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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 evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.
Facilitates transfer of information among providers and patients in the Presque Isle community; implements a model of chronic care management; and educates area health care providers on how best to use current information systems to communicate with each other.
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.
Implemented an outpatient EMR in a rural health system using distinct phases to match the expected learning curve and to reduce the potential loss of practice productivity often associated with the implementation of an EMR; also collected data about patient safety, quality, access, cost, and productivity.