Evaluations for New Healthcare Environment Commissioning and Operational Decision Making Using Simulation and Human Factors: A Case Study of an Interventional Trauma Operating Room
Bibliographic record
Abstract
PURPOSE: The aim of this article is to provide a case study example of the preopening phase of an interventional trauma operating room (ITOR) using systems-focused simulation and human factor evaluations for healthcare environment commissioning. BACKGROUND: Systems-focused simulation, underpinned by human factors science, is increasingly being used as a quality improvement tool to test and evaluate healthcare spaces with the stakeholders that use them. Purposeful real-to-life simulated events are rehearsed to allow healthcare teams opportunity to identify what is working well and what needs improvement within the work system such as tasks, environments, and processes that support the delivery of healthcare services. This project highlights salient evaluation objectives and methods used within the clinical commissioning phase of one of the first ITORs in Canada. METHODS: A multistaged evaluation project to support clinical commissioning was facilitated engaging 24 stakeholder groups. Key evaluation objectives highlighted include the evaluation of two transport routes, switching of operating room (OR) tabletops, the use of the C-arm, and timely access to lead in the OR. Multiple evaluation methods were used including observation, debriefing, time-based metrics, distance wheel metrics, equipment adjustment counts, and other transport route considerations. RESULTS: The evaluation resulted in several types of data that allowed for informed decision making for the most effective, efficient, and safest transport route for an exsanguinating trauma patient and healthcare team; improved efficiencies in use of the C-arm, significantly reduced the time to access lead; and uncovered a new process for switching OR tabletop due to safety threats identified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".