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Search found 9 items
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 developed a natural language processing electronic health record search tool that automatically identifies and ranks relevant clinical information based on a patient’s presenting complaint within the emergency department setting.
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.
This project developed, implemented, and evaluated a voice-generated enhanced electronic note system and found that it did not improve the time to finalize notes or clinician satisfaction.
This research evaluated the implementation and effectiveness of a clinical decision support tool designed to support the delivery of recommended care to hospitalized patients with heart failure, regardless of the reason for hospitalization.
This project created a natural language processing-enabled clinical decision support system to pull patient information and determine recommendations for cervical cancer screening, and demonstrated improvement in overall screening and surveillance rates.
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 project team successfully developed and implemented an automated system for measuring the rate of adverse drug events in pediatric patients.
Assessed the impact of Medical Imaging Informatics on health care costs and quality and developed a business case related to the acquisition and implementation of automated radiology systems; developed a financial model to demonstrate the impact of these systems on provider systems and health care quality.