Using co-design to improve the client waiting experience at an outpatient mental health clinic
Bibliographic record
Abstract
Prolonged wait times in healthcare are a complex issue that can negatively impact both clients and staff. Longer wait times are often caused by a number of factors such as overly complicated scheduling, inefficient use of resources, extraneous processes, and misalignment of supply and demand. Growing evidence suggests a correlation between wait times and client satisfaction. This relationship, however, is complex. Some research suggests that client satisfaction with wait times may be improved with interventions that enhance the waiting experience and not actual wait times. This project aimed to improve the average daily rating of the client waiting experience by 1 point on a 7-point Likert scale.A quality improvement study was conducted to analyse client satisfaction with wait times and enhance clients' satisfaction while waiting. Quality improvement methods, mainly co-design sessions, were used to co-create and implement an intervention to improve clients' experience with waiting in the clinic.The project resulted in the implementation of a whiteboard intervention in the clinic to inform clients where they are in the queue. The whiteboard also included static data summarising the average wait times from the previous month. Both aspects of the whiteboard were designed to allow patients to better approximate their wait times. Though the quantitative analysis did not reveal a 1-point improvement on a 7-point Likert scale, the feedback from staff and clients was positive. Since implementation, clinic staff and management have developed the intervention into a high-fidelity digital board that is still in use today. Furthermore, the use of the intervention has been extended locally, with additional ambulatory clinics at the hospital planning to use the set-up in their clinic waiting rooms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".