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ForwardIdentification of IV fluid contamination in complete blood counts and subsequent unnecessary red blood cell transfusions using artificial intelligence
Specimens contaminated with intravenous (IV) fluids can lead to considerable measurement errors in complete blood counts (CBCs), posing challenges for laboratory operations and clinical decision-making. There is no gold-standard method for retrospectively identifying these events, making it difficult to target quality improvement initiatives or optimize laboratory detection protocols.
Target Audience
Healthcare Professionals
Activity Disclosure
No commercial support has been accepted related to the development or publication of this activity.
CME Editor, Julie Katz Karp MD reported no conflicts of interest or financial relationships relevant to this article. This activity underwent peer review in line with the standards of editorial integrity and publication ethics. Conflicts of interest have been identified and resolved in accordance with John Wiley and Sons, Inc.’s Policy on Activity Disclosure and Conflict of Interest.
Learning Objectives
Explore machine learning models to retrospectively identify IV fluid contamination in CBC results
Course summary
John Wiley & Sons, Inc. is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
John Wiley and Sons, Inc. designates this journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit™. Physicians should only claim credit commensurate with the extent of their participation in the activity. For information on applicability and acceptance of continuing medical education credit for this activity, please consult your professional licensing board.
This activity is designed to be completed within 1 hour. To successfully earn credit, participants must access the activity on https://www.wileyhealthlearning.com and complete the activity during the valid credit period, which is up to three years from initial publication. Additionally, up to 3 attempts and a score of 70% or better is needed to pass the post test and claim CC credit. [ACGME competency/CC category: Medical Knowledge].