Cecil Lynch, MD, MS

Dr. Lynch has a unique blend of deep clinical knowledge and technological expertise. At Accenture he is a Subject Matter Advisor on Big Data in Healthcare, Health and Clinical Analytics, Health Data Exchange and Connectivity. He serves as a domain expert in data standards, interoperability and enterprise architecture and is the Product Owner and Architect for the Accenture Microsoft Business Group H365 Health Platform. He serves as a National and International advisor on Health Informatics and has served numerous government agencies.

ABSTRACT

COVID-19 Clinical Analytics using Deep Learning and Graph Analytics – A deep dive on Deep Vein Thrombosis Complications

The COVID-19 pandemic has affected every one of us in some way but for the unfortunate people that are infected with the disease, the clinical course can be devastating. There are several long-term complications of the viral infection and of our body’s reaction to that virus. One of the most severe is the development of coagulation complications that lead to deep vein thrombosis (DVT), pulmonary embolism (PE) or stroke. There is a need to understand the incidence, risk factors and mitigation strategies for this complication that can lead to long-term need for anti-coagulation management or death. To that end we have developed an analytic approach that combines clinical data on over 20K COVID-19 patients, the corpus of literature on DVT COVID-19 and related coronaviruses, and genomic mutations associated with DVT and infection susceptibility. Our goals are to identify the most at risk populations, the driving factors that make these individuals high risk, and suggest a mitigation strategy that can be implemented clinically to reduce the risk of DVT, PE and stroke. I will present our analytic efforts around site of care optimization as this novel approach to analysis of DVT complications.