Commissioning Clinical Spaces During a Pandemic: Merging Methodologies of Human Factors and Simulation
Bibliographic record
Abstract
OBJECTIVES: The objective of this case study is to demonstrate the value of applying tabletop and simulation techniques to highlight high-risk, high-impact outcomes and organizational recommendations in the commissioning of a new clinical spaces. PURPOSE/AIM: Generalizability of lessons learned from this case study aim to support other health organizations in commissioning of clinical spaces during communicable disease outbreaks. BACKGROUND: COVID-19 challenged our healthcare system, requiring teams to prepare in a short span of time. Bridging expertise of human factor and simulation teams provided a novel, interdisciplinary, and timely approach to evaluate and commission spaces. METHODS: Human factors and simulation teams were enlisted to conduct an evaluation of a new space prior to readiness for delivery of safe patient care. An adapted tabletop evaluation and subsequent systems integration simulation was conducted. The goal of the tabletop exercise was to identify and define processes and risks to tested in the physical space using simulation. RESULTS: Applying both human factors science and systems simulation proactively identified the highest risk, highest impact outcomes, validated existing processes and allowed for refining of potential solutions and recommendations of the new space. A strong working relationship between teams fostered an opportunity to share information, debrief, evaluate, and adapt methods while applying timely changes based on emergent findings. CONCLUSIONS: These combined methodologies are important tools that can be learned and applied to healthcare commissioning of new clinical spaces in the identification of high-risk, high-impact outcomes affecting staff and organizational preparedness and safety.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".