A Practical Framework for Promoting Justice-Oriented Innovation in Radiology.

This article argues that innovation should be guided by the values of the field it hopes to advance. As radiology has come to recognize its role in creating or perpetuating imaging disparities, it has become evident that imaging disparities may arise from innovations that fail to consider impacts on justice from the outset. Acknowledging that a radiologist's deepest commitments as a physician involve doing no harm, doing good, and ensuring justice, we propose redefining stewardship in radiologic innovation to include a positive obligation to prioritize justice in innovation. Engaging in justice-oriented innovation requires an imaging disparity to be identified, understood, and targeted through a concerted developmental effort. To this end, we consider the structural changes necessary to achieve this objective and provide a template outlining a future for radiologic innovation that aims to mitigate imaging disparities. We provide a simple rubric by which innovative efforts can be deemed "justice-oriented innovations" in environments featuring a surplus of ideas and limited resources to facilitate their prioritization. Innovations that are likely to mitigate imaging disparities should have access to accelerated pipelines for development and commercialization.

A data science-based analysis of socioeconomic determinants impacting pediatric diagnostic radiology utilization during the COVID-19 pandemic.

BACKGROUND: Research on healthcare disparities in pediatric radiology is limited, leading to the persistence of missed care opportunities (MCO). We hypothesize that the COVID-19 pandemic exacerbated existing health disparities in access to pediatric radiology services. OBJECTIVE: Evaluate the social determinants of health and sociodemographic factors related to pediatric radiology MCO before, during, and after the COVID-19 pandemic. MATERIALS AND METHODS: The study examined all outpatient pediatric radiology exams at a pediatric medical center and its affiliate centers from 03/08/19 to 06/07/21 to identify missed care opportunities. Logistic regression with the least absolute shrinkage and selection operator (LASSO) method and classification and regression tree (CART) analysis were used to explore factors and visualize relationships between social determinants and missed care opportunities. RESULTS: A total of 62,009 orders were analyzed: 30,567 pre-pandemic, 3,205 pandemic, and 28,237 initial recovery phase. Median age was 11.34 years (IQR 5.24-15.02), with 50.8% females (31,513/62,009). MCO increased during the pandemic (1,075/3,205; 33.5%) compared to pre-pandemic (5,235/30,567; 17.1%) and initial recovery phase (4,664/28,237; 16.5%). The CART analysis identified changing predictors of missed care opportunities across different periods. Pre-pandemic, these were driven by exam-specific factors and patient age. During the pandemic, social determinants like income, distance, and ethnicity became key. In the initial recovery phase, the focus returned to exam-specific factors and age, but ethnicity continued to influence missed care, particularly in neurological exams for Hispanic patients. Logistic regression revealed similar results: during the pandemic, increased distance from the examination site (OR 1.1), residing outside the state (OR 1.57), Hispanic (OR 1.45), lower household income ($25,000-50,000 (OR 3.660) and $50,000-75,000 (OR 1.866)), orders for infants (OR 1.43), and fluoroscopy (OR 2.3) had higher odds. In the initial recovery phase, factors such as living outside the state (OR 1.19), orders for children (OR 0.79), and being Hispanic (OR 1.15) correlate with higher odds of MCO. CONCLUSION: The application of basic data science techniques is a valuable tool in uncovering complex relationships between sociodemographic factors and disparities in pediatric radiology, offering crucial insights into addressing inequalities in care.

AI-based opportunistic quantitative image analysis of lung cancer screening CTs to reduce disparities in osteoporosis screening.

