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Search found 7 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 research studied errors in medical documents created with speech recognition software and developed natural language processing methods to detect such errors.
This research combined the artificial intelligence technology technique Dynamic Logic with natural language processing to create a model to predict risk of death over the next 12 months and found it was better than benchmark statistical and machine learning algorithms.
This project convened stakeholder panels to inform the development of an indications-enabled computerized prescriber order entry system.
This project expanded and modified the Child Health Improvement through Computer Automation (CHICA) system to assist pediatricians in identifying and managing four common medical-legal problems that may adversely impact child health, and found initial findings to be inconclusive.
This project implemented clinical decision support and clinical messaging to improve clinician reporting of notifiable conditions to public health agencies.
The project sought to determine if a computer decision support system integrated with routine care could improve standardized developmental screening during early well-child visits and surveillance for developmental disabilities at all pediatric visits.