Search found 11 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 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 project evaluated the impact of an integrated care coordination information system (ICCIS) on the outcomes and satisfaction of patients with chronic and complex illnesses.
This project refined a set of asthma care quality measures and developed and validated the use of an automated method using natural language processing to utilize the measures.
This project evaluated the Pharmaceutical Safety Tracking (PhaST) system, which monitors medication safety in children and adolescents who are taking antidepressants.
This project investigated the feasibility and impact of novel approaches to clinician decision support in multidisciplinary ambulatory care, emphasizing high-risk transitions of care.
Demonstrates the value of an integrated outpatient and inpatient health information system by assessing adherence to evidence-based treatment guidelines for women who are group B streptococcus positive including inappropriate antibiotic use and screening in the outpatient setting, and cost-benefit analysis.