Osteoporosis is underdiagnosed, especially in ethnic and racial minorities who are thought to be protected against bone loss, but often have worse outcomes after an osteoporotic fracture. We aimed to determine the prevalence of osteoporosis by opportunistic CT in patients who underwent lung cancer screening (LCS) using non-contrast CT in the Northeastern United States. Demographics including race and ethnicity were retrieved. We assessed trabecular bone and body composition using a fully-automated artificial intelligence algorithm. ROIs were placed at T12 vertebral body for attenuation measurements in Hounsfield Units (HU). Two validated thresholds were used to diagnose osteoporosis: high-sensitivity threshold (115-165 HU) and high specificity threshold (<115 HU). We performed descriptive statistics and ANOVA to compare differences across sex, race, ethnicity, and income class according to neighborhoods' mean household incomes. Forward stepwise regression modeling was used to determine body composition predictors of trabecular attenuation. We included 3708 patients (mean age 64 ± 7 years, 54 % males) who underwent LCS, had available demographic information and an evaluable CT for trabecular attenuation analysis. Using the high sensitivity threshold, osteoporosis was more prevalent in females (74 % vs. 65 % in males, p < 0.0001) and Whites (72 % vs 49 % non-Whites, p < 0.0001). However, osteoporosis was present across all races (38 % Black, 55 % Asian, 56 % Hispanic) and affected all income classes (69 %, 69 %, and 91 % in low, medium, and high-income class, respectively). High visceral/subcutaneous fat-ratio, aortic calcification, and hepatic steatosis were associated with low trabecular attenuation (p < 0.01), whereas muscle mass was positively associated with trabecular attenuation (p < 0.01). In conclusion, osteoporosis is prevalent across all races, income classes and both sexes in patients undergoing LCS. Opportunistic CT using a fully-automated algorithm and uniform imaging protocol is able to detect osteoporosis and body composition without additional testing or radiation. Early identification of patients traditionally thought to be at low risk for bone loss will allow for initiating appropriate treatment to prevent future fragility fractures. CLINICALTRIALS.GOV IDENTIFIER: N/A.

The Association Between Food Security and Mammography Screening: Cross-Sectional Survey Results From the National Health Interview Survey.

OBJECTIVE: There are limited data about food insecurity within the cancer screening setting. To inform the potential need for food insecurity interventions, our study evaluated the association between food security and mammographic screening among eligible participants. METHODS: Female respondents aged 40 to 74 years in the 2019 National Health Interview Survey without history of breast cancer were included. Food insecurity was assessed using the Six-Item Food Security Scale developed by the National Center for Health Statistics. The proportion of patients who reported mammographic screening within the last year was estimated, stratified by food security. Multiple variable logistic regression analyses evaluated the association between food security and mammography screening, adjusted for potential confounders. All analyses were performed accounting for complex survey design features. RESULTS: In all, 8,956 weighted survey respondents met inclusion criteria; 90.1% were classified as having high or marginal food security, of whom 56.6% reported screening; 6.1% were classified with low food security, of whom 42.1% reported screening; and 3.8% were classified with very low food security, of whom 43.1% reported screening. In our unadjusted analyses, participants with low food security (P < .001) and very low food security (P < .001) were less likely to report screening within the last year. In our adjusted analyses, participants with food insecurity (P = .009) were less likely to report screening. DISCUSSION: In a nationally representative cross-sectional survey, participants with food insecurity were less likely to report mammography screening. Radiology practices should consider screening patients for food insecurity and social determinants of health. Evidence-based food insecurity interventions may increase adherence to mammography screening.

RadioGraphics Update: The 10 Pillars of Lung Cancer Screening-Rationale and Logistics of a Lung Cancer Screening Program.

Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

Factors Associated With Timeliness and Equity of Access to Outpatient MRI Examinations.

OBJECTIVE: The role of MRI in guiding patients' diagnosis and treatment is increasing. Therefore, timely MRI performance prevents delays that can impact patient care. We assessed the timeliness of performing outpatient MRIs using the socio-ecological model approach and evaluated multilevel factors associated with delays. METHODS: This institutional review board-approved study included outpatient MRI examinations ordered between October 1, 2021, and December 31, 2022, for performance at a large quaternary care health system. Mean order-to-performed (OtoP) interval (in days) and prolonged OtoP interval (defined as >10 days) for MRI orders with an expected date of 1 day to examination performance were measured. Logistic regression was used to assess patient-level (demographic and social determinants of health), radiology practice-level, and community-level factors associated with prolonged OtoP interval. RESULTS: There were 126,079 MRI examination orders with expected performance within 1 day placed during the study period (56% of all MRI orders placed). After excluding duplicates, there were 97,160 orders for unique patients. Of the MRI orders, 48% had a prolonged OtoP interval, and mean OtoP interval was 18.5 days. Factors significantly associated with delay in MRI performance included public insurance (odds ratio [OR] = 1.11, P < .001), female gender (OR = 1.11, P < .001), radiology subspecialty (ie, cardiac, OR = 1.71, P < .001), and patients from areas that are most deprived (ie, highest Area Deprivation Index quintile, OR = 1.70, P < .001). DISCUSSION: Nearly half of outpatient MRI orders were delayed, performed >10 days from the expected date selected by the ordering provider. Addressing multilevel factors associated with such delays may help enhance timeliness and equity of access to MRI examinations, potentially reducing diagnostic errors and treatment delays.

