Tracking workflow during high-stakes resuscitation: the application of a novel clinician movement tracing tool during in situ trauma simulation
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Bibliographic 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it