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Simulation-based training intervention using artificial intelligence to improve clinical bronchoscopy performance: a pre-postintervention study

Por: Cold · K. M. · Arshad · A. · Kildegaard · C. · Laursen · C. B. · Konge · L. · Nielsen · A. B.
Objectives

Does a simulation-based training intervention with an artificial intelligence (AI) navigation system improve their clinical bronchoscopy performance? And can the AIs outcome measures be used to evaluate clinical performance?

Design

Pre–postintervention study.

Setting

Odense University Hospital of Southern Denmark, pulmonary endoscopy suite.

Participants

Nine bronchoscopists (4 experienced, >500 bronchoscopies and 5 intermediates, 10–500 bronchoscopies).

Primary outcome measures

Diagnostic completeness (DC), structured progress (SP), procedure time (PT) and procedure efficiency (DC/PT).

Results

The primary outcome measures showed no statistically significant difference between the pre- and postintervention bronchoscopies DC: 53% versus 59%, p=0.16, SP: 29% versus 32%, p=0.35 and PT: 219 s versus 181 s, p=0.22. The experienced outperformed the intermediates regarding DC: 73% versus 43%, p

Conclusions

DC, SP and PT showed no statistically significant difference after a simulation-based training intervention. DC, SP and procedure efficiency differentiated between experienced and intermediate bronchoscopists and can be used to evaluate clinical bronchoscopy performance.

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