Empathy and Equity: Key Considerations for Large Language Model Adoption in Health Care.

The growing presence of large language models (LLMs) in health care applications holds significant promise for innovative advancements in patient care. However, concerns about ethical implications and potential biases have been raised by various stakeholders. Here, we evaluate the ethics of LLMs in medicine along 2 key axes: empathy and equity. We outline the importance of these factors in novel models of care and develop frameworks for addressing these alongside LLM deployment.

Handheld Lung Ultrasound to Detect COVID-19 Pneumonia in Inpatients: A Prospective Cohort Study.

Background: Chest imaging, including chest X-ray (CXR) and computed tomography (CT), can be a helpful adjunct to nucleic acid test (NAT) in the diagnosis and management of Coronavirus Disease 2019 (COVID-19). Lung point of care ultrasound (POCUS), particularly with handheld devices, is an imaging alternative that is rapid, highly portable, and more accessible in low-resource settings. A standardized POCUS scanning protocol has been proposed to assess the severity of COVID-19 pneumonia, but it has not been sufficiently validated to assess diagnostic accuracy for COVID-19 pneumonia. Purpose: To assess the diagnostic performance of a standardized lung POCUS protocol using a handheld POCUS device to detect patients with either a positive NAT or a COVID-19-typical pattern on CT scan. Methods: Adult inpatients with confirmed or suspected COVID-19 and a recent CT were recruited from April to July 2020. Twelve lung zones were scanned with a handheld POCUS machine. Images were reviewed independently by blinded experts and scored according to the proposed protocol. Patients were divided into low, intermediate, and high suspicion based on their POCUS score. Results: Of 79 subjects, 26.6% had a positive NAT and 31.6% had a typical CT pattern. The receiver operator curve for POCUS had an area under the curve (AUC) of 0.787 for positive NAT and 0.820 for a typical CT. Using a two-point cutoff system, POCUS had a sensitivity of 0.90 and 1.00 compared to NAT and typical CT pattern, respectively, at the lower cutoff; it had a specificity of 0.90 and 0.89 compared to NAT and typical CT pattern at the higher cutoff, respectively. Conclusions: The proposed lung POCUS protocol with a handheld device showed reasonable diagnostic performance to detect inpatients with a positive NAT or typical CT pattern for COVID-19. Particularly in low-resource settings, POCUS with handheld devices may serve as a helpful adjunct for persons under investigation for COVID-19 pneumonia.

Advancing Health Equity in Lung Cancer Screening and the Role of Humanomics.

Identifying and managing lung cancer, the leading cause of cancer-specific mortality, depend on multiple medical and sociodemographic factors. Humanomics is a model that acknowledges that negative societal stressors from systemic inequity affect individual health by altering pro-inflammatory gene expression. The same factors which may predispose individuals to lung cancer may also obstruct equitably prompt diagnosis and treatment. Increasing lung cancer screening access can lessen disparities in outcomes among disproportionately affected communities. Here, the authors describe several individual, provider, and health system-level obstacles to lung cancer screening and offer actionable solutions to increase access.

Social Determinants of Health in Imaging-based Cancer Screening: A Case-based Primer with Strategies for Care Improvement.

