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This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
This project aimed to capture and understand how clinical work is actually done, and then to analyze how the efficiency and quality of care could be measurably improved through health information technology.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.
Implemented an ambulatory computer physician order entry (ACPOE) system with clinical decision support capabilities in an ambulatory, community-based, integrated health-system; evaluated the impact of the system both internally, on organizational processes and human factors, and externally, on patient safety as measured by medication errors and adverse drug events.