Investigating the Use of Telemedicine by Health Care Providers to Diagnose and Manage Patients With Musculoskeletal Disorders: Systematic Review and Meta-Analysis

Abstract

BackgroundAccess to care is a major challenge for patients with musculoskeletal disorders (MSKDs). Telemedicine is one of the solutions to improve access to care. However, initial remote diagnosis of MSKDs involves some challenges, such as the impossibility of touching the patient during the physical examination, which makes it more complex to obtain a valid diagnosis. No meta-analysis has been performed to date to synthesize evidence regarding the initial assessment including a physical evaluation using telemedicine to diagnose patients with MSKDs. ObjectiveThis study aims to appraise the evidence on diagnostic and treatment plan concordance between remote assessment using synchronous or asynchronous forms of telemedicine and usual in-person assessment for the initial evaluation of various MSKDs. MethodsAn electronic search was conducted up to August 2023 using terms related to telemedicine and assessment of MSKDs. Methodological quality of studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Random-effect model meta-analyses were performed. The Grading of Recommendations, Assessment, Development, and Evaluations framework was used to synthesize the quality and certainty of the evidence. ResultsA total of 23 concordance studies were eligible and included adult participants (N=1493) with various MSKDs. On the basis of high certainty, pooled κ and prevalence-adjusted and bias-adjusted κ for the diagnostic concordance between remote and in-person assessments of MSKDs were 0.80 (95% CI 0.72-0.89; 7 studies, 353 patients) and 0.83 (95% CI 0.76-0.89; 6 studies, 306 patients). On the basis of moderate certainty, pooled Gwet AC1 for treatment plan concordance between remote and in-person assessments of MSKDs was 0.90 (95% CI 0.80-0.99; 2 studies, 142 patients). ConclusionsThe diagnostic concordance for MSKDs is good to very good. Treatment plan concordance is probably good to excellent. Studies evaluating the accuracy to detect red and yellow flags as well as the potential increase in associated health care resources use, such as imaging tests, are needed.