Data-Driven Transfusion Medicine: Looking to the Future

 

Blood transfusion is cited as one of the most overused healthcare interventions, making patient blood management (PBM) a prime target for informatics-based approaches and artificial intelligence (AI) solutions. During this presentation, we’ll probe PBM strategies, AI use cases, and the current state of the field of transfusion medicine, including AI limitations. An analysis of a novel data visualization tool that can provide unique insights into PBM practice performance will be presented.

Lecture Presenter

Ryan A. Metcalf, MD, CQA(ASQ)

Ryan A. Metcalf, MD, CQA(ASQ)

Associate Professor (Clinical)
University of Utah School of Medicine
Section Chief
Transfusion Medicine
Medical Director, Transfusion Service/Blood Bank
ARUP Laboratories

Dr. Ryan A. Metcalf is a medical director of the ARUP Blood Services and Immunohematology Reference Lab and an associate professor (clinical) of pathology at the University of Utah School of Medicine. Dr. Metcalf graduated from the University of California Davis School of Medicine with a medical degree before completing an anatomic and clinical pathology residency at Stanford University School of Medicine. Dr. Metcalf has completed two fellowships at Stanford University, including a pathology fellowship and a transfusion medicine fellowship. Dr. Metcalf is board certified in blood bank/transfusion medicine and in clinical pathology. His research interests include patient blood management and data-driven quality management.

Objectives

After this presentation, participants will be able to:

  • Describe common informatics-based patient blood management interventions you can implement at your hospital
  • Appraise the current state of artificial intelligence and machine learning in transfusion medicine
  • Analyze how a novel data visualization tool can provide unique insights into PBM practice performance

Sponsored by:

University of Utah School of Medicine, Department of Pathology, and ARUP Laboratories