Strategies to Improve Racial and Ethnic Diversity in Breast Imaging Training and Beyond
Natasha Monga1, Katie M Davis2, Alejandra Cardona-Del Valle3
1Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY, USA.
Abstract:
Diversity and inclusion in breast imaging can improve creativity and innovation, enrich the workplace environment, and enhance culturally appropriate care for an increasingly diverse patient population. Current estimates predict the racial and ethnic demographics of the United States population will change markedly by the year 2060, with increases in representation of the Black demographic projected to comprise 15% of the population (currently 13.3%) and the Hispanic/Latinx demographic projected to comprise 27.5% of the population (currently 17.8%). However, matriculation rates for those who are underrepresented in medicine (URM), defined as "racial and ethnic populations that are underrepresented in the medical profession relative to their numbers in the general population," have remained largely stagnant. Black students comprise only 7.1% of medical student matriculants, and Hispanic/Latinx students comprise only 6.2% of medical school matriculants compared to the general population. The matriculation rate of URM students into diagnostic radiology is even lower, with Black trainees comprising 3.1% of radiology residents and Hispanic/Latinx trainees comprising 4.8% of radiology residents. This lack of URM radiology resident representation leads to a lack of URM potential applicants to breast imaging fellowships due to the pipeline effect. Strategies to improve diversity and inclusion in breast imaging include recruiting a diverse breast imaging workforce, establishing robust mentorship and sponsorship programs, fostering an inclusive training and workplace environment, and retaining and promoting a diverse workforce.
More Related Videos
05:04Author Spotlight: Evaluating Clinicians' Adoption of Ultrasound-Guided Vascular Cannulation Through Simulation Training
Published on: August 9, 2024
15:48Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
