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This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
The goal of this project is to generate a systematic and replicable process for transforming evidence-based research findings, including findings from patient-centered outcomes research, into shareable clinical decision support (CDS) standards and a publicly available CDS prototype.
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 project assessed the clinical and operational implications of electronic health record downtimes and developed a simulation model to support the creation of effective downtime contingency plans.
This study assessed the usability and impact of inpatient portals on patient experience, engagement, and perceptions of care.
This project provided input to inform the development of nine proposed Stage 3 MU objectives focused on patient engagement, interoperability, and care coordination.
This report was created to address the nationally significant challenge of developing comprehensive clinical datasets, collected in real world environments and accessible in real time, to support clinical research and to address public health concerns.
This project was a joint grant solicitation between the National Science Foundation and the Agency for Healthcare Research and Quality to fund research projects that address systems modeling in health services research, with a specific emphasis on the supportive role of health information technology.
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.