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This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
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 study assessed the usability and impact of inpatient portals on patient experience, engagement, and perceptions of care.
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.
This project looked at the ability of EHRs to facilitate patient outcomes tracking, improve provider communication, reduce medical errors, and improve quality of care.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.
Expands upon an electronic medical records-sharing initiative for high-risk infants and their families in Mississippi, linking new health centers and clinics and serving a rural area that spans 17 counties; uses telemedicine technologies to enhance evidence-based developmental care for newborns in acute care hospitals; and creates Web-based decision support resources for physicians who care for infants.
Evaluated the effects of a Web portal-based patient empowerment program and EMR system on quality of care, patient safety, and utilization for patients with diabetes and physicians in primary care practices.