Socioeconomic and Demographic Predictors of Missed Opportunities to Provide Advanced Imaging Services

Prevalence of financial hardship and health-related social needs among patients with missed radiology appointments

PURPOSE

We aimed to evaluate the prevalence of financial hardship and Health-Related Social Needs (HRSN) among patients who missed their radiology appointment.

METHODS

English-speaking adult patients, with a missed outpatient imaging appointment at any of a tertiary care imaging centers between 11/2022 and 05/2023 were eligible. We measured self-reported general financial worry using Comprehensive Score for Financial Toxicity (COST), imaging hardship (worry that the current imaging is a financial hardship to patient and their family), material hardship (e.g., medical debt), cost-related care nonadherence, and HRSNs including housing instability, food insecurity, transportation problems, and utility help needs.

RESULTS

282 patients were included (mean age 54.7 ± 15.0 years; 70.7 % female). Majority were non-Hispanic White (52.4 %), followed by Asian (23.0 %) and Hispanic (16.0 %) racial/ethnic background. Most missed appointments were patient-initiated (74.8 %); 13.5 % due to cost or insurance coverage and 6.4 % due to transportation and parking. Mean COST score was 26.8 with 44.4 % and 28.8 % reporting their illness and imaging as a source of financial hardship. 18.3 % and 35.2 % endorsed cost-related care nonadherence and material hardship. 32.7 % had at least one HRSNs with food insecurity the most common (25.4 %). Only 12.5 % were previously screened for financial hardship or HRSNs. Having comorbidity and living in more disadvantaged neighborhoods was associated with higher report of financial hardship and HRSNs.

CONCLUSION

Financial hardship and HRSNs are common among those who miss radiology appointments. There needs to be more rigorous screening for financial hardship and HRSNs at every health encounter and interventions should be implemented to address these.

Sociodemographic factors and Child Opportunity Index disparities associated with missed care opportunities in pediatric patients with lymphoma and leukemia referred for FDG-PET/CT

BACKGROUND

Little data exists on the association of missed care opportunities (MCOs) in children referred for nuclear medicine/nuclear oncology imaging examinations and socioeconomic disparities.

OBJECTIVE

To determine the prevalence of MCOs in children with lymphoma/leukemia scheduled for fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) and the impact of sociodemographic factors and Child Opportunity Index (COI).

MATERIALS AND METHODS

Retrospective analysis of MCOs in children with lymphoma/leukemia scheduled for FDG-PET/CT (2012 to 2022) was performed. In univariate analysis, patient, neighborhood, and appointment data were assessed across MCOs and completed appointments. Logistic regression evaluated independent effects of patient-, neighborhood-, and appointment-level factors with MCOs. Two-sided P-value < .05 was considered statistically significant.

RESULTS

In 643 FDG-PET/CT appointments (n = 293 patients; median age 15 years (IQR 11.0-17.0 years); 37.9% female), there were 20 MCOs (3.1%) involving 16 patients. Only 8.2% appointments involved Black/African American non-Hispanic/Latino patients, yet they made up a quarter of total MCOs. Patients living in neighborhoods with very low or low COI experienced significantly higher MCOs versus zip codes with very high COI (6.9% vs. 0.8%; P = 0.02). Logistic regression revealed significantly increased likelihood of MCOs for patients aged 18 to 21 [odds ratio (OR) 4.50; 95% CI 1.53-13.27; P = 0.007], Black/African American non-Hispanic/Latino (OR 3.20; 95% CI 1.08-9.49; P = 0.04), zip codes with very low or low COI (OR 9.60; 95% CI 1.24-74.30; P = 0.03), and unknown insurance status.

CONCLUSION

Children with lymphoma/leukemia, living in zip codes with very low or low COI, and who identified as Black/African American non-Hispanic/Latino experienced more MCOs. Our study supports the need to address intersecting sociodemographic, neighborhood, and health system factors that will improve equitable access to necessary healthcare imaging for children.

Missed Screening Mammography Appointments: Patient Sociodemographic Characteristics and Mammography Completion After 1 Year

OBJECTIVE

Patients who miss screening mammogram appointments without notifying the health care system (no-show) risk care delays. We investigate sociodemographic characteristics of patients who experience screening mammogram no-shows at a community health center and whether and when the missed examinations are completed.

METHODS

We included patients with screening mammogram appointments at a community health center between January 1, 2021, and December 31, 2021. Language, race, ethnicity, insurance type, residential ZIP code tabulation area (ZCTA) poverty, appointment outcome (no-show, same-day cancelation, completed), and dates of completed screening mammograms after no-show appointments with ≥1-year follow-up were collected. Multivariable analyses were used to assess associations between patient characteristics and appointment outcomes.

