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Endoscopic submucosal dissection: a cognitive task analysis framework toward training design



One of the major impediments to the proliferation of endoscopic submucosal dissection (ESD) training in Western countries is the lack of sufficient experts as instructors. One way to address this gap is to develop didactic systems, such as surgical simulators, to support the role of trainers. Cognitive task analysis (CTA) has been used in healthcare for the design and improvement of surgical training programs, and therefore can potentially be used for design of similar systems for ESD.


The aim of the study was to apply a CTA-based approach to identify the cognitive aspects of performing ESD, and to generate qualitative insights for training.

Materials and methods

Semi-structured interviews were designed based on the CTA framework to elicit knowledge of ESD practitioners relating to the various tasks involved in the procedure. Three observations were conducted of expert ESD trainers either while they performed actual ESD procedures or at a training workshop. Interviews were either conducted over the phone or in person. Interview participants included four experts and four novices. The observation notes and interviews were analyzed for emergent qualitative themes and relationships.


The qualitative analysis yielded thematic insights related to four main cognition-related categories: learning goals/principles, challenges/concerns, strategies, and decision-making. The specific insights under each of these categories were systematically mapped to the various tasks inherent to the ESD procedure.


The CTA approach was applied to identify cognitive themes related to ESD procedural tasks. Insights developed based on the qualitative analysis of interviews and observations of ESD practitioners can be used to inform the design of ESD training systems, such as virtual reality-based simulators.

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The authors gratefully acknowledge the support of this study by NIH/NCI grant R01CA197491. The authors are grateful to Dr. Stavros N. Stavropoulos for providing his insights on the ESD procedure and utility of the CTA for training design, and for facilitating, in part, the data collection for this study.

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Correspondence to Sudeep Hegde.

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Dr. Sudeep Hegde reports grants and personal fees from the National Cancer Institute, during the conduct of the study. Dr. Cullen Jackson reports grants from the NIH/National Cancer Institute, during the conduct of the study; personal fees from Aptima, Inc., outside the submitted work. Drs. Sudeep Hegde, Mark A. Gromski, Tansel Halic, Zhaohui Xia, Melih Turkseven, Mandeep S. Sawhney, Daniel B. Jones, Suvranu De, and Mr. Berk Cetinsaya have no conflicts of interest or financial ties to disclose.

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Decision tables

Factors affecting knife choice


Lesion type

Organ wall





Thick wall





Thin wall




  1. Choice of knife
  2. Bold indicates aggressive knife (e.g., Hook)—more precise, cutting, long tip
  3. Italics indicates non-aggressive knife (e.g., Dual, IT/IT2/IT-Nano)—less precise, burning, short tip

Positioning the patient—gravity

Useful for

Not useful for

Small to mid-size lesions

Too small (weight does nothing), and too large and bulky

Direction of cut/dissection



If gravity can be used to pull lesion down

Start distal

Start proximal

Bottom first, then top and come down

Circumferential cutting or tunneling method

Adequate traction + good fluid retention

Inadequate traction/poor fluid retention



Currents: cut and coag




High coag (Force coag, Swift, etc.)

Blended cut (Endo-cut)—adjustable coag and cut

Dry cut

Knife-cut or coag

Artery or big vein

Small venules (narrower than knife tip)

Skeletonize using coag-graspers or forceps

Cut through—knife tip should coagulate the vessel

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Hegde, S., Gromski, M.A., Halic, T. et al. Endoscopic submucosal dissection: a cognitive task analysis framework toward training design. Surg Endosc 34, 728–741 (2020).

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  • Endoscopic surgery training
  • Endoscopic submucosal dissection
  • Gastrointestinal cancer
  • ESD
  • Human factors
  • Cognitive task analysis