Assessment of the Efficiency of Virtual Clinics in Hail Hospitals, Saudi Arabia
Bibliographic record
Abstract
Introduction: Virtual clinics have emerged as a viable alternative to traditional face-to-face medical consultations, particularly during the COVID-19 pandemic. This study aims to evaluate the efficiency of virtual clinics by examining the satisfaction of patients and healthcare providers and their opinions of the virtual clinic service. Method: Data were collected using two separate questionnaires—one distributed to patients and the other to healthcare providers, between January 2023 and October 2023. Results: The analysis revealed that patients, regardless of their demographics, preferred virtual clinics due to reduced waiting times (43.9%), easy access to healthcare professionals (36.7%), and the convenience of receiving medical advice from home (19.8%). Healthcare providers also favoured virtual clinics as they reduced workloads and improved accessibility to health services. However, concerns were identified regarding the limitations of telemedicine in conducting physical examinations and technological barriers for physicians and patients. Conclusion: Based on the findings, it is recommended that healthcare providers and patients be encouraged to maximise the use of telemedicine services. Efforts should be made to address barriers, such as technological challenges, and to ensure that appropriate measures are taken to overcome limitations related to physical examinations. By embracing virtual clinics and removing barriers, healthcare delivery can be improved, leading to increased patient satisfaction and reduced provider workload.
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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 | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".