RESULTS

Of 6,159 patients, 12.1% (743 of 6,159) experienced no-shows. The no-show group differed from the completed group by language, race and ethnicity, insurance type, and poverty level (all P < .05). Patients with no-shows more often had: primary language other than English (32.0% [238 of 743] versus 26.7% [1,265 of 4,741]), race and ethnicity other than White non-Hispanic (42.3% [314 of 743] versus 33.6% [1,595 of 4,742]), Medicaid or means-tested insurance (62.0% [461 of 743] versus 34.4% [1,629 of 4,742]), and residential ZCTAs with ≥20% poverty (19.5% [145 of 743] versus 14.1% [670 of 4,742]). Independent predictors of no-shows were Black non-Hispanic race and ethnicity (adjusted odds ratio [aOR], 1.52; 95% confidence interval [CI], 1.12-2.07; P = .007), Medicaid or other means-tested insurance (aOR, 2.75; 95% CI, 2.29-3.30; P < .001), and ZCTAs with ≥20% poverty (aOR, 1.76; 95% CI, 1.14-2.72; P = .011). At 1-year follow-up, 40.6% (302 of 743) of patients with no-shows had not completed screening mammogram.

DISCUSSION

Screening mammogram no-shows is a health equity issue in which socio-economically disadvantaged and racially and ethnically minoritized patients are more likely to experience missed appointments and continued delays in screening mammogram completion.

Healthcare utilization is increased in children living in urban areas, with ethnicity-related disparities: A big data analysis study

This study aimed to investigate differences in pediatric healthcare utilization in Israel over 10 years by examining differences across populations defined by living environment and ethnicity. Data was obtained from the Clalit Health Care data warehouse, covering over 250,000 children residing in Haifa and Western Galilee districts. The population groups were categorized based on ethnicity (Jewish vs Arab) and residential settings (urban vs rural). Healthcare utilization was consistently higher among Jewish than Arab children, irrespective of the specific dimension analyzed. Additionally, urban-dwelling children exhibited higher usage rates than those residing in rural areas in all investigated dimensions. However, Jewish children showed significantly about 18% lower hospitalization rates than Arab children across all years (P < 0.001). No significant differences in hospitalizations were observed between urban and rural children (RR 0.999, CI (0.987-1.011)). Notably, the study revealed reduced antibiotic consumption and hospitalizations over the years for all populations. Additionally, we found that Arab children and those living in rural areas had reduced access to healthcare, as evidenced by 10-40% fewer physician visits, laboratory tests, and imaging (P < 0.001).    Conclusion: This study highlights the substantial population-based disparities in healthcare utilization among children in Israel despite the equalizing effect of the national health insurance law. Rural and low socioeconomic populations seem to have reduced healthcare access, showing decreased healthcare utilization. Consequently, it is imperative to address these disparities and implement targeted interventions to enhance healthcare access for Arab children and rural communities. The decline in antibiotic usage and hospitalizations suggests positive trends in pediatric health care, necessitating ongoing efforts to ensure equitable access and quality of care for all populations. What is Known: • Healthcare systems worldwide vary in coverage and accessibility, including Israel, which stands out for its diverse population. • Existing research primarily focuses on healthcare utilization among adults, leaving a need for comprehensive data on children's healthcare patterns globally. What is New: • Investigating over 250,000 children, this study reveals higher healthcare utilization among Jewish and urban children across all dimensions. • Despite Israel's national health insurance law, the study underscores the significant population-based disparities in healthcare utilization.

Decision analysis framework for predicting no-shows to appointments using machine learning algorithms

BACKGROUND

No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources.

METHODS

In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of other classification algorithms, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and resampling techniques, such as Random under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE) and NearMiss-1. We validated the framework using two attendance datasets from Brazilian hospitals with no-show rates of 6.65% and 19.03%.

RESULTS

From the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to other techniques, particularly IHT, which excelled when combined with all classification algorithms and led to low variability in performance metrics results. Our results also outperformed sensitivity outcomes reported in the literature, with values above 0.94 for both datasets.

CONCLUSION

This is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying on few validation runs for imbalanced datasets as it may lead to biased results and inadequate analysis of the generalization and stability of the models obtained during the training stage.

