Usability of Telemedicine in Physical Therapy Rehabilitation: Systematic review (Preprint)
Bibliographic record
Abstract
BACKGROUND: The term 'Telemedicine' was coined in the 1970s to imply 'healing at a distance. Physical therapy rehabilitation (PTR) focuses on the re-institution of function in bodily strength and movement. Covid-19 has created a challenge in one-on-one PTR sessions due to social distancing, which requires the minimization of all non-essential physical contact. Most outpatient services in PTR have had to be staggered and minimized to increase adherence to social distancing requirements and flatten the pandemic's curve. Telemedicine is applicable in PTR in several ways, including guided therapy sessions, and remote monitoring of patient progress through videoconferencing. Telemedicine allows patients to access PTR from the comfort of their homes, which minimizes travel costs and general strain on the body. Although it has been encumbered by various challenges, telemedicine could revolutionize the delivery of PTR while also increasing access to essential healthcare services. OBJECTIVE: Purpose: Covering the main aspects of telemedicine usability in physical therapy rehabilitation, that encourage using of the telemedicine in physical therapy in the world regions not covered by, especially the Middle East. METHODS: Method: A systematic search in libgen.is, jmir.org, wiley.com, sagepub.com, and scholar.google.com. was performed using the search terms: Telemedicine; asynchronous telemedicine; synchronous telemedicine; Covid-19; rehabilitation medicine; physical therapy rehabilitation; videoconferencing; Medicaid programs; telehealth; and HIPAA. Papers published up to Oct. 22nd, 2020, in English, were included. RESULTS: Telemedicine is cost-effective for physical therapy Rehabilitation particularly in pandemic like COVID19, also it's time and human resources saving especially with hands-off skills rehabilitation. CONCLUSIONS: Telemedicine is a revolutionary aspect of medicine that has seen an unexpected uptake following the Covid-19 pandemic. While people formerly preferred live sessions for PTR, the convenience of telemedicine is increasingly emerging. The various advantages, including reduced cost implications, reduced waiting time, and reduction of non-essential travel to obtain therapy have increased the preference for telemedicine. [9] The WHO recognizes the importance of Telemedicine, and conducted a regional and global survey to analyze its viability. At the onset of global lockdown due to Covid-19, the APTA expressed the need for physiotherapists to utilize telemedicine as an alternative for efficient PTR delivery within the bounds of social recommendations for curve-flattening. Telemedicine in PTR has various advantages including cost reduction, the convenience of access, reduction of long waiting lists among others for the patient. [10] PTR can also be availed to areas that are too remote to have a full-fledged PTR center. Through the utilization of nurse aides and other social support systems, therapists can effectively conduct therapy sessions through videoconferencing. Laws governing the adoption and use of telemedicine for PTR include the HIPAA and other DPL, which regulate the extent to which therapists can collect, use, and store data collected during online therapy sessions. Therapists need to obtain verifiable consent from the patient before commencement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".