Health disparities, preventable differences in the burden of disease and disease outcomes often experienced by socially disadvantaged populations, can be found in nearly all areas of radiology, including emergency radiology, neuroradiology, nuclear medicine, image-guided interventions, and imaging-based cancer screening. Disparities in imaging-based cancer screening are especially noteworthy given the far-reaching population health impact. The social determinants of health (SDoH) play an important role in disparities in cancer screening and outcomes. Through improved understanding of how SDoH can drive differences in health outcomes in radiology, radiologists can effectively provide patient-centered, high-quality, and equitable care. Radiologists and radiology practices can become active partners in efforts to assist patients along their imaging journey and overcome existing barriers to equitable cancer screening care for traditionally marginalized populations. As radiology exists at the intersection of diagnostic imaging, image-guided diagnostic intervention, and image-guided treatment, radiologists are uniquely positioned to design these strategies. Cost-effective and socially conscious strategies that address barriers to equitable care can improve both public health and equitable health outcomes. Potential strategies include championing supportive health policy, reducing out-of-pocket costs, increasing price transparency, improving education and outreach efforts, ensuring that appropriate language translation services are available, providing individualized assistance with appointment scheduling, and offering transportation assistance and childcare. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Patient, Provider, and Practice Characteristics Predicting Use of Diagnostic Imaging in Primary Care: Cross-Sectional Data From the National Ambulatory Medical Care Survey.

OBJECTIVE: To determine imaging utilization rates in outpatient primary care visits and factors influencing likelihood of imaging use. METHODS: We used 2013 to 2018 National Ambulatory Medical Care Survey cross-sectional data. All visits to primary care clinics during the study period were included in the sample. Descriptive statistics on visit characteristics including imaging utilization were calculated. Logistic regression analyses evaluated the influence of a variety of patient-, provider-, and practice-level variables on the odds of obtaining diagnostic imaging, further subdivided by modality (radiographs, CT, MRI, and ultrasound). The data's survey weighting was accounted for to produce valid national-level estimates of imaging use for US office-based primary care visits. RESULTS: Using survey weights, approximately 2.8 billion patient visits were included. Diagnostic imaging was ordered at 12.5% of visits with radiographs the most common (4.3%) and MRI the least common (0.8%). Imaging utilization was similar or greater among minority patients compared with White, non-Hispanic patients. Physician assistants used imaging at higher rates than physicians, in particular CT at 6.5% of visits compared with 0.7% for doctors of medicine and doctors of osteopathic medicine (odds ratio 5.67, 95% confidence interval 4.07-7.88). CONCLUSION: Disparities in rates of imaging utilization for minorities seen in other health care settings were not present in this sample of primary care visits, supporting that access to primary care is a path to promote health equity. Higher rates of imaging utilization among advanced-level practitioners highlight an opportunity to evaluate imaging appropriateness and promote equitable, high-value imaging among all practitioners.

Justice and Innovation in Radiology.

Imaging is a central determinant of health outcomes, and radiologic disparities can cascade throughout a patient's illness course. Innovative efforts in radiology are constant, but innovation that is driven by short-term profit-making incentives without explicit regard for principles of justice can lead to exclusion of the vulnerable from potential benefits and widening of inequities. Accordingly, we must consider the ways in which the field of radiology can shape innovative efforts to ensure that innovation ameliorates injustice instead of exacerbating it. The authors propose a distinction between approaches to innovation that prioritize justice and those that do not. The authors argue that the field's institutional incentives should be adjusted to prioritize forms of innovation that are likely to ameliorate imaging inequities, and they provide examples of initial steps that can be taken to make these adjustments. The authors propose the term justice-oriented innovation as a way of describing forms of innovation that are motivated by reducing injustice and can reasonably be expected to do so.

Analysis of Geographic Accessibility of Breast, Lung, and Colorectal Cancer Screening Centers Among American Indian and Alaskan Native Tribes.

