Racial/Ethnic Disparities in Follow-Up Adherence for Incidental Pulmonary Nodules: An Application of a Cascade-of-Care Framework

Demographics and socioeconomic determinants of health predict continued participation in a CT lung cancer screening program

PURPOSE

We developed machine learning (ML) models to assess demographic and socioeconomic status (SES) variables' value in predicting continued participation in a low-dose CT lung cancer screening (LCS) program.

MATERIALS AND METHODS

480 LCS subjects were retrospectively examined for the following outcomes: (#1) no follow-up (single LCS scan) vs. multiple follow-ups (220 and 260 subjects respectively) and (#2) absent or delayed (>1 month past the due date) follow-up vs timely follow-up (356 and 124 subjects respectively). We quantified the contributions of 14 socioeconomic, demographic, and clinical predictors to LCS adherence, and validated and compared prediction performances of multivariate logistic regression (MLR), support vector machine (SVM) and shallow neural network (NN) models.

RESULTS

For outcome #1, age, sex, race, insurance status, personal cancer history, and median household income were found to be associated with returning for follow-ups. For outcome #2, age, sex, race, and insurance status were significant predictor of absent/delayed LCS follow-up. Across 5-fold cross-validation, the MLR model achieved an average AUC of 0.732 (95% CI, 0.661-0.803) for outcome #1 and 0.633 (95% CI, 0.602-0.664) for outcome #2 and is the model with best predictive performance overall, whereas NN and SVM tended to overfit training data and fell short on testing data performance for either outcome.

CONCLUSIONS

We identified significant predictors of LCS adherence, and our ML models can predict which subjects are at higher risk of receiving no or delayed LCS follow-ups. Our results could inform data-driven interventions to engage vulnerable populations and extend the benefits of LCS.

The Prevalence of Persistent Toe Walking in Children with and without Autism Spectrum Disorder and the Odds of Subsequent Surgery

Persistent toe walking is associated with autism spectrum disorder. The true prevalence of persistent toe walking and odds of progression to surgery in children with and without autism remains unclear. This retrospective descriptive study identified patients ages 3 - 17 years who were enrolled in our healthcare system over a two-year period. Using international classification of disease codes, we identified all children with autism and persistent toe walking, and excluded children with conditions that may independently cause toe walking. Data on Achilles lengthening surgeries, sex, race and body mass index was gathered. The toe walking prevalence amongst children with and without autism was calculated. Multivariable logistic regression analysis controlling for sex, race and body mass index was used to determine independent risk factors for persistent toe walking and surgery. Of the children who met inclusion criteria (N = 284,925), 4,622 (1.6%) had persistent toe walking. Prevalence of persistent toe walking was higher amongst children with autism (6.3% versus 1.5%, p < 0.01), as were odds of persistent toe walking (OR 4.13, 95% CI 3.74 - 4.56, p < 0.01). Males and White patients had higher odds of persistent toe walking compared to females and patients of any other race, respectively (p < 0.01 for all). Although children with autism and toe walking had higher rates of surgery than their counterparts without autism (4.3% versus 2.6%, p = 0.04), this difference was not significant after controlling for sex, race and BMI (OR 1.59, 95% CI 0.95 - 2.69, p > 0.05). LEVEL OF CLINICAL EVIDENCE: III.

The Impact of Closed-Loop Imaging on Actionable CT-Detected Breast Findings

PURPOSE

Closed-loop imaging programs (CLIPs) are designed to ensure that patients receive appropriate follow-up, but a review of incidental CT-detected breast findings in the setting of CLIPs has not been performed.

METHODS

A retrospective review was conducted of CT reports at a single academic institution from July 1, 2020, to January 31, 2022, to identify reports with recommendations for breast imaging follow-up. Medical records were reviewed to evaluate patient adherence to follow-up, CLIP intervention, subsequent BI-RADS assessment, and diagnosis. Adherence was defined as diagnostic breast imaging performed within 6 months of the CT recommendation.

