ArticleBMJ simulation & technology enhanced learning2019
Tracking workflow during high-stakes resuscitation: the application of a novel clinician movement tracing tool during in situ trauma simulation.
Article in BMJ simulation & technology enhanced learning, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 35 citations in OpenAlex.
- Article
- Deep Learning Analysis of Surgical Video Recordings to Assess Nontechnical Skills.JAMA network open · 2024Article
- Data-driven resuscitation training using pose estimation.Advances in simulation (London, England) · 2023Article
- Measuring teamwork for training in healthcare using eye tracking and pose estimation.Frontiers in psychology · 2023Article
- Translational simulation: from description to action.Advances in simulation (London, England) · 2021Article
- Latent safety threat identification during in situ simulation debriefing: a qualitative analysis.BMJ simulation & technology enhanced learning · 2021Article
- A prospective randomised simulation trial comparing our novel AIR-BOX to standard airway equipment storage modalities.BMJ simulation & technology enhanced learning · 2021Article
- Stress Testing the Resuscitation Room: Latent Threats to Patient Safety Identified During Interprofessional In Situ Simulation in a Canadian Academic Emergency Department.AEM education and training · 2020Article
- Considerations for psychological safety with system-focused debriefings.BMJ simulation & technology enhanced learning · 2020Article
- Laborious but Elaborate: The Benefits of Really Studying Team Dynamics.Frontiers in psychology · 2019Review
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Clinician movement and workflow analysis provides an opportunity to identify inefficiencies during trauma resuscitation care. Inefficient workflows may represent latent safety threats (LSTs), defined as unrecognised system-based elements that can negatively impact patients. In situ simulation (ISS) can be used to model resuscitation workflows without direct impact on patients. We report the pilot application of a novel, tracing tool to track clinician movement during high-fidelity ISS trauma sessions. Methods: Twelve unannounced ISSs were conducted. An open source, Windows-based video overlay tracing tool was developed to generate a visual representation of participant movement during ISS. This tracing tool used a manual mouse tracking algorithm to produce point-by-point location information of a selected participant in a video. The tracing tool was applied to video recordings of clinicians performing a cricothyroidotomy during ISS trauma scenarios. A comparative workflow and movement analysis was completed, which included distance travelled and space utilisation. This data was visually represented with time-lapsed movement videos and heat maps. Results: A fourfold difference in the relative distance travelled was observed between participants who performed a cricothyroidotomy during an ISS trauma resuscitation. Variation in each participant's movement was attributable to three factors: (1) team role assignment and task allocation; (2) knowledge of clinical space: equipment location and path to equipment retrieval; and (3) equipment bundling. This tool facilitated LST identification related to cricothyroidotomy performance. Conclusion: This novel tracing tool effectively generated a visual representation of participants' workflows and quantified movement during ISS video review. An improved understanding of human movement during ISS trauma resuscitations provides a unique opportunity to augment simulation debriefing, conduct human factor analysis of system elements (eg, tools/technology, physical environment/layout) and foster change management towards efficient workflows.
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