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Implementation of an Institution-Wide Rules-Based Automated CT Protocoling System.

Ryan Chung1, John P Demers2, Roberta Tiberio2

  • 1Department of Radiology, Division of Abdominal Imaging, Massachusetts General Hospital, 55 Fruit St, White 270, Boston, MA 02114.

AJR. American Journal of Roentgenology
|January 17, 2024
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Summary

An automated CT protocoling system significantly reduced radiologists' workload and decreased examination times. The system proved efficient with infrequent protocol errors, improving overall care delivery.

Keywords:
CTautomated protocolingautomatic protocolingprotocolingworkflow

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Area of Science:

  • Radiology
  • Medical Imaging
  • Health Informatics

Background:

  • Radiologists face increased workload and workflow inefficiencies due to manual examination protocoling.
  • Automated protocoling systems aim to streamline the process and reduce noninterpretive tasks.

Purpose of the Study:

  • To evaluate the impact of an automated CT protocoling system on examination process times.
  • To assess the protocol error rates associated with automated versus manual CT protocoling.

Main Methods:

  • Retrospective analysis of 317,597 CT examinations over two years.
  • Implementation of a rules-based automated protocoling system within the electronic health record (EHR).
  • Comparison of process times, quality improvement (QI) reports, and recalls between automated and manual protocoling.

Main Results:

  • Automatic protocoling increased from 27.4% to 64.5% over the study phases (p < .001).
  • Automated protocoling significantly reduced times from order entry to protocol assignment and examination completion across all patient types (p < .001).
  • Protocol error rates, indicated by QI reports and recalls, were infrequent for automated protocoling.

Conclusions:

  • The automated protocoling system effectively reduced radiologists' workload and improved efficiency.
  • The system demonstrated a high degree of accuracy, comparable to manual protocoling.
  • Automated CT protocoling offers a viable solution for enhancing care efficiency by minimizing noninterpretive tasks.