RadTranslateâ„¢: An AI-powered intervention for urgent imaging to enhance care equity for patients with limited English proficiency during the COVID-19 pandemic

Chonde, Daniel B.; Pourvaziri, Ali; Williams, Joy; McGowan, Jennifer; Moskos, Margo; Alvarez, Carmen; Narayan, Anand K.; Daye, Dania; Flores, Efren J.; Succi, Marc D.

BACKGROUND Disproportionally high rates of COVID-19 have been noted among communities with limited English proficiency (LEP), resulting in an unmet need for improved multilingual care and interpreter services. To enhance multilingual care, we created a freely available web app (RadTranslate™) that provides multilingual radiology exam instructions. The purpose of this study was to evaluate the implementation of this intervention in radiology. METHODS The device-agnostic web app leverages artificial-intelligence text-to-speech to provide standardized, human-like spoken exam instructions in the patient’s preferred language. Standardized phrases were collected from a consensus group consisting of technologists, radiologists, and ancillary staff. RadTranslate was piloted in Spanish for chest radiographs (CXR) performed at a COVID-19 triage outpatient center that served a predominantly Spanish-speaking Latine community. Implementation included a tablet displaying the app in the CXR room. Imaging appointment duration (IAD) was measured and compared between pre- and post-implementation groups. RESULTS In the 63-day test period following launch, there were 1267 app uses, with technologists voluntarily switching exclusively to RadTranslate for Spanish-speaking patients. The most used phrases were a general explanation of the exam (30% of total) followed by instructions to disrobe and remove any jewelry (12%). There was no significant difference in the IAD, 11±7 min (mean ± standard deviation) and 12±3 for standard-of-care versus RadTranslate, respectively; however, variability was significantly lower when RadTranslate was used (p=0.003). CONCLUSION AI-aided multilingual audio instructions were successfully integrated into imaging workflows, reducing strain on medical interpreters and variance in throughput resulting in more reliable average exam length.