Search found 18 items
This project will identify orthopedic clinical outcome measures that are most important to patients, and study the impact on satisfaction and outcomes when this information is provided to patients and their doctors.
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 created a natural language processing-enabled clinical decision support system to pull patient information and determine recommendations for cervical cancer screening, and demonstrated improvement in overall screening and surveillance rates.
The project team developed automated methods for identifying relevant new information versus redundant information in electronic health record clinical notes.
This project tested three types of clinical decision support alerts and found that pop-up alerts were the most effective, but were the least preferred by dental providers.
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.