Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CTs with Intracranial Hemorrhage
Thomas J. O’Neill , Yin Xi, Edward Stehel, Travis Browning, Yee Seng Ng, Chris Baker, Ronald M. Peshock
Purpose: To determine how to optimize the delivery of machine learning techniques in a clinical setting to detect intracranial hemorrhage (ICH) on noncontrast enhanced CT to radiologists to improve workflow.
Materials and Methods: In this study, a commercially available machine learning algorithm to flag abnormal noncontrast CT examinations for ICH was implemented in a busy academic neuroradiology practice between September 2017 to March 2019. The algorithm was introduced in three places: (a) as a “pop-up” widget on ancillary monitors, (b) as a marked examination in reading worklists, and (c) as a marked examination for reprioritization based on the presence of the flag. A statistical approach was implemented based on a queuing theory to assess the impact of each intervention on queue-adjusted wait and turnaround time compared with historical controls.
Results: Notification with a widget or flagging the examination had no effect on queue-adjusted image wait (P > .99) or turnaround time (P = .6). However, a reduction in queue adjusted wait time was observed between negative (15.45 minutes; 95% confidence interval [CI]: 15.07, 15.38) and positive (12.02 minutes; 95% CI: 11.06, 12.97; P < .0001) artificial intelligence ICH detected examinations with reprioritization. Reduced wait time was present for all order classes but was greatest for examinations ordered as routine for both inpatients and outpatients due to their low priority.
Conclusion: The approach used to present flags from AI and machine learning algorithms to the radiologist can reduce image wait time and turnaround times.
Read Full Article Here: https://doi.org/10.1148/ryai.2020200024