Improving patient satisfaction with time spent in an orthopedic outpatient clinic.
Bibliographic record
Abstract
OBJECTIVE: To determine if patient satisfaction can be improved by changing patients' expectations of the clinic visit and by decreasing the total time spent in the clinic. DESIGN: A prospective comparative analysis carried out in 4 phases. SETTING: An university-affiliated orthopedic outpatient clinic. PATIENTS: All patients seen in the orthopedic outpatient clinic were eligible. Phase 1 determined the total clinic time required by patient type; phase 2 assessed baseline satisfaction; phase 3 altered patients' expectations; and phase 4 altered patients' expectations and scheduled visits by patient type. INTERVENTION: Patient questionnaires. MAIN OUTCOME MEASURE: Patient satisfaction with time spent in the clinic. RESULTS: Of 708 distributed questionnaires, 622 (88%) were completed (547 totally complete, 75 partially complete). Total time spent in the clinic decreased across phases 2, 3 and 4 (mean 99.2, 94.7 and 85.2 minutes, respectively, but was significantly different only between phases 3 and 4; p = 0.05, Duncan's multiple range test). The percentage of patients who rated their waiting time as "excellent" increased across phases 2, 3 and 4 (14.6%, 18.8% and 31.1%, respectively; p = 0.0004, chi 2 test). CONCLUSION: Patient satisfaction can be improved by altering patient expectations and by decreasing the total time spent in clinic.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".