Impact of AI-Triage on Radiologist Report Turnaround Time: Real-World Time-Savings and Insights from Model Predictions
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866912653953204224 |
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| author | Thompson, Yee Lam Elim Fergus, Jonathan Chung, Jonathan Delfino, Jana G. Chen, Weijie Levine, Gary M. Samuelson, Frank W. |
| author_facet | Thompson, Yee Lam Elim Fergus, Jonathan Chung, Jonathan Delfino, Jana G. Chen, Weijie Levine, Gary M. Samuelson, Frank W. |
| contents | Objective: To quantify the impact of workflow parameters on time-savings in report turnaround time (TAT) due to an AI-triage device that prioritized pulmonary embolism (PE) in chest CT pulmonary angiography (CTPA) exams. Methods: This retrospective study analyzed 11252 adult CTPA exams conducted for suspected PE at a single tertiary academic medical center. Data was divided into two periods: pre-AI and post-AI. For PE-positive exams, 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, 527234 records were retrieved from the PACS and workflow parameters such as exam inter-arrival time and radiologist read-time extracted. These parameters were input into a computational model to predict time-savings following deployment of an AI-triage device and to study the impact of workflow parameters. Results: The pre-AI dataset included 4694 chest CTPA exams with 13.3% being PE-positive. The post-AI dataset comprised 6558 exams with 16.2% being PE-positive. The mean TAT for pre-AI and post-AI during work hours are 68.9 [95% CI" 55.0, 82.8] and 46.7 [38.1, 55.2] minutes respectively, and those during off-hours are 44.8 [33.7, 55.9] and 42.0 [33.6, 50.3] minutes. Clinically-observed time-savings during work hours (22.2 [95% CI: 5.85, 38.6] minutes) were significant (p=0.004), while off-hour (2.82 [-11.1, 16.7] minutes) were not (p=0.345). Observed time-savings aligned with model predictions (29.6 [95% range: 23.2, 38.1] minutes for work hours; 2.10 [1.76, 2.58] minutes for off-hours). Discussion: Consideration and quantification of clinical workflow contribute to an accurate assessment of the expected time-savings in TAT following deployment of an AI-triage device. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15237 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Impact of AI-Triage on Radiologist Report Turnaround Time: Real-World Time-Savings and Insights from Model Predictions Thompson, Yee Lam Elim Fergus, Jonathan Chung, Jonathan Delfino, Jana G. Chen, Weijie Levine, Gary M. Samuelson, Frank W. Performance Objective: To quantify the impact of workflow parameters on time-savings in report turnaround time (TAT) due to an AI-triage device that prioritized pulmonary embolism (PE) in chest CT pulmonary angiography (CTPA) exams. Methods: This retrospective study analyzed 11252 adult CTPA exams conducted for suspected PE at a single tertiary academic medical center. Data was divided into two periods: pre-AI and post-AI. For PE-positive exams, 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, 527234 records were retrieved from the PACS and workflow parameters such as exam inter-arrival time and radiologist read-time extracted. These parameters were input into a computational model to predict time-savings following deployment of an AI-triage device and to study the impact of workflow parameters. Results: The pre-AI dataset included 4694 chest CTPA exams with 13.3% being PE-positive. The post-AI dataset comprised 6558 exams with 16.2% being PE-positive. The mean TAT for pre-AI and post-AI during work hours are 68.9 [95% CI" 55.0, 82.8] and 46.7 [38.1, 55.2] minutes respectively, and those during off-hours are 44.8 [33.7, 55.9] and 42.0 [33.6, 50.3] minutes. Clinically-observed time-savings during work hours (22.2 [95% CI: 5.85, 38.6] minutes) were significant (p=0.004), while off-hour (2.82 [-11.1, 16.7] minutes) were not (p=0.345). Observed time-savings aligned with model predictions (29.6 [95% range: 23.2, 38.1] minutes for work hours; 2.10 [1.76, 2.58] minutes for off-hours). Discussion: Consideration and quantification of clinical workflow contribute to an accurate assessment of the expected time-savings in TAT following deployment of an AI-triage device. |
| title | Impact of AI-Triage on Radiologist Report Turnaround Time: Real-World Time-Savings and Insights from Model Predictions |
| topic | Performance |
| url | https://arxiv.org/abs/2510.15237 |