Laparoscopic Nissen fundoplication assessment: task analysis as a model for the development of a procedural checklist.

ABSTRACT

BACKGROUND: Learning an advanced laparoscopic procedure is a complex process that requires clinical exposure, direct teaching, and deliberate practice. Expert surgeons automate their knowledge, making it difficult to teach incremental steps. Our aim was to deconstruct the steps of a laparoscopic Nissen fundoplication (LNF) and develop a procedural checklist assessment instrument. METHODS: A behavioral task analysis was conducted with five experts using the Delphi technique to identify all steps of a LNF. The Delphi survey included video analysis of expert performance, two electronic iterative rounds and final group interview to reach consensus. The created checklist was then used to assess the performance of 14 general surgery residents. Participants viewed a brief instructional video and performed a LNF on a porcine model. Laparoscope video recordings were evaluated by a blinded investigator using the created LNF checklist. RESULTS: The task analysis produced a 65-step procedural checklist with six major components (patient positioning and port placement, dissection of crura and esophagus, closure of crura, mobilization of fundus, orientation of fundoplication, and creation of fundoplication). Thirteen of 14 participants completed the procedure. Median score for all residents was 31 (range 13-38) with senior residents (36, 34-38) having significantly higher scores than junior residents (30, 13-36) (p = 0.0162). Most residents attempted the major components of the procedure; 13 of 14 dissected the crura and created the fundoplication, 12 closed the crura, and 11 mobilized the fundus. However, residents frequently failed to complete key elements such as protection of the vagus nerve or mediastinal mobilization of the esophagus. CONCLUSIONS: The task analysis and Delphi technique was successful in reaching expert consensus on the procedural steps of a LNF and in creating a valid checklist. By capturing automated knowledge in a checklist form, we can scaffold resident learning and improve feedback for an advanced laparoscopic case.