Assessing the Relationship between Radiology Department Research Funding and Institutional Community Inclusion and Investment

Background Health care access disparities and lack of inclusion in clinical research have been well documented for marginalized populations. However, few studies exist examining the research funding of institutions that serve historically underserved groups. Purpose To assess the relationship between research funding awarded to radiology departments by the National Institutes of Health (NIH) and Lown Institute Hospitals Index rankings for inclusivity and community benefit. Materials and Methods This retrospective study included radiology departments awarded funding from the NIH between 2017 and 2021. The 2021 Lown Institute Hospitals Index rankings for inclusivity and community benefit were examined. The inclusivity metric measures how similar a hospital's patient population is to the surrounding community in terms of income, race and ethnicity, and education level. The community benefit metric measures charity care spending, Medicaid as a proportion of patient revenue, and other community benefit spending. Linear regression and Pearson correlation coefficients (r values) were used to evaluate the relationship between aggregate NIH radiology department research funding and measures of inclusivity and community benefit. Results Seventy-five radiology departments that received NIH funding ranging from $195 000 to $216 879 079 were included. A negative correlation was observed between the amount of radiology department research funding received and institutional rankings for serving patients from racial and/or ethnic minorities (r = -0.34; P < .001), patients with low income (r = -0.44; P < .001), and patients with lower levels of education (r = -0.46; P < .001). No correlation was observed between the amount of radiology department research funding and institutional rankings for charity care spending (r = -0.19; P = .06), community investment (r = -0.04; P = .68), and Medicaid as a proportion of patient revenue (r = -0.10; P = .22). Conclusion Radiology departments that received more NIH research funding were less likely to serve patients from racial and/or ethnic minorities and patients who had low income or lower levels of education. © RSNA, 2024 See also the editorial by Mehta and Rosen in this issue.

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.

Factors Associated with No-Show Rates in a Pediatric Audiology Clinic

OBJECTIVE

To evaluate factors associated with no-show rates in a pediatric audiology clinic.

STUDY DESIGN

Retrospective review.

SETTING

Tertiary referral center.

PARTICIPANTS

All pediatric patients younger than 18 years whose parents/guardians scheduled an appointment at a tertiary Audiology Clinic between June 1, 2015, and July 1, 2017.

MAIN OUTCOME MEASURES

Data included whether the patient came to their appointment, patient age, sex, race, insurance type, appointment type, location, season of appointment, and day of the week of the appointment.

RESULTS

Of the 7,784 pediatric appointments scheduled with audiology, the overall no-show rate was 24.3% (n = 1893). Lower age was significantly associated with no-shows ( p = 0.0003). Black/African American children were more likely to no-show compared with White/Caucasians ( p = 0.0001). Compared with self-pay/military/other insurance, those with Medicaid were more likely to no-show ( p = 0.0001). The highest rate of no-shows occurred during summer (27%). On multivariate analysis, younger age, Black/African American race, and Medicaid insurance were associated with increased no-show rates.

CONCLUSION

A variety of factors influence no-show rates in a pediatric audiology setting. No-shows can affect treatment quality and affect overall hearing outcomes. Further investigation is necessary to assess barriers to appointment adherence and to develop interventions to improve adherence and care.

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.

Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond

There are many impactful applications of artificial intelligence (AI) in the electronic radiology roundtrip and the patient's journey through the healthcare system that go beyond diagnostic applications. These tools have the potential to improve quality and safety, optimize workflow, increase efficiency, and increase patient satisfaction. In this article, we review the role of AI for process improvement and workflow enhancement which includes applications beginning from the time of order entry, scan acquisition, applications supporting the image interpretation task, and applications supporting tasks after image interpretation such as result communication. These non-diagnostic workflow and process optimization tasks are an important part of the arsenal of potential AI tools that can streamline day to day clinical practice and patient care.

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.

Practical Approaches to Advancing Health Equity in Radiology, From the AJR Special Series on DEI

Despite significant advances in healthcare, many patients from medically underserved populations are impacted by existing healthcare disparities. Radiologists are uniquely positioned to decrease health disparities and advance health equity efforts in their practices. However, literature on practical tools for advancing radiology health equity efforts applicable to a wide variety of patient populations and care settings is lacking. Therefore, this article seeks to equip radiologists with an evidence-based and practical knowledge toolkit of health equity strategies, presented in terms of four pillars of research, clinical care, education, and innovation. For each pillar, equity efforts across diverse patient populations and radiology practice settings are examined through the lens of existing barriers, current best practices, and future directions, incorporating practical examples relevant to a spectrum of patient populations. Health equity efforts provide an opportune window to transform radiology through personalized care delivery that is responsive to diverse patient needs. Guided by compassion and empathy as core principles of health equity, leveraging the four pillars provides a helpful framework to advance health equity efforts as a step towards social justice in health.

