Search found 16 items
This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
This project developed, implemented, and evaluated a program that includes clinical decision support to improve diagnosis of hypertension in children.
This project integrated a validated anxiety-specific screening tool in an existing clinical decision support system and tested it with a randomized feasibility pilot that found the tool did not increase detection of anxiety in pediatric primary care.
This project developed a patient-centric tool called the Surgical Risk Preoperative Assessment System to estimate the risk of adverse operative outcomes.
This project assessed provider mental workload and performance while processing electronic abnormal test results and found that a test result tracking mechanism improved physicians’ clinical performance.
This project developed and pilot-tested a novel, outcomes-based emergency department triage tool and found that risk stratification and waiting times were improved for some patients.
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 developed and evaluated a clinical decision support system that effectively communicated genomic data to clinicians to improve healthcare decision making.
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.
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.