Off-console automated artificial intelligence enhanced workflow enables improved emergency department CT capacity

Purpose: Increasing CT capacity to keep pace with rising ED demand is critical. The conventional process has inherent drawbacks. We evaluated an off-console automated AI enhanced workflow which moves all final series creation off-console. We hypothesized the off-console workflow would 1) decrease overall ED CT exam begin to end times and decrease length and variability of time CT is occupied at the individual exam level. Methods: Study population was identified retrospectively and included all CT exams done on all ED adult patients. 3 months of data was collected using the conventional workflow and 3 months of data was collected after implementation of the off-console workflow. Exam begin and the exam end timestamps were collected from the EMR. Additionally, 4 subgroups from the above conventional and off-console workflows were identified retrospectively with an Emergency Severity Index level 1, undergoing one of the four most common CT exam set(s) performed on ESI level 1 patients. Results: 6,795 ED adult patients underwent ED CT in the 3 months immediately prior to implementation of the off-console workflow and 6,708 adult ED patients underwent CT in the 3 months after complete implementation. The off-console workflow demonstrated a 36% decrease in median exam begin to end times (P < 0.001). 4 subgroups demonstrated 56–75% decreases in median CT occupied time (P < 0.001) and decreases in variability in ¾ subgroups. Discussion: This off-console workflow enables increased CT capacity to meet rising ED demand. Similar improvements could be expected across most exam sets and imaging settings if broadly implemented.