Lars J. Grimm

In a contemporary breast cancer screening setting with digital breast tomosynthesis and multimodal supplemental screening, approximately half of false-negative cancers were missed, one-quarter were true interval, and one-quarter were mammographically occult cancers.

Objective: To assess the perceived clinical impact of radiologist staffing shortages across the U.S. and identify potential short- and long-term solutions. Methods: A 26-question survey on breast imaging staffing shortages was distributed to active Society of Breast Imaging physician members from May 31, 2024, to June 28, 2024. Questions regarding the impact on patient care and radiologist wellbeing, as well as potential solutions, were included. Bivariable analysis (chi-squared) was performed between the survey demographics and survey response questions of interest. Results: Of 226 respondents who felt understaffed in their practice due to an insufficient number of radiologists, the most frequently reported impact on patients was delayed diagnostic imaging (171/226, 75.7%) and procedures (155/226, 68.6%). The most frequently reported impacts on radiologists were increased stress (198/226, 87.6%), working hours (173/226, 76.6%), and burnout (173/226, 76.6%). As a perceived consequence, 56.2% (127/226) of respondents reported colleagues transitioning to part-time and 35.8% (81/226) reported colleagues switching practice types. To mitigate effects of understaffing, the most frequently reported short-term strategy was offering moonlighting (100/226, 44.2%), whereas offering part-time employment was the most frequently reported long-term strategy for retaining (86/226, 38.1%) and recruiting (120/226, 53.1%) radiologists. Over one-third (77/226, 34.1%) of respondents reported using advanced practice providers, most often for procedural support and patient navigation. Conclusion: Understaffed breast radiologists often perceived delays in patient care and increased levels of physician burnout. Moonlighting and part-time opportunities may mitigate these effects in the short and long-term, respectively.

Objective: To assess variability in parental leave and lactation policies affecting breast radiologists and examine their associations with job satisfaction and burnout. Methods: An anonymous 43-question survey was distributed to U.S.-based physician members of the Society of Breast Imaging between December 2023 and February 2024. Questions assessed parental leave and lactation policies, workplace experiences, and validated items on job satisfaction and burnout. Descriptive statistics, chi-square tests, and multivariable linear regression were performed. Results: 262 respondents (8.0% [262/3275] response rate) completed the survey. The mean age was 47 ± 10.4 years, with an average of 14.6 ± 10 years of post-training experience; 86% (223/262) identified as women. Among respondents, 61% (160/262) were aware of their workplace’s parental leave policy, which included a median of 6 weeks of paid leave (IQR 10). Forty percent (88/220) were unsure if a lactation policy existed, 57% (125/220) reported no dedicated lactation time, and 72% (147/220) were unaware of a designated lactation space. Most respondents (87% [191/219]) said these policies were not discussed during job interviews, though 59% (128/216) considered them important when choosing a job. One-third (72/262) had welcomed a child within the last five years. Among these, 39% (27/69) reported insufficient lactation support, and 26% (18/69) stopped breastfeeding earlier than desired due to work constraints. Lactation support was inversely correlated with burnout (r = -0.516, P < 0.001) and positively correlated with job satisfaction (r = 0.440, P < 0.001). Conclusion: Improved transparency and institutional investment in parental leave and lactation policies by radiology practices may support well-being and retention of breast radiologists.

Until recently, no breast cancer database in the United States records how breast cancers are initially detected, and, therefore, current data lack the fundamental ability to link breast cancer outcomes to screening directly. Consequently, the magnitude and relative contributions that screening and treatment play in the reduction of breast cancer mortality are unknown, and the debate over breast cancer screening continues. To address these shortcomings, advocates have proposed that the Method of Detection (MOD) be documented with every breast cancer diagnosis. MOD data can then be abstracted to cancer registries to provide direct evidence of the impact of screening on breast cancer diagnoses and subsequent outcomes and thereby resolve the debate about the benefits of screening. Given the widespread support of MOD reporting, there is also great potential to improve and facilitate its clinical implementation. Barriers to MOD adoption include a lack of an organized national breast cancer screening program, the cost of implementation, a lack of radiologist MOD training, and technical lapses, among others. The American College of Radiology and the Society of Breast Imaging have addressed barriers to MOD implementation through educational opportunities and the Breast Imaging Reporting and Data System 6th Edition provides comprehensive education on the MOD program. However, consistent and accurate MOD reporting and linkage to tumor registries on a national level will require a multifaceted approach to achieve success. Ultimately, the information gained from the direct role of MOD will result in breast cancer screening recommendations built on science-based policy.

Combining patient services into fewer clinical visits has been increasingly explored across medical specialties as more attention is given to patient-centered care, patient access, and care delivery efficiency from health enterprises. The typical breast imaging care model requires multiple clinical visits to achieve a final diagnosis and, therefore, might be optimized to perform 2 or more steps in the process in 1 patient clinical visit. Recent studies suggest that this model can mitigate patient disparities in timeliness of care, improve patient satisfaction, and even improve patient adherence. Despite the potential benefits, there is variability in the use of same-day services across breast imaging facilities because of various local/institutional level barriers, staffing limitations, and concerns about interpreting examinations in real time. In this review, we describe the various same-day models that have been reported in the breast imaging literature, discuss their impact, and present evidence that may support further adoption of these care models. We also explore the barriers and limitations to this model and future directions of same-day services.

Objective: To understand the perspective of patients undergoing breast imaging on the use of artificial intelligence (AI) in breast cancer screening. Methods: A 36-item survey was administered to breast imaging patients at 6 academic and 2 private practice groups in the United States. The survey included questions regarding demographics, breast imaging history, and electronic health literacy. Respondents were asked Likert scale questions on the role of AI in breast cancer screening, the role of AI as an independent or complementary reader, and concerns regarding AI in breast imaging. Results: The survey yielded 3532 responses, a response rate of 69.9% (3532/5053). The median age was 55.9 years (SD, 12.3 years), and most respondents were White (73.0%, 2679/3532). Respondents indicated support for the role of AI to identify suspicious findings (70.6%, 2492/3532), triage findings for review (69.5%, 2382/3532), calculate breast density (73.2%, 2588/3532), and estimate breast cancer risk (61.9%, 2186/3532). Significantly higher support was noted among patients who were White, had more education, and had greater health literacy (all P <.05). There was strong agreement that it was necessary for radiologists to also review each examination (67.3%, 376/3532). Respondents were uncertain about whether AI (41.2%, 1456/3532) or radiologists (31.8%, 1124/3532) were responsible for errors. There was concern that AI will limit communication between patients and radiologists (75.7%, 2673/3532). Conclusion: Breast imaging patients have an overall favorable view of AI in breast cancer screening, with variable support by demographics. Education and outreach efforts should target perceived challenges to AI adoption to improve patient acceptance.