Health Care Disparities in Radiology: A Primer for Resident Education

Abstract

As the population of the United States grows increasingly diverse, health care disparities become vital to understand and mitigate. The ethical and financial implications of how groups of Americans gain access to health care have evolved into some of today's most challenging socioeconomic problems. Educators in radiology are just beginning to tackle the concepts of health care disparities, unconscious bias, and cultural competency. In July 2017, the Accreditation Council for Graduate Medical Education required that all trainees and teaching faculty of accredited training programs receive training and experience in new areas of quality improvement to include an understanding of health care disparities as part of the core competencies. To our knowledge, there is no centralized curriculum regarding health care disparities for radiology residents and fellows. Many programs, in fact, have yet to introduce the concept to their trainees, who may have difficulty recognizing that this is even a problem affecting radiology. This manuscript serves as a primer for radiology trainees on health care disparities, with the goal of defining major concepts and providing examples of how variable access to radiological care can have substantial impact on patient outcomes.

Sustainable low-field cardiovascular magnetic resonance in changing healthcare systems

“…Recognizing the role CMR has to play, the Society of Cardiovascular Magnetic Resonance has released guidance, education, and information for the use of CMR in women with CVDs in a drive to address this gender disparity. 71,[74][75][76] How can LF CMR be implemented?…”

Not all biases are bad: equitable and inequitable biases in machine learning and radiology

“…population imaging) is influenced by who has access to radiology services in the first place. In many countries, there are significant differences in access to healthcare among social groups [12]. The people who do have the best access to radiology services are most likely the ones most benefitting from the application of the ML technology, because they were represented in the algorithm's training data.…”

“…A study on mammography screening, for example, has shown that radiologists were less likely to detect malignant breast lesions in patients with a minority ethnic background or low income [19]. The higher rate of false negatives in these patients suggests that health inequities are not only due to these groups' diminished access to services but are also influenced by radiologists' stereotypes and prejudices [12].…”

An Introduction to Health Disparities for the Practicing Radiologist

“…More importantly, patients being cared for in clinical departments where knowledge of disparities or cultural competency training is lacking may experience greater disparities themselves. For those involved in training technologists, medical students, or residents, there are resources available to give them a basic introduction to health care disparities and the delivery of culturally competent care [27][28][29][30][31][32]. Fostering education in these areas is a prerequisite to a professional community well equipped to discuss and deal with health care disparities.…”