Telehealth for the Initial Evaluation of Musculoskeletal Disorders: Qualitative Study of Patients, Health Care Providers, and Key Stakeholders in the Province of Quebec in Canada
Bibliographic record
Abstract
Background: Access to care for patients with musculoskeletal disorders (MSKDs) remains a significant challenge. Telehealth has emerged as a promising solution to improve access to care. However, conducting initial evaluations of MSKDs remotely raises concerns about patient safety and clinical efficacy due to the necessary adaptations required for a clinical examination and the challenges of obtaining an accurate and reliable diagnosis. Objective: We aim to explore the use of telehealth for the initial evaluation of MSKDs in the province of Quebec, Canada. Through semistructured interviews with selected patients, health care providers, and other key stakeholders involved in telehealth, this study aims to provide a comprehensive and detailed understanding of its application, benefits, and challenges. Methods: Semistructured interviews were conducted in the province of Quebec with patients, clinicians, telehealth software specialists, and professional bodies' representatives. Five tailored interview guides were developed using the Consolidated Framework of Implementation Research and the Framework of Mathieu-Fritz and Esterle for the study of telehealth interventions. The themes explored included participants' prior experiences with telehealth, perceived strengths and limitations of telehealth, particularly regarding the initial evaluation and diagnosis of new patients, and the current global environment of telehealth use. All interviews were transcribed verbatim, and a reflexive thematic analysis was performed using the Mathieu-Fritz Framework. Results: Thirty-eight participants, including patients (n=11), health care providers (family physicians and musculoskeletal medical specialists: n=11; and physiotherapy professionals: n=10), telehealth software specialists (n=2), and representatives from professional bodies (n=4), shared their perspectives on telehealth for the initial evaluation of MSKDs. Five key themes emerged: (1) several participants viewed telehealth, including remote evaluations, as a solution to improve access to care; (2) patients and health care providers reported that a remote evaluation was more appropriate for simpler MSKD presentations; (3) some health care providers expressed concerns about the potential for an increase in diagnostic errors and the challenges of performing all usual components of a standard MSKD physical examination remotely; (4) patients expressed doubts about their ability to effectively perform certain tasks or tests on themselves; and (5) broader challenges were also highlighted by all participants, such as the impact on the patient-clinician relationship, access to appropriate hardware, digital literacy, and confidentiality concerns. Conclusions: Telehealth is seen as a valuable solution to improve access to care for patients with MSKDs, especially for simpler cases or urgent needs. However, remote physical examination poses challenges associated with concerns about diagnostic accuracy and limited remote physical examination procedures and components. Effective implementation will likely require more evidence-based guidelines, provider training on remote techniques and strategies to maintain patient-provider relationships. Addressing access to technology, digital literacy, and privacy concerns is also essential to ensure equitable adoption and to optimize telehealth in musculoskeletal care.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".