AI-assisted radiographic fracture detection and length of stay in the adult ambulatory orthopedic emergency department: a before-after cohort study with a disease-specific internal control.

Authors

Nadav Graif, Gil Rachevsky, Alexis Sandler, David Zeltser, Amal Khoury, Roy Gigi

Published in

International journal of medical informatics. Volume 221. Pages 106679. Aug 20, 2026. Epub Aug 20, 2026.

Abstract

Although artificial intelligence-assisted radiographic fracture detection tools (AI-RFDT) have demonstrated high diagnostic accuracy in adult limb radiography, real-world evidence regarding their operational impact in the adult emergency department (ED) remains limited.
We evaluated whether deploying an AI-RFDT reduced ED length of stay (LOS) and revisit rates in fracture-excluded (F-E) patients undergoing limb radiography, using fracture-confirmed (F-C) patients as a disease-specific internal control. This retrospective controlled before-after cohort study, with complementary interrupted time-series analysis, was conducted in a tertiary Ambulatory Orthopedic ED. We included 8253F-E and 6864F-C visits between January 2021 and February 2026. Fracture status was assigned from the treating physician's ICD-coded discharge diagnosis, without study-level re-adjudication against imaging. The primary outcome was ED LOS before versus after deployment on 1 October 2023; secondary outcomes were 24-hour, 7-day and 30-day revisits. Subgroup analyses were exploratory.
Mean F-E LOS decreased from 135.6 to 127.5 min (Δ -8.13 min; 95% CI -11.52 to -4.70; p < 0.001) and median LOS from 114.0 to 109.8 min (p < 0.001); the F-C control was unchanged (Δ + 0.37 min; 95% CI -3.95 to +4.74; p = 0.87); the cohort-by-period interaction was -7.99 min (95% CI -13.36 to -2.62; p = 0.004). The reduction was concentrated at the upper tail: no change at the 25th percentile and -24.0 min at the 90th (90th-25th difference -24.0 min; 95% CI -35.4 to -15.1); stays over four hours decreased from 11.3% to 8.2%. Interrupted time-series analysis showed a 19.2-min level decrease at deployment against a flat pre-existing trend, with no significant control change. Early revisits did not increase.
AI-RFDT deployment was associated with decreased ED LOS in F-E patients, concentrated in the distribution's slow tail. The unchanged F-C control is consistent with an F-E-specific mechanism, with no increase in early revisits. These associations do not establish causation.

PMID:
42632355
Bibliographic data and abstract were imported from PubMed on 23 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.