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Effect of Virtual Versus In Person Interpreting on Diabetes Outcomes in Non-English Language Preference Patients: A
Marvin So1, Hailie Jadoo2, Jennifer Stong1
1University of Minnesota Medical School, Minneapolis, MN, USA.
Objective:
The objective of this pilot study was to explore the impact of interpreter format (virtual vs in person) on clinical outcomes in patients with non-English language preference (NELP) and type 2 diabetes mellitus (T2DM) in a primary care setting. We hypothesized that NELP patients utilizing in person interpreters would have improved HbA1c values, better follow-up rate, and more complex care plans compared to patients utilizing virtual interpreters.
Methods:
We completed a retrospective chart review of 137 NELP patients with T2DM who required a medical interpreter (February to June 2021). We calculated univariate and bivariate statistics to characterize the sample and assess the extent to which measures of continuity (follow-up visit rate and time to follow-up visit), quality (change in HbA1c), and complexity (medication intervention complexity) were associated with interpreter type.
Results:
There was no statistically significant difference in follow-up rate or average days to follow-up visit for NELP patients with in person as opposed to virtual interpreters. Patients with virtual interpreters demonstrated a non-statistically significant decrease in HbA1c compared to those with in person interpreters. Finally, there was no statistically significant association between interpreter format and intervention complexity.
Conclusions:
Quality medical interpretation contributes to optimal health outcomes in NELP patients with diabetes. Our study suggests that both in person and virtual interpreters can be effective in providing care for NELP patients, especially for chronic disease management in the context of a primary care relationship. It also highlights the importance of pursuing additional qualitative and mixed method studies to better understand the benefits of various interpreter formats across different visit types.
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