Importance of patient-centred signage and navigation guide in an orthopaedic and plastics clinic
Bibliographic record
Abstract
Gulshan & Nanji Orthopaedic and Plastics Center at the North York General Hospital is the second busiest site after the emergency department serving more than 26,000 patients annually. Increase in patient flow, overworked staff, and recent renovations to the hospital have resulted in patients experiencing long wait times, and thusly patient dissatisfaction and stress. Several factors contribute to patient dissatisfaction and stress: i) poor and unfriendly signage; ii) inconsistent utilization of the numbering system; and iii) difficulty navigating to and from the imaging center. A multidisciplinary QI team was assembled to improve the patient experience. We developed a questionnaire to assess patient stress levels at the baseline. Overall, more than half of the patients (54.8%) strongly agreed or agreed to having a stressful waiting experience. Subsequently, based on patient feedback and staff perspectives, we implemented two PDSA cycles. For PDSA 1, we placed a floor graphic (i.e. black tape) to assist patients in navigating from the clinic to the imaging centre and back. For PDSA 2, we involved creating a single 21"×32" patient-friendly sign at the entrance to welcome patients, with clear instructions outlining registration procedures. Surveys were re-administered to assess patient stress levels. A combination of both interventions caused a statistically significant reduction in patient stress levels based on the Kruskal-Wallis and Mann-Whitney U Tests. The present project highlighted the importance of involving stakeholders as well as frontline staff when undertaking quality improvement projects as a way to identify bottlenecks as well as establish sustainable solutions. Additionally, the team recognized the importance of incorporating empirical based solutions and involving experts in the field to optimize results. The present project successfully implemented strategies to improve patient satisfaction and reduce stress in a high flow community clinic. These endpoints were achieved by incorporating patient friendly signage, as well as improving patient flow directors.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".