Search found 37 items
This project aims to refine and develop methods to address missing electronic health record data to improve data quality and research validity.
This project will redesign approaches for collecting and using allergy information with the goal of improving healthcare quality and safety, including completeness and accuracy of allergy data.
This project will develop a clinical decision support tool for the perioperative setting.
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 analyzed secondary data to identify factors associated with timely opening of electronic health record-based asynchronous alerts, timely response to the alerts, and patient outcomes.
This project will refine the Leapfrog Computerized Provider Order Entry (CPOE)/Electronic Health Record (EHR) test – a “flight simulator” for EHRs with CPOE which evaluates the safety performance of EHRs after deployment, with a particular focus on high impact patient safety and medication safety problems.
This project built an automated intervention that recognized critical imaging results that require additional testing and populated a discharge summary with recommendations, resulting in improved patient followup.
This project evaluated select Stage 3 Meaningful Use criteria in the Patient and Family Engagement, Care Coordination, and Interoperability domains and developed recommendations to improve them and increase their value to hospitals and practices implementing them.