Improving In-person Interpreter Utilization in Complex Care: Findings from a Stepped-Wedge Cluster Randomized Trial...

Area of Science:

• Health Informatics
• Clinical Operations
• Health Equity

Background:

• Underutilization of in-person interpreters contributes to health disparities for medically complex inpatients with limited English proficiency.
• Addressing language barriers is crucial for equitable healthcare delivery.

Purpose of the Study:

• To implement a machine learning and informatics algorithm to enhance the utilization of in-person interpreters for complex inpatients with non-English language preference (NELP).
• To integrate this algorithm into existing clinical and language services workflows.

Main Methods:

• A two-armed, stepped-wedge cluster randomized trial involving 35 inpatient units was conducted.
• The study included adult inpatients (≥18 years) with NELP in acute care settings.
• An algorithm identified complex patients needing interpreters, with language services then initiating targeted outreach.

Main Results:

• The study enrolled 749 control and 672 intervention unique admissions.
• No statistically significant difference was observed in the time to receiving an in-person interpreter between the control and intervention groups (HR=1.02, 95% CI [0.81, 1.29], p=0.87).

Conclusions:

• The intervention demonstrated a non-significant trend towards increased in-person interpreter use among inpatients with NELP and complex needs.
• Findings support the need for a larger, well-powered multicenter trial to further evaluate the algorithm's effectiveness.