Turnaround time prediction for clinical chemistry samples using machine learning.

Journal: Clinical chemistry and laboratory medicine
Published Date: Oct 12, 2022

Abstract

OBJECTIVES: Turnaround time (TAT) is an essential performance indicator of a medical diagnostic laboratory. Accurate TAT prediction is crucial for taking timely action in case of prolonged TAT and is important for efficient organization of healthcare. The objective was to develop a model to accurately predict TAT, focusing on the automated pre-analytical and analytical phase.

Authors

  • Eline R Tsai
    Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.
  • Derya Demirtas
    Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.
  • Nick Hoogendijk
    Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.
  • Andrei N Tintu
    Department of Clinical Chemistry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
  • Richard J Boucherie
    Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands.

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