Bridging Orthopedic Gaps: A Student-Run Clinic Model for Immigrant and Uninsured Populations in Dallas
Abstract
Background: Uninsured patients frequently face substantial barriers to obtaining orthopedic care, resulting in delayed treatment and worsened musculoskeletal outcomes. These barriers often include financial limitations, lack of insurance coverage, transportation difficulties, and language or cultural differences. To address these challenges, the Islamic Circle of North America (ICNA) Relief Free Clinic in Dallas established a student-run orthopedic care program within its broader free-care model. Patients are evaluated on a walk-in basis, with care provided by medical students under the supervision of an attending physician. The objective of this study was to describe and analyze the barriers encountered by uninsured patients accessing orthopedic care and to assess the effectiveness of this care model in overcoming them.
Method: This was a single-site, cross-sectional descriptive study of patients seen through the orthopedic clinical pathway of the ICNA Relief Free Clinic in Dallas, Texas. Clinical encounter data were reviewed for the program period beginning November 2024, and a standardized, secure web-based survey (Research Electronic Data Capture) administered during clinic sessions from March 1 to April 9, 2025 captured demographics, insurance status, presenting complaints, treatments provided, and self-reported barriers to care. Analyses were descriptive (frequencies and proportions; no inferential testing) and were performed in Microsoft Excel.
Results: A total of 62 patients were included. Financial limitations were reported by 27.4% of patients, while 38.7% experienced language or cultural barriers, and 32.3% reported transportation as a challenge. Patient satisfaction was high, with 96.8% of patients rating their care 7 or higher on a 0-10 scale.
Conclusions: A student-run orthopedic pathway within a free clinic delivered accessible, highly rated musculoskeletal care to a predominantly uninsured, linguistically diverse population, supporting the scalability of this model in similarly underserved communities. Future work will incorporate validated functional outcome measures, structured follow-up with navigator-led referral tracking, and multilingual after-visit summaries, and will evaluate the model across additional sites.
Copyright (c) 2026 Muaz Wahid, Ammaar Kazi, Zuhair Zaidi, Sameer Sajjad, Rafay Wahid, Drew Sanders

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