Scientific Director, Automation & Special Projects, ARUP Laboratories, Professor (Adjunct) of Pathology, University of UtahBiography
Dr. Charles Hawker is Scientific Director for Automation and Special Projects at ARUP, where he has been for 22 years. Dr. Hawker is also Professor (Adjunct) of Pathology in the University of Utah, School of Medicine. Previously, over a twenty year period, he held various positions in research and development and management at Laboratory Procedures, Inc. (Upjohn) and SmithKline Beecham Clinical Labs. At ARUP he has installed several major automation and robotic systems that have made ARUP one of the countrys most automated laboratories. He is a past president of the Association of Clinical Scientists, the National Academy of Clinical Biochemistry (NACB), and the Clinical Ligand Assay Society (CLAS). In July, 2014 he will received the AACCs highest award: Outstanding Lifetime Contributions to Clinical Chemistry and Laboratory Medicine, and AACCs MSPSD Division award for Outstanding Contributions to Management Sciences and Patient Safety. He has been honored by the Association of Clinical Scientists, Clinical and Laboratory Standards Institute (CLSI), NACB, and the Association for Laboratory Automation. He has chaired automation committees in CLSI and Health Level 7. He is the author of a chapter on clinical laboratory automation in the December, 2007 issue of Clinics in Laboratory Medicine and co-author of chapters in the Tietz Textbook of Clinical Chemistry and Molecular Diagnostics (4th and 5th Editions) and the Tietz Fundamentals of Clinical Chemistry (6th and 7th Editions). He is a frequent lecturer on laboratory automation to national and international audiences. He has three issued patents and has published 43 peer reviewed papers, 14 book chapters or invited reviews, 2 invited editorials, and 47 abstracts. His most recent research efforts have focused on the use of machine vision systems for automated quality inspection of clinical laboratory specimens, particularly the development of an automated camera system that uses optical character recognition (OCR) to identify mislabeled specimens.