PURPOSE: To evaluate geographic accessibility of ACR mammographic screening (MS), lung cancer screening (LCS), and CT colorectal cancer screening (CTCS) centers among US federally recognized American Indian and Alaskan Native (AI/AN) tribes. METHODS: Distances from AI/AN tribes' ZIP codes to their closest ACR-accredited LCS and CTCS centers were recorded using tools from the ACR website. The FDA's database was used for MS. Persistent adult poverty (PPC-A), persistent child poverty (PPC-C), and rurality indexes (rural-urban continuum codes) were from the US Department of Agriculture. Logistic and linear regression analyses were used to assess distances to screening centers and relationships among rurality, PPC-A, and PPC-C. RESULTS: Five hundred ninety-four federally recognized AI/AN tribes met the inclusion criteria. Among all closest MS, LCS, or CTCS center to AI/AN tribes, 77.8% (1,387 of 1,782) were located within 200 miles, with a mean distance of 53.6 ± 53.0 miles. Most tribes (93.6% [557 of 594]) had MS centers within 200 miles, 76.4% (454 of 594) had LCS centers within 200 miles, and 63.5% (376 of 594) had CTCS centers within 200 miles. Counties with PPC-A (odds ratio [OR], 0.47; P < .001) and PPC-C (OR, 0.19; P < .001) were significantly associated with decreased odds of having a cancer screening center within 200 miles. PPC-C was associated with decreased likelihood of having an LCS center (OR, 0.24; P < .001) and an CTCS center (OR, 0.52; P < .001) within the same state as the tribe's location. No significant association was found between PPC-A and PPC-C and MS centers. CONCLUSIONS: AI/AN tribes experience distance barriers to ACR-accredited screening centers, resulting in cancer screening deserts. Programs are needed to increase equity in screening access among AI/AN tribes.

Impact of High Neighborhood Socioeconomic Deprivation on Access to Accredited Breast Imaging Screening and Diagnostic Facilities.

PURPOSE: The aim of this study was to evaluate the presence or absence of accredited breast imaging facilities in ZIP codes with high or low neighborhood socioeconomic deprivation. METHODS: A retrospective ecological study design was used. Neighborhood socioeconomic disadvantage rankings at the ZIP code level were defined by the University of Wisconsin Neighborhood Atlas Area Deprivation Index. Outcomes included the presence or absence of FDA- or ACR-accredited mammographic facilities, accredited stereotactic biopsy or breast ultrasound facilities, and ACR Breast Imaging Centers of Excellence. US Department of Agriculture rural-urban commuting area codes were used to define urban and rural status. Access to breast imaging facilities in high-disadvantage (≥97th percentile) and low-disadvantage (≤3rd percentile) ZIP codes was compared using χ2 tests, stratified by urban or rural status. RESULTS: Among 41,683 ZIP codes, 2,796 were classified as high disadvantage (1,160 rural, 1,636 urban) and 1,028 as low disadvantage (39 rural, 989 urban). High-disadvantage ZIP codes were more likely rural (P < .001) and less likely to have FDA-certified mammographic facilities (28% versus 35%, P < .001), ACR-accredited stereotactic biopsy (7% versus 15%, P < .001), breast ultrasound (9% versus 23%, P < .001), or Breast Imaging Centers of Excellence (7% versus 16%, P < .001). Among urban areas, high-disadvantage ZIP codes were less likely to have FDA-certified mammographic facilities (30% versus 36%, P = .002), ACR-accredited stereotactic biopsy (10% versus 16%, P < .001), breast ultrasound (13% versus 23%, P < .001), and Breast Imaging Centers of Excellence (10% versus 16%, P < .001). CONCLUSIONS: People living in ZIP codes with high socioeconomic disadvantage are less likely to have accredited breast imaging facilities within their ZIP codes, which may contribute to disparities in access to breast cancer care experienced by underserved groups living in these areas.

Understanding Race-Based Medicine and Its Impact on Radiology.

Although race is widely accepted as a social construct, the practice of medicine is embedded with the assumption of race as a genetic marker that explains differences in disease prevalence, presentation, and health outcomes and encourages the "correction" for race in interpreting certain test results and values. This false premise, which is fundamental to the theory of race-based medicine, is integrated into clinical practice and has resulted in inequitable care among communities of color. The impact of race-based medicine in radiology might be less apparent but is nonetheless considerable because it affects the entire continuum of radiology practice. In this review, we discuss historical perspectives, examine various incriminated scenarios relevant to radiology, and offer mitigation strategies.