Search found 15 items
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 developed and tested a tablet-based decision aid to assist primary care providers in applying patient-reported outcomes to smoking cessation and found that the tool facilitated more conversations about smoking cessation between patients and providers.
This project tested a pediatric voice therapy telehealth system and found that it was feasible to implement and well accepted by children and their families.
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 study developed electronic medical record-based quality indices for eleven cardiovascular primary care services. It related physicians’ prior index scores to subsequent disease incidence and to care utilization in their patients.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.
This project, part of five grants awarded by the Agency for Healthcare Research and Quality to conduct electronic prescribing (e-prescribing) pilots, tested e- prescribing standards within small, community-based practices.