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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 informed Stage 3 Meaningful Use requirements by evaluating metrics related to identification of delirium in real time and improving accuracy of the problem list, thus potentially improving care for patients with delirium.
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 evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.
Shared an electronic medical records system that improved patient safety and quality of care. Also served as a critical learning tool for clinicians in a coalition of three large health organizations and 24 primary care clinics in northern Iowa.
Designed a system-wide patient-centered planning process and an EHR implementation plan that securely exchanged patient information within and across diverse health care settings for the Hancock County Memorial Hospital, in addition to 21 affiliated physician health organization clinics.