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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.
The project team developed a technical brief on the current literature around core functionalities for pediatric electronic health records to describe current practice and to provide a framework for future research.
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.
The project team successfully developed a knowledgebase specific to medication management for children. The tools created included an algorithm for rounding of medication doses and a compounded medication knowledgebase.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.
This project developed a secure Web-based electronic health record for children with special health care needs called the Tennessee Child Health Profile.
Developed a detailed plan for the implementation and support of informatics tools in regional health centers including the creation of informatics tools to manage institutional surgical care information, creation of a multi-institutional partnership to manage both the informatics and surgical quality improvement programs, and the development of an economic model related to the business and safety benefits.