RESULTS

Follow-up recommendations for breast imaging were included in CT report impressions for 311 patients. Almost half of patients (47.3% [147 of 311]) underwent follow-up breast imaging within 6 months, yielding breast cancer diagnoses in 12.9% (19 of 147) and a biopsy-proven positive predictive value of 65.5% (19 of 29). Most patients who returned for follow-up within 6 months did so without CLIP intervention. The majority of CT report impressions in the follow-up group (85.0% [125 of 147]) contained specific recommendations for "diagnostic breast imaging." For patients who did not receive follow-up, the CLIP team tracked all cases and intervened in 19.1% (28 of 147). The most common intervention was a phone call and/or fax to the primary care provider. Outpatient CT examination setting and specific recommendation for diagnostic breast imaging were significantly associated with higher follow-up adherence (P < .0001).

CONCLUSIONS

Actionable CT-detected breast findings require follow-up diagnostic breast imaging because of a relevant cancer detection rate of 12.9%. Although many patients return for breast imaging without intervention, almost half of patients did not receive follow-up and may account for a significant number of missed cancer diagnoses. Specific CT recommendation verbiage is associated with higher follow-up adherence, which can be addressed across settings even without CLIPs.

Understanding and Mitigating Bias in Imaging Artificial Intelligence

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions). Clinical decisions informed by biased models may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities due to differing performance among patient populations. However, while inequitable bias can harm patients in this context, a mindful approach leveraging equitable bias can address underrepresentation of minority groups or rare diseases. Radiologists should also be aware of bias after AI deployment such as automation bias, or a tendency to agree with automated decisions despite contrary evidence. Understanding common sources of imaging AI bias and the consequences of using biased models can guide preventive measures to mitigate its impact. Accordingly, the authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development. The authors review definitions of bias in AI, describe common sources of bias, and present recommendations to guide quality control measures to mitigate the impact of bias in imaging AI. Understanding the terms featured in this article will enable a proactive approach to identifying and mitigating bias in imaging AI. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Rouzrokh and Erickson in this issue.

Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models

OBJECTIVE

To quantify the relative importance of demographic, contextual, socio-economic, and nodule-related factors that influence patient adherence to incidental pulmonary nodule (IPN) follow-up visits and evaluate the predictive performance of machine learning models utilizing these features.

METHODS

We curated a 1,610-subject patient data set from electronic medical records consisting of 13 clinical and socio-economic predictors and IPN follow-up adherence status (timely, delayed, or never) as the outcome. Univariate analysis and multivariate logistic regression were performed to quantify the predictors' contributions to follow-up adherence. Three additional machine learning models (random forests, neural network, and support vector machine) were fitted and cross-validated to examine prediction performance across different model architectures and evaluate intermodel concordance.

RESULTS

On univariate basis, all 13 predictors except comorbidity were found to have a significant association with follow-up. In multiple logistic regression, inpatient or emergency clinical context (odds ratio favoring never following up: 7.28 and 8.56 versus outpatient, respectively) and high nodule risk (odds ratio: 0.25 versus low risk) are the most significant predictors of follow-up, and sex, race, and marital status become additionally significant if clinical context is removed from the model. Clinical context itself is associated with sex, race, insurance, employment, marriage, income, nodule risk, and smoking status, suggesting its role in mediating socio-economic inequities. On cross-validation, all four machine learning models demonstrated comparable and good predictive performances, with mean area under the curve ranging from 0.759 to 0.802, with sensitivity 0.641 to 0.660 and specificity 0.768 to 0.840.

CONCLUSION

Socio-economic factors and clinical context are predictive of IPN follow-up adherence, with clinical context being the most significant contributor and likely representing uncaptured socio-economic determinants.

Evaluation of Socioeconomic Disparities in Follow-Up Completion for Incidental Pulmonary Nodules

PURPOSE

The aim of this study was to evaluate the association between census tract-level measures of social vulnerability and residential segregation and incidental pulmonary nodule (IPN) follow-up.

METHODS

This retrospective cohort study included patients with IPNs ≥6 mm in size or multiple subsolid or ground-glass IPNs <6 mm (with nonoptional follow-up recommendations) diagnosed between January 1, 2018, and December 30, 2019, at a large urban tertiary center and followed for ≥2 years. Geographic sociodemographic context was characterized by the 2018 Centers for Disease Control and Prevention Social Vulnerability Index (SVI) and the index of concentration at the extremes (ICE), categorized in quartiles. Multivariable binomial regression models were used, with a primary outcome of inappropriate IPN follow-up (late or no follow-up). Models were also stratified by nodule risk.

