Impact of Artificial Intelligence Triage on Radiologist Report Turnaround Time: Real-World Time Savings and Insights From Model Predictions.
Authors
Affiliations (5)
Affiliations (5)
- US FDA / Center for Devices and Radiological Health, White Oak, Maryland. Electronic address: [email protected].
- University of Chicago, Chicago, Illinois.
- University of Chicago, Chicago, Illinois; Division Chief of Cardiothoracic Imaging at University of California, San Diego Medical Center.
- Deputy Director Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, US FDA / Center for Devices and Radiological Health, White Oak, Maryland.
- US FDA / Center for Devices and Radiological Health, White Oak, Maryland.
Abstract
To quantify the impact of workflow parameters on time savings in report turnaround time due to an AI triage device that prioritized pulmonary embolism (PE) in chest CT pulmonary angiography (CTPA) examinations. This retrospective study analyzed 11,252 adult CTPA examinations conducted for suspected PE at a single tertiary academic medical center. Data was divided into two periods: pre-artificial intelligence (AI) and post-AI. For PE-positive examinations, turnaround time (TAT)-defined as the duration from patient scan completion to the first preliminary report completion-was compared between the two periods. Time savings were reported separately for work-hour and off-hour cohorts. To characterize radiologist workflow, 527,234 records were retrieved from the PACS and workflow parameters such as examination interarrival time and radiologist read time extracted. These parameters were input into a computational model to predict time savings after deployment of an AI triage device and to study the impact of workflow parameters. The pre-AI dataset included 4,694 chest CTPA examinations with 13.3% being PE-positive. The post-AI dataset comprised 6,558 examinations with 16.2% being PE-positive. The mean TAT for pre-AI and post-AI during work hours are 68.9 (95% confidence interval 55.0-82.8) and 46.7 (38.1-55.2) min, respectively, and those during off-hours are 44.8 (33.7-55.9) and 42.0 (33.6-50.3) min. Clinically observed time savings during work hours (22.2 [95% confidence interval: 5.85-38.6] min) were significant (P = .004), while off-hour (2.82 [-11.1 to 16.7] min) were not (P = .345). Observed time savings aligned with model predictions (29.6 [95% range: 23.2-38.1] min for work hours; 2.10 [1.76, 2.58] min for off-hours). Consideration and quantification of the clinical workflow contributes to the accurate assessment of the expected time savings in report TAT after deployment of an AI triage device.