Examining Factors Impacting Encounter Length and Missed Appointments at a Student-Run Free Clinic: A Retrospective Analysis

Abstract

Background: St. Vincent's Clinic (SVC) is a free, student-run clinic affiliated with the University of Texas Medical Branch that has been an invaluable resource in providing free healthcare services to marginalized populations in Galveston, Texas. The clinic offers a wide variety of specialty services along with free resources such as transportation and medication assistance, telehealth options, and interpreter services. Despite these resources, the clinic has faced challenges with consistently high no-show rates and long encounter lengths, impacting overall efficiency and patient care. We aimed to explore factors that may contribute to these challenges and uncover opportunities to improve patient satisfaction and optimize clinic efficiency. Methods: A retrospective chart review was conducted on all patients seen at SVC across all specialty clinics between March 2021 and March 2023. Patient demographics, appointment status, encounter length, language spoken, department specialty, and appointment modality were recorded. A series of statistical analyses were conducted on collected variables, including chi-square analysis, unpaired t-tests, and single-factor analysis of variance (ANOVA) tests, to assess significant associations. Results: The average encounter length varies significantly across different spoken languages and specialty clinics, but no significance was observed between different appointment modalities. The no-show rates were significantly different depending on the appointment modality, specialty clinic, and patient language spoken. Notably, while the encounter length was significantly shorter for English-speaking patients, Spanish-speaking patients had a lower no-show rate and were more likely to keep scheduled appointments. Conclusions: Language barriers and specialty clinic types can impact the encounter lengths and no-show rates, highlighting the need for targeted interventions such as proper resource allocation. Limitations include potential data discrepancies from factors such as human error or variations in documenting appointments. Future research should explore patient perspectives and experiences to improve patient satisfaction and overall optimize clinic operations.