Availability of Emergency Department Wait Times Information: A Patient-Centered Needs Assessment
Bibliographic record
Abstract
INTRODUCTION: Many Emergency Departments (ED) publish wait times; however, the patient perspective in what information is requested and the quantity of information to post is limited. METHODS: We conducted a mixed-methods study at a tertiary care academic center. First, we conducted focus groups of 7 patients. We then generated themes following content analysis to create a patient survey. We administered in-person surveys to patients in ED waiting rooms at sites randomized for survey administration. We used preassigned shifts utilized for even patient perspective representation of the 24 hours-a-day/7 days-a-week service. We included waiting room patients over 18 years of age and excluded patients directly referred to a specialty service or who did not speak French or English. We analyzed survey data using descriptive statistics. RESULTS: We identified nine dominant focus group themes: wait time definition, wait time notification, communication, education, patient expectations, utilization of the ED, patient behaviour, physical comfort, and patient empowerment. Of the 240 patient questionnaires administered, 81.3% of respondents wanted to know ED wait times before hospital arrival hospital and 90.8% wanted ED wait times posted in the waiting room. Website (46.7%) was the most popular choice for publishing wait times outside the ED. Within the ED, patients had no preference regarding display modality, if times were displayed (39.6%). Overall, 76.7% stated that their satisfaction with the ED would be improved if wait times were posted. CONCLUSION: ED patients strongly supported having access to wait time information. Patients believed having wait time information will have a positive impact on their overall ED satisfaction.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".