Presentation Title: Decentralized Digital Health Study to Predict Nursing Burnout Using Wearables

Speakers:

• Arjun Athreya, Ph.D., M.S.

  Senior Associate Consultant in the Department of Molecular Pharmacology and Experimental Therapeutics

  Dr. Arjun Athreya is a senior associate consultant in the Department of Molecular Pharmacology and Experimental Therapeutics, and assistant professor of pharmacology and psychiatry in the Mayo Clinic College of Medicine. Dr. Athreya leads the Health Engineering and Analytics Laboratory (HEAL) which develops technologies in artificial intelligence and machine learning for aiding precision medicine initiatives. Dr. Athreya’s laboratory designs technologies by envisioning the needs of patients and providers and by collaborating closely with clinicians, bioethicists and basic science faculty members. Dr. Athreya leads these efforts through novel design of decentralized digital health studies. The laboratory conducts participatory design studies to gather design and usability perspectives of health care systems and dashboards. Dr. Athreya received his Doctor of Philosophy in electrical and computer engineering from the University of Illinois at Urbana-Champaign, and Master of Science from Carnegie Mellon University. During his doctoral program at Illinois, he was the inaugural Fellow of the Mayo-Illinois Alliance
  
   

• William Bobo, M.D., M.P.H.

• Cecilia and Dan Carmichael Family Associate Director for the Center for Individualized Medicine in Florida

  Dr. William Bobo is the Cecilia and Dan Carmichael Family Associate Director for the Center for Individualized Medicine in Florida. He is also a professor and consultant in Psychiatry and Psychology. The research interests of Dr. Bobo are focused on examining and predicting the clinical effects — both beneficial and harmful — of medications used to treat people with severe mood disorders. Dr. Bobo's research examines the effects of selective serotonin reuptake inhibitors (SSRIs) and serotonin and norepinephrine reuptake inhibitors (SNRIs) on vulnerable populations such as pregnant women and applies artificial intelligence and other advanced computational approaches to health care. In collaboration with researchers at the University of Illinois at Urbana-Champaign, Dr. Bobo has developed a machine learning workflow that has shown considerable promise for accurately predicting the eventual treatment outcome of adults with major depression who receive SSRI or SNRI antidepressants. This workflow is now being turned into a practical tool that may aid clinical decision-making. Dr. Bobo's epidemiology studies are addressing important questions about the fetal, neonatal and developmental safety of antidepressants that are commonly used by pregnant women.

Upon conclusion of this activity, participants should be able to:

•Explain practical applications of genomics and pharmacogenomics for individualizing patient care.

•Define strategies to overcome obstacles to implementing genomic sequencing for patient care.

•Describe new and emerging technologies that promote individualized care for patients.

•Discuss ethical, legal, and social issues arising from the translation of individualized medicine into the clinical practice and the promotion of molecular, cellular, and genomic medicine research.

ATTENDANCE / CREDIT
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TRANSCRIPT
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