to women with high-risk pregnancies.