Using Virtual Reality (VR) Mock-Ups for Evidence-Based Healthcare Facility Design Decisions
Bibliographic record
Abstract
(1) Background: There are many complexities and trade-offs that design teams consider when designing or renovating a built environment for healthcare. Virtual reality (VR) mock-ups can allow design teams to evaluate the planned design. This study aimed to examine the overall value of using VR mock-ups to conduct a simulation-based mock-up evaluation. (2) Methods: Data collected from scenario enactments within a VR mock-up was compared to data collected from an existing medication room with the same design to assess predictive validity. Outcomes regarding quality and patient safety were also examined as a result of design modifications to the VR mock-up which were identified through a post-occupancy evaluation (POE) of the existing medication room. Survey data from participants, hospital design stakeholders, and POE recommendation recipients captured perceptions regarding the evaluation process. Specifically, this included perceptions regarding mock-up and scenario realism as well as utility of the evaluation process. (3) Results: Evidence-based data collected using the VR mock-up accurately assessed workflow (link analysis), bumps, impediments, interruptions, and task completion times. Collecting data pertaining to selection errors and equipment placement were identified after procuring the VR software and therefore the accuracy of these measures was not assessed. Searching behaviours were not possible to capture using the VR software. A 506% return on investment was achieved through the VR mock-up evaluations. (4) Conclusion: Organizations should consider what evaluation objectives are planned and how they will be measured for a mock-up evaluation to determine if VR is appropriate.
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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 | 0.127 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".