A User-Centered Approach to Evaluating Wayfinding Systems in Healthcare
Bibliographic record
Abstract
OBJECTIVE: The purpose of this methodology is to provide the designers of wayfinding systems in healthcare facilities a process for evaluating and optimizing a design prior to implementation. The use of this methodology can improve patient experience in hospitals by preventing the installation of confusing, incomplete, and/or frustrating wayfinding systems. BACKGROUND: Despite known wayfinding and information design principles, wayfinding continues to be a challenge in healthcare environments. One reason is that the design of wayfinding systems is rarely evaluated with end users prior to implementation. The methodology outlined in this article is an application of usability testing that involves end users navigating a space using prototyped signage and other elements of a wayfinding system to determine the effectiveness of the system and identify improvement opportunities. This methodology was developed for use in an emergency department that had outdated signage and required a new wayfinding system. CONCLUSION: This methodology provides a structured process for testing and improving the design of a hospital wayfinding system prior to implementation.
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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 | 0.063 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".