Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Noncontrast Computed Tomography.

Journal: Current problems in diagnostic radiology
Published Date: Nov 15, 2020

Abstract

OBJECTIVE: The timely reporting of critical results in radiology is paramount to improved patient outcomes. Artificial intelligence has the ability to improve quality by optimizing clinical radiology workflows. We sought to determine the impact of a United States Food and Drug Administration-approved machine learning (ML) algorithm, meant to mark computed tomography (CT) head examinations pending interpretation as higher probability for intracranial hemorrhage (ICH), on metrics across our healthcare system. We hypothesized that ML is associated with a reduction in report turnaround time (RTAT) and length of stay (LOS) in emergency department (ED) and inpatient populations.

Authors

  • Melissa A Davis
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar St. Tompkins East TE-2, New Haven, CT 06520.
  • Balaji Rao
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar St. Tompkins East TE-2, New Haven, CT 06520. Electronic address: balaji.rao@yale.edu.
  • Paul A Cedeno
    Department of Radiology and Biomedical Imaging ,Yale School of Medicine, Yale University, New Haven, CT 06520.
  • Atin Saha
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar St. Tompkins East TE-2, New Haven, CT 06520.
  • Vahe M Zohrabian
    Department of Radiology and Biomedical Imaging ,Yale School of Medicine, Yale University, New Haven, CT 06520.. Electronic address: vahe.zohrabian@gmail.com.

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