Cue Detection and Self-Debriefing Techniques in Virtual Simulation: Techniques Using Cognitive Engineering Inspired Expertise Development Approaches.

Introduction: Online experiential learning can benefit learners with scalable techniques to self-debrief and to develop cognitive skills in recognizing clinical cues.

Methods: We developed techniques for cue-detection exercises and self-debriefing, based on cognitive engineering-inspired expertise development approaches that focus on tacit knowledge, sensemaking, and mental models. Self-debriefing was structured by asking the learners to compare and then reflect on their choices and rationales against those provided by a panel of experts. Using these techniques, we generated scenario-based experiential learning materials in a virtual environment for a 2-hour module on deteriorating patient conditions that can lead to imminent cardiac arrests. The module was tested in a senior nursing course as an optional assignment. The volume of voluntarily submitted reflections by learners was used to assess engagement and subsequent thematic analysis to assess feasibility of the techniques.

Results: The module was completed by 189 of the 197 students invited (95.9%). Engagement level was high with all completed students submitting reflections after self-debriefing, most of which (between 53.4% and 87.8%) were specific enough for thematic analysis. The main theme of reflections was "missing something" in the scenario, followed by the themes of importance of reading the patient monitor and refining actions and priorities.

Conclusions: We demonstrated the feasibility of the techniques based on cognitive engineering-inspired approaches for virtual simulation learning in health care that structures self-debriefing by comparing a learner's situation assessment and responses with those of experts. The techniques have the potential to help learners in health care efficiently and consistently develop key critical thinking skills, especially those based on tacit knowledge to detect cues.