Satisfaction With Telemedicine in Patients With Orthopedic Trauma During the COVID-19 Lockdown: Interview Study
Bibliographic record
Abstract
BACKGROUND: Telemedicine can take many forms, from telephone-only consultations to video consultations via a smartphone or personal computer, depending on the goals of the treatment. One of the advantages of videoconferencing is the direct visual contact between patients and therapists even over long distances. Although some telemedicine models require specially designed add-on devices, others get by with off-the-shelf equipment and software and achieve similarly successful successful results. This depends, among other things, on the nature of the injury, the desired outcome of therapy, and the medical consultation. In the last decade, the science and practice of telemedicine have grown exponentially and even more so during the COVID-19 pandemic. Depending on the traumatic lesion, posttraumatic and postoperative treatment and care of patients who experience trauma may require medical or physical therapy consultations in a clinic or office. However, due to the COVID-19 lockdown, direct physical follow-up was more difficult, and therefore, telemedicine solutions were sought and implemented. OBJECTIVE: The aim of this study was to assess satisfaction with telemedical aftercare in patients with orthopedic trauma. METHODS: Between March and July 2020, a standardized interview using a standardized questionnaire-Freiburg Index of Patient Satisfaction (FIPS)-among patients with orthopedic trauma who received telemedical postsurgical or physiotherapeutic care was conducted. The FIPS is composed of 5 questions regarding treatment and 1 question on the overall treatment satisfaction. Furthermore, we assessed patients' demographics and their telemedical use. Subgroup analysis was performed for age groups (<65 years vs ≥65 years), the used device, and gender. RESULTS: In total, we assessed 25 patients with a mean age of 43 (SD 24.31) years (14 female). The majority of patients (n=19, 76%) used their smartphone for consultations. The mean overall FIPS score assessed was 2.14 (SD 0.87). The mean FIPS score for younger patients was 2.23 (SD 0.90) vs 1.91 (SD 0.82) for older patients. The vast majority of the surveyed patients (n=20, 80%) were absolutely confident with their smartphone or tablet use. CONCLUSIONS: Most patients surveyed stated a high satisfaction with the telemedical follow-up. Older patients showed a higher satisfaction rate than their younger counterparts. It seems that telemedical postsurgical or physiotherapeutic care is a viable option, especially in times of reduced contact, like the current COVID-19 pandemic. Thus, telemedicine offers the opportunity to ensure access to effective patient care, even over long distances, while maintaining patient 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".