RESULTS

The study consisted of 2,492 patients (mean age, 65.6 ± 12.6 years; 1,361 women). Top-quartile SVI patients were more likely to have inappropriate follow-up (risk ratio [RR], 1.24; 95% confidence interval [CI], 1.12-1.36) compared with the bottom quartile; risk was also elevated in top-quartile SVI subcategories of socioeconomic status (RR, 1.23; 95% CI, 1.13-1.34), Minority status and language (RR, 1.24; 95% CI, 1.03-1.48), housing and transportation (RR, 1.13; 95% CI, 1.02-1.26), and ICE (RR, 1.20; 95% CI, 1.11-1.30). Furthermore, top-quartile ICE was associated with greater risk for inappropriate follow-up among high-risk versus lower risk IPNs (RR, 1.33 [95% CI, 1.18-1.50] versus 1.13 [95% CI, 1.02-1.25]), respectively; P for interaction = .017).

CONCLUSIONS

Local social vulnerability and residential segregation are associated with inappropriate IPN follow-up and may inform policy or interventions tailored for neighborhoods.

Disparities Across the Continuum of Lung Cancer Care

Despite the overall decline in lung cancer incidence and mortality, minority populations continue to bear a higher disease burden. Lung cancer remains the leading cause of cancer-related death in the United States and disproportionately impacts minority populations. Social determinants of health-including low-socioeconomic status, lack of health insurance, and access to health care- disproportionately impact racial, ethnic, and rural populations resulting in direct consequences on lung cancer disparities.

Reducing Racial Disparities in the Timeliness of Potential Lung Cancer Evaluation With a Novel Application-Supported Rapid Outpatient Diagnostic Program: An Interrupted Time Series Analysis

INTRODUCTION

Rapid outpatient diagnostic programs (RODP) expedite lung cancer evaluation, but their impact on racial disparities in the timeliness of evaluation is less clear.

MATERIALS AND METHODS

This was a retrospective analysis of the impact of an internally developed application-supported RODP on racial disparities in timely referral completion rates for patients with potential lung cancer at a safety-net healthcare system. An application screened referrals to pulmonology for indications of lung mass or nodule and presented relevant clinical information that enabled dedicated pulmonologists to efficiently review and triage cases according to urgency. Subsequent care coordination was overseen by a dedicated nurse coordinator. To determine the program's impact, we conducted an interrupted time series analysis of the monthly fraction of referrals completed within 30 days, stratified by those identified as White, non-Hispanic and those that were not (racial and ethnic minorities).

RESULTS

There were 902 patients referred in the 2 years preintervention and 913 in the 2 years postintervention. Overall, the median age was 63 years, and 44.7% of referred patients were female. 44.2% were White, non-Hispanic while racial and ethnic minorities constituted 54.3%. After the intervention, there was a significant improvement in the proportion of referrals completed within 30 days (62.4% vs. 48.2%, P <.01). The interrupted time series revealed a significant immediate improvement in timely completion among racial and ethnic minorities (23%, P < .01) that was not reflected in the majority White, non-Hispanic subgroup (11%, not significant).

CONCLUSION

A thoughtfully designed and implemented RODP reduced racial disparities in the timely evaluation of potential lung cancer.

White Paper: Best Practices in the Communication and Management of Actionable Incidental Findings in Emergency Department Imaging

PURPOSE

Actionable incidental findings (AIFs) are common in radiologic imaging. Imaging is commonly performed in emergency department (ED) visits, and AIFs are frequently encountered, but the ED presents unique challenges for communication and follow-up of these findings. The authors formed a multidisciplinary panel to seek consensus regarding best practices in the reporting, communication, and follow-up of AIFs on ED imaging tests.

METHODS

A 15-member panel was formed, nominated by the ACR and American College of Emergency Physicians, to represent radiologists, emergency physicians, patients, and those involved in health care systems and quality. A modified Delphi process was used to identify areas of best practice and seek consensus. The panel identified four areas: (1) report elements and structure, (2) communication of findings with patients, (3) communication of findings with clinicians, and (4) follow-up and tracking systems. A survey was constructed to seek consensus and was anonymously administered in two rounds, with a priori agreement requiring at least 80% consensus. Discussion occurred after the first round, with readministration of questions where consensus was not initially achieved.