Barriers and facilitators to uptake of lung cancer screening: A mixed methods systematic review

Numerous factors contribute to the low adherence to lung cancer screening (LCS) programs. A theory-informed approach to identifying the obstacles and facilitators to LCS uptake is required. This study aimed to identify, assess, and synthesize the available literature at the individual and healthcare provider (HCP) levels based on a social-ecological model and identify gaps to improve practice and policy decision-making. Systematic searches were conducted in nine electronic databases from inception to December 31, 2020. We also searched Google Scholar and manually examined the reference lists of systematic reviews to include relevant articles. Primary studies were scored for quality assessment. Among 3938 potentially relevant articles, 36 studies, including 25 quantitative and 11 qualitative studies, were identified for inclusion in the review. Fifteen common factors were extracted from 34 studies, including nine barriers and six facilitators. The barriers included individual factors (n = 5), health system factors (n = 3), and social/environmental factors (n = 1). The facilitators included only individual factors (n = 6). However, two factors, age and screening harm, remain mixed. This systematic review identified and combined barriers and facilitators to LCS uptake at the individual and HCP levels. The interaction mechanisms among these factors should be further explored, which will allow the construction of tailored LCS recommendations or interventions for the Chinese context.

Racial and ethnic disparities in pediatric magnetic resonance imaging missed care opportunities

BACKGROUND

Imaging missed care opportunities (MCOs), previously referred to as "no shows," impact timely patient diagnosis and treatment and can exacerbate health care disparities. Understanding factors associated with imaging MCOs could help advance pediatric health equity.

OBJECTIVE

To assess racial/ethnic differences in pediatric MR imaging MCOs and whether health system and socioeconomic factors, represented by a geography-based Social Vulnerability Index (SVI), influence racial/ethnic differences.

MATERIALS AND METHODS

We conducted a retrospective analysis of MR imaging MCOs in patients younger than 21 years at a pediatric academic medical center (2015-2019). MR imaging MCOs were defined as: scheduled but appointment not attended, canceled within 24 h, and canceled but not rescheduled. Mixed effects multivariable logistic regression assessed the association between MCOs and race/ethnicity and community-level social factors, represented by the SVI.

RESULTS

Of 68,809 scheduled MRIs, 6,159 (9.0%) were MCOs. A higher proportion of MCOs were among Black/African-American and Hispanic/Latino children. Multivariable analysis demonstrated increased odds of MCOs among Black/African-American (adjusted odds ratio [aOR] 1.9, 95% confidence interval [CI] 1.7-2.3) and Hispanic/Latino (aOR 1.5, 95% CI 1.3-1.7) children compared to White children. The addition of SVI >90th percentile to the adjusted model had no effect on adjusted OR for Black/African-American (aOR 1.9, 95% CI 1.7-2.2) or Hispanic/Latino (aOR 1.5, 95% CI 1.3-1.6) children. Living in a community with SVI >90th percentile was independently associated with MCOs.

CONCLUSION

Black/African-American and Hispanic/Latino children were almost twice as likely to experience MCOs, even when controlling for factors associated with MCOs. Independent of race/ethnicity, higher SVI was significantly associated with MCOs. Our study supports that pediatric health care providers must continue to identify systemic barriers to health care access for Black/African-American and Hispanic/Latino children and those from socially vulnerable areas.

Racial and Ethnic Disparities in the Imaging Workup and Treatment of Knee and Hip Osteoarthritis

BACKGROUND

There is limited evidence on sociodemographic differences in osteoarthritis management, particularly in non-African American (AA) minorities. We sought to identify differences in imaging modalities, administration of intra-articular injections, and total joint arthroplasty (TJA) between racial/ethnic groups.

METHODS

We retrospectively reviewed patients presenting to outpatient clinics with a diagnosis of hip or knee osteoarthritis from January 2013 to March 2020 at a tertiary center. Univariate analyses compared differences between groups. Multivariate logistic regression analyses determined sociodemographic predictors of imaging workup and treatment.

