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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 will study the usability of electronic health records (EHRs) by cardiac care physicians and nurses to develop a set of best practices in EHR design to inform vendors of the wants and needs of clinical providers.
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.
Establishes a Web-based electronic medical record system for 10 small rural hospitals to connect them to the area's regional medical center. The project's ultimate goal is to quickly give alImplements a regional health information exchange among an established collaborative of hospitals, clinics, and providers across Nebraska's remote 14,000-square-mile western panhandle; also helps participating providers acquire the equipment and other resources necessary to share laboratory and pharmaceutical data, as well as electronic medical records.
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.