RESULTS

Consensus was reached in the four areas identified. There was particularly strong consensus that AIFs represent a system-level issue, with need for approaches that do not depend on individual clinicians or patients to ensure communication and completion of recommended follow-up.

CONCLUSIONS

This multidisciplinary collaboration represents consensus results on best practices regarding the reporting and communication of AIFs in the ED setting.

Research Priorities for Interventions to Address Health Disparities in Lung Nodule Management: An Official American Thoracic Society Research Statement

Background: Lung nodules are common incidental findings, and timely evaluation is critical to ensure diagnosis of localized-stage and potentially curable lung cancers. Rates of guideline-concordant lung nodule evaluation are low, and the risk of delayed evaluation is higher for minoritized groups. Objectives: To summarize the existing evidence, identify knowledge gaps, and prioritize research questions related to interventions to reduce disparities in lung nodule evaluation. Methods: A multidisciplinary committee was convened to review the evidence and identify key knowledge gaps in four domains: 1) research methodology, 2) patient-level interventions, 3) clinician-level interventions, and 4) health system-level interventions. A modified Delphi approach was used to identify research priorities. Results: Key knowledge gaps included 1) a lack of standardized approaches to identify factors associated with lung nodule management disparities, 2) limited data evaluating the role of social determinants of health on disparities in lung nodule management, 3) a lack of certainty regarding the optimal strategy to improve patient-clinician communication and information transmission and/or retention, and 4) a paucity of information on the impact of patient navigators and culturally trained multidisciplinary teams. Conclusions: This statement outlines a research agenda intended to stimulate high-impact studies of interventions to mitigate disparities in lung nodule evaluation. Research questions were prioritized around the following domains: 1) need for methodologic guidelines for conducting research related to disparities in nodule management, 2) evaluating how social determinants of health influence lung nodule evaluation, 3) studying approaches to improve patient-clinician communication, and 4) evaluating the utility of patient navigators and culturally enriched multidisciplinary teams to reduce disparities.

Applying Implementation Science Principles to Systematize High-Quality Care for Potentially Significant Imaging Findings

OBJECTIVE

Use principles of implementation science to improve the diagnosis and management of potentially significant imaging findings.

METHODS

Multidisciplinary stakeholders codified the diagnosis and management of potentially significant imaging findings in eight organs and created a finding tracking management system that was embedded in radiologist workflows and IT systems. Radiologists were trained to use this system. An automated finding tracking management system was created to support consistent high-quality care through care pathway visualizations, increased awareness of specific findings in the electronic medical record, templated notifications, and creation of an electronic safety net. Primary outcome was the rate of quality reviews related to eight targeted imaging findings. Secondary outcome was radiologist use of the finding tracking management tool.

RESULTS

In the 4 years after implementation, the tool was used to track findings in 7,843 patients who received 10,015 ultrasound, CT, MRI, x-ray, and nuclear medicine examinations that were interpreted by all 34 radiologists. Use of the tool lead to a decrease in related quality reviews (from 8.0% to 0.0%, P < .007). Use of the system increased from 1.7% of examinations in the early implementation phase to 3.1% (+82%, P < .00001) in the postimplementation phase. Each radiologist used the tool on an average of 294.6 unique examinations (SD 404.8). Overall, radiologists currently use the tool approximately 4,000 times per year.

DISCUSSION

Radiologists frequently used a finding tracking management system to ensure effective communication and raise awareness of the importance of recommended future follow-up studies. Use of this system was associated with a decrease in the rate of quality review requests in this domain.

Predictors of Completion of Clinically Necessary Radiologist-Recommended Follow-Up Imaging: Assessment Using an Automated Closed-Loop Communication and Tracking Tool

