Identification of limited English proficient patients in clinical care

Leah S Karliner, Anna M Napoles-Springer, Dean Schillinger, Kirsten Bibbins-Domingo, Eliseo J Pérez-Stable, Leah S Karliner, Anna M Napoles-Springer, Dean Schillinger, Kirsten Bibbins-Domingo, Eliseo J Pérez-Stable

Abstract

Background: Standardized means to identify patients likely to benefit from language assistance are needed.

Objective: To evaluate the accuracy of the U.S. Census English proficiency question (Census-LEP) in predicting patients' ability to communicate effectively in English.

Design: We investigated the sensitivity and specificity of the Census-LEP alone or in combination with a question on preferred language for medical care for predicting patient-reported ability to discuss symptoms and understand physician recommendations in English.

Participants: Three hundred and two patients > 18 who spoke Spanish and/or English recruited from a cardiology clinic and an inpatient general medical-surgical ward in 2004-2005.

Results: One hundred ninety-eight (66%) participants reported speaking English less than "very well" and 166 (55%) less than "well"; 157 (52%) preferred receiving their medical care in Spanish. Overall, 135 (45%) were able to discuss symptoms and 143 (48%) to understand physician recommendations in English. The Census-LEP with a high-threshold (less than "very well") had the highest sensitivity for predicting effective communication (100% Discuss; 98.7% Understand), but the lowest specificity (72.6% Discuss; 67.1% Understand). The composite measure of Census-LEP and preferred language for medical care provided a significant increase in specificity (91.9% Discuss; 83.9% Understand), with only a marginal decrease in sensitivity (99.4% Discuss; 96.7% Understand).

Conclusions: Using the Census-LEP item with a high-threshold of less than "very well" as a screening question, followed by a language preference for medical care question, is recommended for inclusive and accurate identification of patients likely to benefit from language assistance.

Figures

Figure 1
Figure 1
Algorithm for determining likely benefit from language assistance.

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