RESULTS

In total, 105,873 patients were included. There were 74,769 (70.6%) Caucasian, 27,117 (25.6%) AA, 1,878 (1.8%) Hispanic, 1,479 (1.4%) Asian, and 630 (0.6%) Native American patients. Multivariate analyses demonstrated that AAs had decreased odds of undergoing a knee magnetic resonance imaging (odds ratio [OR] 0.77, P < .001) or injection (OR 0.94, P = .006). Asian patients had lower odds of receiving any hip X-ray (OR 0.72, P = .047) or knee injection (OR 0.83, P = .017). AA (total knee arthroplasty [TKA]: OR 0.51, P < .001; total hip arthroplasty [THA]: OR 0.57, P < .001), Hispanic (TKA: OR 0.69, P = .003; THA: OR 0.60, P = .006), and Asian (TKA: OR 0.73, P = .010; THA: OR 0.56, P = .010) patients had lower odds of undergoing TJA compared to Caucasians. We found that higher income quartiles had greater odds of receiving a magnetic resonance imaging and TJA, males had lower odds of receiving injections and greater odds of undergoing TJA, and Medicaid and self-pay patients had lower odds of undergoing TJA (P < .05).

CONCLUSION

After adjusting for sociodemographic factors, we found disparities in the imaging, administration of injections, and/or arthroplasty for AA, Asian, and Hispanic patients. Insurance status, income, and gender were also associated with imaging and treatments performed in managing hip and knee osteoarthritis.

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.

Sociodemographic Missed-care Predictors for Pediatric Orthopaedic Telemedicine During COVID-19

BACKGROUND

Increased telehealth services may not benefit communities already lacking access to care. Race, socioeconomic status, and insurance type are known to predict missed-care opportunities (MCO) in health care. We examined differences in pediatric orthopaedic telemedicine MCOs during the COVID-19 pandemic, compared with MCOs of in-person visits in a prepandemic time frame. We hypothesized that groups with known health disparities would experience higher rates of pediatric orthopedic telemedicine MCOs.

METHODS

We retrospectively analyzed pediatric orthopaedic telemedicine MCOs during the COVID-19 pandemic lockdown (March-May 2020) and in-person pediatric orthopaedic visit MCOs during a nonpandemic timeframe (March-May 2019). We examined predictors of MCOs including race, ethnicity, language, insurance status, and other socioeconomic determinants of health.

RESULTS

There were 1448 telemedicine appointments in the pandemic cohort and 8053 in-person appointments in the prepandemic cohort. Rates of telemedicine MCOs (12.5%; n=181) were significantly lower than in-person MCOs (19.5%; n=1566; P<0.001). Telemedicine appointments with public insurance or without insurance (P<0.001) and being Black or Hispanic/Latinx (P=0.003) were associated with MCOs. There were significant differences between in-person MCOs and telemedicine MCOs among all predictors studied, except for orthopaedic subspecialty team and patient's social vulnerability index.

CONCLUSIONS

Patients with telemedicine appointments during the COVID-19 pandemic were less likely to experience MCOs than patients with in-person visits during the nonpandemic timeframe. However, when controlling for socioeconomic factors including race, ethnicity, and insurance type, disparities found for in-person visits persisted with the shift to telemedicine. Pediatric orthopaedists should be aware that the use of telemedicine does not necessarily improve access for our most vulnerable patients.

LEVEL OF EVIDENCE

Level III.

Modelling the no-show of patients to exam appointments of computed tomography

BACKGROUND

Patients' no-shows negatively impact healthcare systems, leading to resources' underutilisation, efficiency loss, and cost increase. Predicting no-shows is key to developing strategies that counteract their effects. In this paper, we propose a model to predict the no-show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital.

METHODS

We carried out a retrospective study on 8382 appointments to computed tomography (CT) exams between January and December 2017. Penalised logistic regression and multivariate logistic regression were used to model the influence of 15 candidate variables on patients' no-shows. The predictive capabilities of the models were evaluated by analysing the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC).

RESULTS

The no-show rate in computerised tomography exams appointments was 6.65%. The two models performed similarly in terms of AUC. The penalised logistic regression model was selected using the parsimony criterion, with 8 of the 15 variables analysed appearing as significant. One of the variables included in the model (number of exams scheduled in the previous year) had not been previously reported in the related literature.

CONCLUSIONS

Our findings may be used to guide the development of strategies to reduce the no-show of patients to exam appointments.

Neighborhood Effects on Missed Appointments in a Large Urban Academic Multispecialty Practice

BACKGROUND

Missed appointments diminish the continuity and quality of care.

OBJECTIVE

To determine whether missing scheduled appointments is associated with characteristics of the populations in places where patients reside.

DESIGN

Retrospective cross-sectional study using data extracted from electronic health records linked to population descriptors for each patient's census tract of residence.

