Practical Tips for Creating a Diversity, Equity, and Inclusion Committee: Experience From a Multicenter, Academic Radiology Department.

PURPOSE: Coronavirus disease 2019 and the publicly documented deaths of countless Black individuals have highlighted the need to confront systemic racism, address racial/ethnic disparities, and improve diversity and inclusion in radiology. Several radiology departments have begun to create diversity, equity, and inclusion (DEI) committees to systematically address DEI issues in radiology. However, there are few articles that provide departments with guidance on how to create DEI committees to comprehensively address DEI issues in radiology. The purpose of this review is to provide readers with a framework and practical tips for creating a comprehensive, institutionally aligned radiology DEI committee. METHODS: The authors describe key components of the strategic planning process and lessons learned in the creation of a radiology DEI committee, on the basis of the experience of an integrated, academic northeastern radiology department. RESULTS: A hospital-based strategic planning process defining the DEI vision, mission, goals, and strategies was used to inform the formation of the radiology department DEI committee. The radiology department performed gap analyses by conducting internal and external research. Strengths, weaknesses, opportunities, and threats analyses were performed on the basis of consultations with institutional and other departmental DEI leaders as well as DEI leaders from other academic medical centers. This framework served as the basis for the creation of the radiology departmental DEI committee, including a steering committee and four task forces (education, research, patient experience, and workforce development), each charged with addressing specific institutional goals and strategies. CONCLUSIONS: This review provides academic radiology departments with a blueprint to create a comprehensive, institutionally aligned radiology DEI committee.

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.