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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,016 | 0,007 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».