BACKGROUND. Patients with adverse social determinants of health may be at increased risk of not completing clinically necessary follow-up imaging. OBJECTIVE. The purpose of this study was to use an automated closed-loop communication and tracking tool to identify patient-, referrer-, and imaging-related factors associated with lack of completion of radiologist-recommended follow-up imaging. METHODS. This retrospective study was performed at a single academic health system. A tool for automated communication and tracking of radiologist-recommended follow-up imaging was embedded in the PACS and electronic health record. The tool prompted referrers to record whether they deemed recommendations to be clinically necessary and assessed whether clinically necessary follow-up imaging was pursued. If imaging was not performed within 1 month after the intended completion date, the tool prompted a safety net team to conduct further patient and referrer follow-up. The study included patients for whom a follow-up imaging recommendation deemed clinically necessary by the referrer was entered with the tool from October 21, 2019, through June 30, 2021. The electronic health record was reviewed for documentation of eventual completion of the recommended imaging at the study institution or an outside institution. Multivariable logistic regression analysis was performed to identify factors associated with completion of follow-up imaging. RESULTS. Of 5856 recommendations entered during the study period, the referrer agreed with 4881 recommendations in 4599 patients (2929 women, 1670 men; mean age, 61.3 ± 15.6 years), who formed the study sample. Follow-up was completed for 74.8% (3651/4881) of recommendations. Independent predictors of lower likelihood of completing follow-up imaging included living in a socioeconomically disadvantaged neighborhood according to the area deprivation index (odds ratio [OR], 0.67 [95% CI, 0.54-0.84]), inpatient (OR, 0.25 [95% CI, 0.20-0.32]) or emergency department (OR, 0.09 [95% CI, 0.05-0.15]) care setting, and referrer surgical specialty (OR, 0.70 [95% CI, 0.58-0.84]). Patient age, race and ethnicity, primary language, and insurance status were not independent predictors of completing follow-up (p > .05). CONCLUSION. Socioeconomically disadvantaged patients are at increased risk of not completing recommended follow-up imaging that referrers deem clinically necessary. CLINICAL IMPACT. Initiatives for ensuring completion of follow-up imaging should be aimed at the identified patient groups to reduce disparities in missed and delayed diagnoses.

How We Got Here: The Legacy of Anti-Black Discrimination in Radiology

Current disparities in the access to diagnostic imaging for Black patients and the underrepresentation of Black physicians in radiology, relative to their representation in the general U.S. population, reflect contemporary consequences of historical anti-Black discrimination. These disparities have existed within the field of radiology and professional medical organizations since their inception. Explicit and implicit racism against Black patients and physicians was institutional policy in the early 20th century when radiology was being developed as a clinical medical field. Early radiology organizations also embraced this structural discrimination, creating strong barriers to professional Black radiologist involvement. Nevertheless, there were numerous pioneering Black radiologists who advanced scholarship, patient care, and diversity within medicine and radiology during the early 20th century. This work remains important in the present day, as race-based health care disparities persist and continue to decrease the quality of radiology-delivered patient care. There are also structural barriers within radiology affecting workforce diversity that negatively impact marginalized groups. Multiple opportunities exist today for antiracism work to improve quality of care and to apply standards of social justice and health equity to the field of radiology. An initial step is to expand education on the disparities in access to imaging and health care among Black patients. Institutional interventions include implementing community-based outreach and applying antibias methodology in artificial intelligence algorithms, while systemic interventions include identifying national race-based quality measures and ensuring imaging guidelines properly address the unique cancer risks in the Black patient population. These approaches reflect some of the strategies that may mutually serve to address health care disparities in radiology. ^© RSNA, 2023 See the invited commentary by Scott in this issue. Quiz questions for this article are available in the supplemental material.

Racial Disparities in Adherence to Annual Lung Cancer Screening and Recommended Follow-Up Care: A Multicenter Cohort Study

