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. We are using modern big data analytical techniques combined with machine learning to attempt to create predictive models capable of providing insight to aid in the complex clinical decision making. We are creating databases directly from the EHR at an unprecedented scale, 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 am helping work with his team to develop a model of the thoracic cavity to create a realistic surgical simulation for cardiothoracic procedures without the need of animal models.

Dr. Wakeman and the URMC team for pediatric surgery have created flowsheet algorithms for determining when a pediatric trauma patient needs imaging. These clinical resources describing the proper utilization of imaging for this patient population has decreased unnecessary radiation for pediatric patients. They wanted a way to easily distribute a user-friendly version of these algorithms so I developed an interactive mobile app built on the react native framework. The app is currently in beta testing on both iOS and Android.