Using structured progress to measure competence in flexible bronchoscopy

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Documents

  • Kristoffer Mazanti Cold
  • Morten Bo Søndergaard Svendsen
  • Uffe Bodtger
  • Leizl Joy Nayahangan
  • Paul Frost Clementsen
  • Konge, Lars

Background: Flexible bronchoscopy is a core invasive procedure in pulmonary medicine and training in the procedure is mandatory. Diagnostic completeness and procedure time have been identified as useful measures of competence. No outcome measures have been developed regarding navigational path in bronchoscopy to assess whether the bronchial segments have been identified in an arbitrary or structured order. We investigated whether a new outcome measure for structured progression could be used to assess competency in flexible bronchoscopy. Methods: The study was designed as a prospective comparative study. Twelve novices, eleven intermediates, and ten expert bronchoscopy operators completed three full bronchoscopies in a simulated setting on a phantom. The following outcome measures were collected through a checklist evaluation by a trained rater: Diagnostic Completeness as amount of visualized bronchial segments, Structured Progress between the bronchial segments in ascending order, and average intersegmental time (AIT). Results: The ability to follow a structured ascending path through the bronchial tree correlated with a higher amount of identified bronchial segments (Pearson's correlation, r=0.62, P<0.001) and a lower AIT (Pearson's correlation, r=−0.52, P<0.001). Conclusions: Operators should advance through the bronchial tree in a structured ascending order to ensure systematic progress with the highest level of diagnostic yield and the lowest procedure time. Structured progression is a useful measure to evaluate competency in flexible bronchoscopy.

Original languageEnglish
JournalJournal of Thoracic Disease
Volume12
Issue number11
Pages (from-to)6797-6805
ISSN2072-1439
DOIs
Publication statusPublished - 2020

    Research areas

  • Assessment, Education, Flexible bronchoscopy, Mastery learning, Simulation

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