The Impact of Remote Urology Outpatient Clinics during the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: The coronavirus (COVID-19) pandemic of 2020 had a major impact on NHS services. From the 23rd of March 2020, the Urology Department in Basingstoke initiated telephone-led consultation clinics instead of face-to-face outpatient appointments, in accordance with U.K. guidance. Objectives: To evaluate patient experience and satisfaction following the introduction of remote (telephone) consultations during the COVID-19 pandemic. Patients and methods: The first 200 remote patient appointments between the 30th of March 2020 and the 16th of April 2020 were sent a postal questionnaire (19 questions relating to their experience and level of satisfaction with the interaction). Telephone consultations were conducted by 6 consultants, 3 registrars, and 2 specialist nurses. The patients were not prewarned to expect a questionnaire after the remote appointment. The associated cost saving resulting from a switch from face-to-face appointments to remote telephone appointments was also calculated. Results: 100 out of the 200 patients responded within 1 month (response rate 50%). A total of 44% of the patients were new referrals, while 56% were follow-ups. Overall, the feedback was positive regarding the telephone consultation, with 88% rating the care received as excellent or very good. In addition, 90% would recommend a telephone consultation to family and friends. However, 35% would prefer in the future to have another telephone consultation rather than face-to-face consultation, with 46% preferring a face-to-face appointment in the future and 19% unsure. For new patients, the proportion wishing to have a face-to-face appointment, in the end, was unsurprisingly higher than it was for those undergoing a follow-up (39% vs. 7 %). In these 2 weeks, the cost reduction to the NHS from shifting from face-to-face consultation to telephone consultation was estimated to be £6500. Conclusions: Telephone urology clinics are a satisfactory alternative to face-to-face appointments for many of our patients now and beyond the COVID-19 pandemic. They are efficient, cost-effective, and feasible to undertake urological consultation and can be implemented successfully in selected patients. The feedback from this questionnaire would suggest that priority should be given to face-to-face appointments for new patients and for complex follow-up appointments. Telephone follow-up appointments, however, are a good approach for the majority of patients.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".