RadTranslate: An Artificial Intelligence-Powered Intervention for Urgent Imaging to Enhance Care Equity for Patients With Limited English Proficiency During the COVID-19 Pandemic.

PURPOSE

Disproportionally high rates of coronavirus disease 2019 (COVID-19) have been noted among communities with limited English proficiency, resulting in an unmet need for improved multilingual care and interpreter services. To enhance multilingual care, the authors created a freely available web application, RadTranslate, that provides multilingual radiology examination instructions. The purpose of this study was to evaluate the implementation of this intervention in radiology.

METHODS

The device-agnostic web application leverages artificial intelligence text-to-speech technology to provide standardized, human-like spoken examination instructions in the patient's preferred language. Standardized phrases were collected from a consensus group consisting of technologists, radiologists, and ancillary staff members. RadTranslate was piloted in Spanish for chest radiography performed at a COVID-19 triage outpatient center that served a predominantly Spanish-speaking Latino community. Implementation included a tablet displaying the application in the chest radiography room. Imaging appointment duration was measured and compared between pre- and postimplementation groups.

RESULTS

In the 63-day test period after launch, there were 1,267 application uses, with technologists voluntarily switching exclusively to RadTranslate for Spanish-speaking patients. The most used phrases were a general explanation of the examination (30% of total), followed by instructions to disrobe and remove any jewelry (12%). There was no significant difference in imaging appointment duration (11 ± 7 and 12 ± 3 min for standard of care versus RadTranslate, respectively), but variability was significantly lower when RadTranslate was used (P = .003).

CONCLUSIONS

Artificial intelligence-aided multilingual audio instructions were successfully integrated into imaging workflows, reducing strain on medical interpreters and variance in throughput and resulting in more reliable average examination length.