Our expert answers 3 Questions
My work focuses on understanding variation in perioperative anesthesia care among providers and institutions, and how these variations can impact outcomes. Additionally, my work uses novel data science methods to leverage the perioperative period as an opportunity to improve diagnosis and management of patients with cardiovascular disease, including heart failure. A challenge with heart failure is that at its onset, signs and symptoms can be subtle and non-specific. As a result, patients are under-diagnosed in early stages, and may not receive appropriate life-extending therapies.
Utilizing highly granular data from the intraoperative record, hemodynamic responses to cardiac stressors, such as a surgical incision, can be studied much in the same way a cardiologist might study a patient undergoing a cardiac stress test. Through innovative data science methods, computers can be trained to inspect these subtle patterns within the intraoperative record and potentially recognize patients with undiagnosed cardiovascular diseases, such as heart failure.
Heart failure is a common chronic condition with high rates of mortality and healthcare cost. Despite advances in treatment, little progress has been made to improve heart failure diagnosis. Through cost-effective and innovative data science methods applied to patient information already collected during perioperative period, an improved diagnosis of heart failure can lead to significant patient life-years and healthcare dollars saved.