Michael Mathis, M.D.

Assistant Professor
Medical SchoolAnesthesiologyResearch
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Dr. Mathis has research interests in improving perioperative care for patients with advanced cardiovascular disease, particularly for patients with heart failure. As part of the Multicenter Perioperative Outcomes Group (MPOG), an international consortium of perioperative databases for which UM serves as the coordinating center, he plays a lead role in integration of MPOG data with data from national cardiac and thoracic surgery registries. He also has interests in leveraging novel data science methods to understand patterns within highly granular intraoperative physiologic data, studying hemodynamic responses to surgical and anesthetic stimuli as a means for early detection of cardiovascular diseases such as heart failure. Additionally, he serves on IHPI's Early Career Faculty Advisory Council (ECFAC).

  • M.D., University of Michigan
  • B.S.E., Biomedical Engineering, Duke University

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U-M Academic Affiliation(s)

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What are you thinking about?

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. 


Why is this interesting to you?

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.


What are the practical implications for healthcare?

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.


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