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This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
This project will formulate evidence-based recommendations for clinical decision support used by community pharmacist delivering medication therapy management. The goal is to reduce medication-related problems and improve health outcomes for chronically ill patients.
This project integrated a validated anxiety-specific screening tool in an existing clinical decision support system and tested it with a randomized feasibility pilot that found the tool did not increase detection of anxiety in pediatric primary care.
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.