Search found 18 items
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 will apply machine learning against a large data set to develop a model to both understand and predict surgical cancellations on individual pediatric patients at two pediatric surgical sites.
This project will develop decision support tools that integrate with electronic health records to increase the quality and effectiveness of chronic pain care.
This study assessed the usability and impact of inpatient portals on patient experience, engagement, and perceptions of care.
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.
This project sought to reduce the use of emergency department services for non-urgent care by improving access to primary care physicians for Medicaid patients via the electronic medical record.
This project used a mixed-method approach to investigate the validity of using electronic health record data for diabetes performance measures.