Search found 17 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 research took an existing sepsis-related clinical decision support (CDS) and developed, tested, implemented, and validated a knowledge-based artificial intelligence-enhanced sepsis CDS.
This study assessed the usability and impact of inpatient portals on patient experience, engagement, and perceptions of care.
This project examined the prevalence of variation in electronic health record documentation in physician practice, its causes, effects, and strategies to mitigate its potential for harm.
This project tested the impact of a training module that teaches clinicians how to best communicate with patients in the presence of an electronic health record and found improvements in provider communication skills, but no impact on patient outcomes.
This project explored whether the use of data from pain management practices can be used to develop more robust evidence-based approaches to chronic pain management.
This study found increased rates for some screening and preventative services following adoption of federally-certified electronic health records.
The goal of this project was to inform development of several Stage 3 Meaningful Use patient engagement objectives.
This project assessed the readiness, feasibility, and perceived impact of achieving proposed Stage 3 Meaningful Use care coordination criteria among primary care practices.