Perception and satisfaction of patients after telemedicine urology consultations: A matched analysis with physicians’ perspective
Bibliographic record
Abstract
INTRODUCTION: During the first regional COVID-19 lockdown in March 2020, we conducted a study aimed at evaluating completeness of telemedicine consultation in urology. Of 1679 consultations, 67% were considered completely managed by phone. The aim of the present study was to assess patients' experience and satisfaction with telemedicine and to compare them with urologists' perceptions about quality and completeness of the telemedicine consultation. METHODS: We contacted a randomly selected sample of patients (n=356) from our previous study to enquire about their experience. We used a home patient experience questionnaire, inspired by the Patient Experiences Questionnaire for Out-of-Hours Care (PEQOHC) and the Consumer Assessment Health Profile Survey (CAHPS). RESULTS: Of 356 patients contacted, 315 agreed to complete the questionnaire. Urological consultations were for non-oncological (104), oncological (121), cancer suspicion (41), and pediatric (49) indications. Mean patient satisfaction score after telemedicine consultation was 8.8/10 (median 9/10) and 86.3% of patients rated the quality of the consultation as either excellent (54.6%) or very good (31.7%). Consultations regarding cancer suspicion had the lowest score (8.3/10). Overall, 46.7% of all patients would have preferred an in-person visit outside of the pandemic situation. Among patients whose consultations were rated suboptimal by urologists, almost a third more (31.2%) would have preferred an in-person visit (p=0.03). CONCLUSIONS: Despite high reported patient satisfaction rates with telemedicine, it is noteworthy that nearly half of the patients would have preferred an in-person visit. Post-pandemic, it will be important to incorporate telemedicine as an alternative, while retaining and offering in-person visits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".