Abstract
BACKGROUND: In-person interpreters are underused, contributing to health inequities among medically complex inpatients with non-English language preference (NELP). OBJECTIVE: To integrate a machine learning and informatics algorithm into clinical and language services workflows to increase the use of in-person interpreters among complex inpatients with NELP. DESIGN: We conducted a two-armed stepped-wedge cluster randomized trial from May 1, 2023, to June 21, 2024, at Mayo Clinic Rochester across 35 inpatient units, including hospital floors, ICUs, and emergency department. Units were randomized to transition between standard care and intervention phases every 60 days. PARTICIPANTS: Adult inpatients ≥ 18 years with NELP in acute care settings. INTERVENTION: We used the algorithm to identify and order inpatients with medical complexity and NELP. Among patient admissions in the intervention arm, we shared the ordered list with language services, who did targeted outreach to bedside nurses via a secure chat message. The message stated that the patient would benefit from an in-person interpreter and provided contact details to reach language services. MAIN MEASURES: Primary outcome was use of in-person interpreter services; secondary outcome assessed time from study entry to first interpreter visit. KEY RESULTS: We enrolled 749 control and 672 intervention unique admissions. Time to receiving interpreter did not differ between groups (HR = 1.02, 95% C.I. (0.81, 1.29), p = 0.87). CONCLUSIONS: We demonstrated an OR and C.I. close to > 1 for receiving an in-person interpreter among inpatients with NELP and complex care needs with the intervention. These findings are supportive of a future well-powered multicenter trial. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT05860777.