Design Thinking–Informed Simulation
Bibliographic record
Abstract
INTRODUCTION: Designing new healthcare facilities is complex and transitions to new clinical environments carry high risks, as unanticipated problems may arise resulting in inefficient care and patient harm. Design thinking, a human-centered design method, represents a unique framework to support the planning, testing, and evaluation of new clinical spaces throughout all phases of construction. Healthcare simulation has been used to test new clinical spaces, yet most report using simulation only in the late design stages. Moreover, healthcare design models have potentially underused human factors approaches calling for human-centered design. We applied a multimodal simulation-based approach underpinned by the principles of design thinking throughout the planning and construction stages of a newly renovated academic emergency department. METHODS: A multidisciplinary team developed and integrated 3 simulation strategies (table-top, mock-up, and in situ simulation) into the 5-step process of design thinking. Through end-user engagement, we identified potential challenges, prototyped solutions through table-top and mock-up simulations, and iteratively tested these solutions through in situ simulation within the actual clinical space. RESULTS: The team used end-user engagement and feedback to brainstorm and implement effective solutions to problems encountered before opening the new emergency department. The iterative steps and targeted use of simulation resulted in redesigning departmental processes and actual clinical space while mitigating anticipated safety threats and departmental deficiencies. CONCLUSIONS: Design thinking coupled with multimodal simulation across all phases of construction enhanced the design and testing of new clinical infrastructure. Applying this approach early, thoroughly, and efficiently will help healthcare organizations plan changes to clinical spaces.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".