The study will develop, validate, implement, and assess the usability of a machine learning based clinical decision support tool designed to reduce laboratory testing overutilization in pediatric intensive care unit patients.
This research will develop, evaluate, and test the efficacy of Empower NICU – A Bridge to Resources for Adjusting and Coping with Emotions (EmBRACE), a mobile health application designed to screen and monitor psychological symptoms in parents of infants hospitalized in the neonatal intensive care unit, identify those at risk, and connect parents with services, information, support, and resources.
This research will enhance an augmented reality headset used to provide real-time feedback on pediatric chest compressions. Researchers will evaluate the usability and user experience of the augmented reality cardiopulmonary resuscitation tool in an international multicenter randomized simulation study, with the aim of improving the quality of chest compressions and saving lives.
This research will develop and validate machine learning enhanced predictive models improving the allocation of critical care pharmacists to intensive care units to reduce adverse drug events.
This research will use artificial intelligence and machine learning to create prediction tools integrated into visualization dashboards to guide critical care pharmacists in preventing adverse drug events.
This research evaluates an artificial intelligence risk predictive tool called CoMET that uses visual outputs of patient data to serve as an early warning system for patients at risk of cardiac decompensation to allow for earlier intervention and reduction in morbidity and mortality.
This research implemented a clinical decision support tool to identify patients at risk for a life-threatening, uncommon cardiac arrhythmia.
This research study addressed the overuse of blood cultures to diagnose sepsis by developing an electronic health record-embedded clinical decision support tool that draws upon the strengths of analytical and naturalistic decision making.
This project evaluated the use of an in-room interactive monitor to improve patient-centered care and family engagement within a pediatric intensive care unit.
This research identified the data domains at greatest risk of communication errors during intensive care unit (ICU) patient rounds, developed the ability to conduct simulated ICU rounds for research and training, and found that there is a need for real-time simultaneous reviewing of data by all members of the rounding team to reduce communication errors.
