As chief analytics officer, Dr. Clarke focuses on delivering innovative data and advanced analytic solutions that fulfill Highmark Health’s Living Health strategy—a model that blends the payer-provider ecosystem; provides more proactive, personalized, and cost-effective care for patients; and allows for better experiences for clinicians. Dr. Clarke is at the forefront of artificial intelligence and data strategy in health care and works closely with Highmark Health’s strategic partner, Google Cloud, to develop the next generation of high-quality care that will help solve for key issues such as data interoperability, patient flow, and social determinants of health.
Dr. Clarke initially trained as a neuroscientist and biophysicist. Since moving out of academia, his experience includes large-scale data/analytic transformations, building and scaling advanced analytic products, and building and managing data science teams. Dr. Clarke is passionate about delivering business impact through advanced analytics, growing leaders that connect analytics to business priorities, and building diverse teams.
Prior to arriving at Highmark Health in 2016, Dr. Clarke served in a leadership role at McKinsey & Company.
Dr. Clarke holds a B.S. in Neuroscience from Allegheny College and a Ph.D. in Neuroscience from the Center for Neural Basis of Cognition, a joint program between Carnegie Mellon University and the University of Pittsburgh. During his doctoral work, he published 4 peer-reviewed articles to investigate the functioning of NMDA receptors, which are neuronal mechanisms believed to be the basis of memory formation.
Based in the Pittsburgh area, Dr. Clarke and his wife, Shauna, have two children and one very loved dog. When not at work, Dr. Clarke enjoys golfing, fishing, and reading fantasy novels.
Thought Leadership Topics:
• Transforming health care with generative AI/AI
• Using AI to transform the workplace
• Responsible and ethical use of data
• Integrating advanced analytic insights into workflow systems
• Adoption and use of analytic solutions
• Building and maintaining Data Science teams
• The new role of the Analytic Translator
• Large scale data and analytic transformations
• Healthcare interoperability
• Predictive and Prescriptive modeling