Rationale: Black patients receive recommended lung cancer screening (LCS) follow-up care less frequently than White patients, but it is unknown if this racial disparity persists across both decentralized and centralized LCS programs. Objectives: To determine adherence to American College of Radiology Lung Imaging Reporting and Data System (Lung-RADS) recommendations among individuals undergoing LCS at either decentralized or centralized programs and to evaluate the association of race with LCS adherence. Methods: We performed a multicenter retrospective cohort study of patients receiving LCS at five heterogeneous U.S. healthcare systems. We calculated adherence to annual LCS among patients with a negative baseline screen (Lung-RADS 1 or 2) and recommended follow-up care among those with a positive baseline screen (Lung-RADS 3, 4A, 4B, or 4X) stratified by type of LCS program and evaluated the association between race and adherence using multivariable modified Poisson regression. Results: Of the 6,134 total individuals receiving LCS, 5,142 (83.8%) had negative baseline screens, and 992 (16.2%) had positive baseline screens. Adherence to both annual LCS (34.8% vs. 76.1%; P < 0.001) and recommended follow-up care (63.9% vs. 74.6%; P < 0.001) was lower at decentralized compared with centralized programs. Among individuals with negative baseline screens, a racial disparity in adherence was observed only at decentralized screening programs (interaction term, P < 0.001). At decentralized programs, Black race was associated with 27% reduced adherence to annual LCS (adjusted relative risk [aRR], 0.73; 95% confidence interval [CI], 0.63-0.84), whereas at centralized programs, no effect by race was observed (aRR, 0.98; 95% CI, 0.91-1.05). In contrast, among those with positive baseline screens, there was no significant difference by race for adherence to recommended follow-up care by type of LCS program (decentralized aRR, 0.95; 95% CI, 0.81-1.11; centralized aRR, 0.81; 95% CI, 0.71-0.93; interaction term, P = 0.176). Conclusions: In this large multicenter study of individuals screened for lung cancer, adherence to both annual LCS and recommended follow-up care was greater at centralized screening programs. Black patients were less likely to receive annual LCS than White patients at decentralized compared with centralized LCS programs. Our results highlight the need for further study of healthcare system-level mechanisms to optimize longitudinal LCS care.

Patient-level factors influencing adherence to follow-up imaging recommendations

PURPOSE

To determine which, if any, patient-level factors were associated with differences in completion of follow-up imaging recommendations at a tertiary academic medical center.

METHODS

In this IRB-approved, retrospective cohort study, approximately one month of imaging recommendations were reviewed from 2017 at a single academic institution that contained key words recommending follow-up imaging. Age, gender, race/ethnicity, insurance, smoking history, primary language, BMI, and home address were recorded via chart extraction. Home addresses were geocoded to Census Block Groups and assigned to a quintile of neighborhood socioeconomic status. A multivariate logistic regression model was used to evaluate each predictor variable with significance set to p = 0.05.

RESULTS

A total of 13,421 imaging reports that included additional follow-up recommendations were identified. Of the 1013 included reports that recommended follow-up, 350 recommended additional imaging and were analyzed. Three hundred eight (88.00%) had corresponding follow-up imaging present and the insurance payor was known for 266 (86.36%) patients: 146 (47.40%) had commercial insurance, 35 (11.36%) had Medicaid, and 85 (27.60%) had Medicare. Patients with Medicaid had over four times lower odds of completing follow-up imaging compared to patients with commercial insurance (OR 0.24, 95% CI 0.06-0.88, p = 0.032). Age, gender, race/ethnicity, smoking history, primary language, BMI, and neighborhood socioeconomic status were not independently associated with differences in follow-up imaging completion.

CONCLUSION

Patients with Medicaid had decreased odds of completing follow-up imaging recommendations compared to patients with commercial insurance.

Novel Quality Measure Set: Closing the Completion Loop on Radiology Follow-up Recommendations for Noncritical Actionable Incidental Findings

BACKGROUND

Care gaps occur when radiology follow-up recommendations are poorly communicated or not completed, resulting in missed or delayed diagnosis potentially leading to worse patient outcomes. This ACR-led initiative assembled a technical expert panel (TEP) to advise development of quality measures intended to improve communication and drive increased completion rates for radiology follow-up recommendations.

MATERIALS AND METHODS

A multistakeholder TEP was assembled to advise the development of quality measures. The project scope, limited to noncritical actionable incidental findings (AIFs), encourages practices to develop and implement systems ensuring appropriate communication and follow-up to completion.

RESULTS

A suite of nine measures were developed: four outcome measures include closing the loop on completion of radiology follow-up recommendations for nonemergent AIFs (with pulmonary nodule and abdominal aortic aneurysm use cases) and overall cancer diagnoses. Five process measures address communication and tracking of AIFs: inclusion of available evidence or guidelines informing the recommendation, communication of AIFs to the practice managing ongoing care, identifying when AIFs have been communicated to the patient, and employing tracking and reminder systems for AIFs.

CONCLUSION

This ACR-led initiative developed a measure set intended to improve patient outcomes by ensuring that AIFs are appropriately communicated and followed up. The intent of these measures is to focus improvement on specific areas in which gaps in communication and AIF follow-up may occur, prompting systems to devote resources that will identify and implement solutions to improve patient care.

