Utilization of Telemedicine in Addressing Musculoskeletal Care Gap in Long-Term Care Patients
Bibliographic record
Abstract
A notable proportion of patient transfers in Ontario are from long-term care facilities for consultation of musculoskeletal (MSK) issues. These transfers are costly for patients and the healthcare system. This study evaluated the utility of a telemedicine MSK (TeleMSK) service for long-term care patients requiring an orthopaedic consultation. Method: A cross-sectional study was used to assess TeleMSK from September 2018 to April 2019. Twenty-six long-term care facilities participated in this study, which included 32 long-term care patients assessed via TeleMSK and 27 telemedicine liaisons. The Telehealth Satisfaction Scale and the Telemedicine Usability Questionnaire (TUQ) surveys were used to evaluate the usefulness of the TeleMSK program. Results: Patients and families rated voice (64.3%) and visual (71.4%) quality of TeleMSK to be excellent as well as the length of consultation (92.9%). A total of 78.6% of participants rated explanations from physicians to be excellent and 92.9% of the participants rated the carefulness, skillfulness, respect, and sensitivity of the attending physician to be excellent 85.7%. Patients felt privacy and confidentiality was maintained and respected throughout the consultation. Most telemedicine liaisons agreed that TeleMSK improved accessibility and productivity of consultations, and 81.5% of the telemedicine liaisons strongly agreed that they would use TeleMSK again in the future. Conclusion: TeleMSK allowed for accessible, timely orthopaedic consultations without compromising the quality of patient care. Patients, families, and telemedicine liaisons rated their experience and the use of TeleMSK as excellent. Barrier to health care is an important issue in the long-term care population. TeleMSK is an excellent medium to close this gap.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".