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This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
This project developed dashboards to support clinical decision making in the emergency department and found that the new technology was readily accepted.
This project evaluated the impact of an electronic health record on the quality of diabetes care as measured by compliance with recommended processes of care and patient outcome measures.
This project used health information technology to identify patients for whom a diagnosis of prostate, lung, or colon cancer had been delayed.
The findings of this study demonstrated that electronic health record-based trigger methods can enable more meaningful measurement and surveillance of diagnostic errors in primary care.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.
This project explored the impact of an electronic health record on measurement-based care of individuals with major depressive disorder.
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.