Health Care Disparities in Radiology-A Review of the Current Literature

INTRODUCTION

Health care disparities exist in all medical specialties, including radiology. Raising awareness of established health care disparities is a critical component of radiology's efforts to mitigate disparities. Our primary objective is to perform a comprehensive review of the last 10 years of literature pertaining to disparities in radiology care. Our secondary objective is to raise awareness of disparities in radiology.

METHODS

We reviewed English-language medicine and health services literature from the past 10 years (2010-2020) for research that described disparities in any aspect of radiologic imaging using radiology search terms and key words for disparities in OVID. Relevant studies were identified with adherence to the guidelines set forth by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.

RESULTS

The search yielded a total 1,890 articles. We reviewed the citations and abstracts with the initial search yielding 1,890 articles (without duplicates). Of these, 1,776 were excluded based on the criteria set forth in the methods. The remaining unique 114 articles were included for qualitative synthesis.

DISCUSSION

We hope this article increases awareness and inspires action to address disparities and encourages research that further investigates previously identified disparities and explores not-yet-identified disparities.

Reducing Health Disparities in Radiology Through Social Determinants of Health: Lessons From the COVID-19 Pandemic

During the COVID-19 pandemic, the disproportionate morbidity and mortality borne by racial minorities, patients of lower socioeconomic status, and patients lacking health insurance reflect the critical role of social determinants of health, which are manifestations of entrenched structural inequities. In radiology, social determinants of health lead to disparate use of imaging services through multiple intersecting contributors, on both the provider and patient side, affecting diagnosis and treatment. Disparities on the provider side include ordering of initial or follow-up imaging studies and providing standard-of-care interventional procedures, while patient factors include differences in awareness of screening exams and confidence in the healthcare system. Disparate utilization of mammography and lung cancer screening lead to delayed diagnosis, while differential provision of minimally invasive interventional procedures contributes to differential outcomes related to treatment. Interventions designed to mitigate social determinants of health could help to equalize the healthcare system. Here we review disparities in access and health outcomes in radiology. We investigate underlying contributing factors in order to identify potential policy changes that could promote more equitable health in radiology.

Racial disparities in provider-patient communication of incidental medical findings

Health disparities research often focuses on the social patterning of health outcomes. Increasingly, there has been an emphasis on understanding the mechanisms perpetuating disparities, even after issues of patient access to health services are addressed. The following study utilizes a novel dataset of electronic medical records (EMR), radiology records, and U.S. Census data to investigate the racial/ethnic patterning of provider-patient communication among patients diagnosed with incidental medical findings requiring follow-up. My results indicate that racial/ethnic disparities in follow-up adherence stem from initial disparities in provider-patient communication. These communication disparities persist even after accounting for multiple socioeconomic, health, and provider characteristics, indicating a bias in medicine, whereby providers are less likely to communicate information about incidental medical findings to patients of color relative to White patients. This paper has important clinical implications, as it sheds new light on why we might see low adherence to medical advice among patients of color. Findings also have social, political, and policy relevance, as they suggest an important mechanism through which health inequalities persist. To finally eliminate racial/ethnic health inequalities in the United States, racial bias and discrimination within medical and public health infrastructures must be eliminated.

Narrowing the Gap: Imaging Disparities in Radiology

It may seem unlikely that the field of radiology perpetuates disparities in health care, as most radiologists never interact directly with patients, and racial bias is not an obvious factor when interpreting images. However, a closer look reveals that imaging plays an important role in the propagation of disparities. For example, many advanced and resource-intensive imaging modalities, such as MRI and PET/CT, are generally less available in the hospitals frequented by people of color, and when they are available, access is impeded due to longer travel and wait times. Furthermore, their images may be of lower quality, and their interpretations may be more error prone. The aggregate effect of these imaging acquisition and interpretation disparities in conjunction with social factors is insufficiently recognized as part of the wide variation in disease outcomes seen between races in America. Understanding the nature of disparities in radiology is important to effectively deploy the resources and expertise necessary to mitigate disparities through diversity and inclusion efforts, research, and advocacy. In this article, the authors discuss disparities in access to imaging, examine their causes, and propose solutions aimed at addressing these disparities.