Patients supported by extracoporeal membrane oxygenation (ECMO) or a left ventricular assist device (LVAD) have an incredible amount of data associated with their clinical course. These are important life-saving treatments for many patients, but are associated with a great deal of morbidity and mortality. During my research year during medical school I worked with the team at University of Rochester to use modern big data analytical techniques combined with machine learning to create predictive models capable of providing insight to aid in the complex clinical decision making. We created databases directly from the EHR at an unprecedented scale for this patient population, such as over 3 million lab values and over 500k echo/cath results to try and improve patient care.

Dr. Ghazi and his team of biomechanical engineers have made incredible progress in the development of high-fidelity surgical simulation models. These 3D printed models are built from actual patient CT scans and can be sutured, cauterized and will bleed just like real tissue. I worked with his incredibly skilled team to help develop a cardiac model to create a realistic surgical simulation for cardiac procedures without the need of animal models. The beating of the heart is controlled by a custom code run on an arduino with an associated touch screen user interface to let the practicing surgeons control rate and rhythm.

Dr. Wakeman and the URMC pediatric surgery team have created flowsheet algorithms for determining when a pediatric trauma patient requires imaging. Implementation of these clinical resources in the ED, which describe the proper utilization of imaging for this patient population, has decreased unnecessary radiation for our pediatric patients. I worked with the team to create an easily distributed and user-friendly mobile app for these algorithms. The interactive mobile app was built on the react native framework and is currently in clinical beta testing in the ED.