PATIENTS

A total of 58,981 patients ≥18 years of age with 275,682 scheduled appointments during 2014-2015 at a multispecialty outpatient practice.

MAIN MEASURES

We used multinomial generalized linear mixed models to examine associations between the outcomes of scheduled appointments (arrived, canceled, or missed) and selected characteristics of the populations in patients' census tracts of residence (racial/ethnic segregation based on population composition, levels of poverty, violent crime, and perceived safety and social capital), controlling for patients' age, gender, type of insurance, and type of clinic service.

KEY RESULTS

Overall, 17.5% of appointments were missed. For appointments among patients residing in census tracts in the highest versus lowest quartile for each population metric, adjusted odds ratios (aORs) for missed appointments were 1.27 (CI 1.19, 1.35) for the rate of violent crime, 1.27 (CI 1.20, 1.34) for the proportion Hispanic, 1.19 (CI 1.12, 1.27) for the proportion living in poverty, 1.13 (CI 1.05, 1.20) for the proportion of the census tract population that was Black, and 1.06 (CI 1.01, 1.11 for perceived neighborhood safety.

CONCLUSIONS

Characteristics of the places where patients reside are associated with missing scheduled appointments, including high levels of racial/ethnic segregation, poverty, and violent crime and low levels of perceived neighborhood safety. As such, targeting efforts to improve access for patients living in such neighborhoods will be particularly important to address underlying social determinants of access to health care.

Targeting Missed Care Opportunities Using Modern Communication Methods: A Quality Improvement Initiative to Improve Access to CT and MRI Appointments

PURPOSE

To evaluate the impact of automated text and phone call reminder systems on CT (computed tomography) and MRI (magnetic resonance imaging) missed care opportunities.

METHODS

This was an IRB (institutional review board) exempt prospective interventional quality improvement study. The proportion of missed care opportunities (appointment made, no imaging performed) related to scheduled CT and MRI examinations were evaluated over 2 months (Month 1: reminder phone calls by staff 48-96 hours prior and mailed letter 1-2 weeks prior; Month 2: no manual call or letter, automated text message 24 hours prior, automated phone call 72 hours prior, automated patient portal message 7 days prior). The proportion of missed care opportunities was calculated in aggregate and by modality. Process control p-charts were generated. An a priori power analysis was performed. Chi-squared tests were performed. p-value < 0.017 was considered significant after Bonferroni correction.

RESULTS

Missed care opportunities occurred for 2.82% (292/10348; 95% CI: 2.51-3.16) of all CT and MRI appointments using traditional communication and 2.44% (262/10719; 95% CI: 2.16-2.75) using automated communication (p = 0.09). Automated messaging did not significantly change the proportion of missed care opportunities for CT (traditional: 2.62% [95% CI: 2.23-3.06] vs. automated: 2.06% [95% CI: 1.70-2.48], p = 0.05) or MRI (traditional: 3.1% [95% CI: 2.60-3.66] vs. automated: 2.83% [95% CI: 2.40-3.30], p = 0.43). Process control p-charts showed dominance of common cause variation.

CONCLUSION

Automated messaging did not meaningfully change the overall proportion of missed care opportunities compared to traditional human-initiated phone calls. Automated communications may reduce cost and improve efficiency without adversely affecting access to 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.

Sociodemographic and Appointment Factors Affecting Missed Opportunities to Provide Neonatal Ultrasound Imaging

PURPOSE

The aim of this study was to assess disparities in outpatient imaging missed care opportunities (IMCOs) for neonatal ultrasound by sociodemographic and appointment factors at a large urban pediatric hospital.

METHODS

A retrospective review was performed among patients aged 0 to 28 days receiving one or more outpatient appointments for head, hip, renal, or spine ultrasound at the main hospital or satellite sites from 2008 to 2018. An IMCO was defined as a missed ultrasound or cancellation <24 hours in advance. Population-average correlated logistic regression modeling estimated the odds of IMCOs for six sociodemographic (age, sex, race/ethnicity, language, insurance, and region of residence) and seven appointment (type of ultrasound, time, day, season, site, year, and distance to appointment) factors. The primary analysis included unknown values as a separate category, and the secondary analysis used multiple imputation to impute genuine categories from unknown variables.

RESULTS

The data set comprised 5,474 patients totaling 6,803 ultrasound appointments. IMCOs accounted for 4.4% of appointments. IMCOs were more likely for Black (odds ratio [OR], 3.31; P < .001) and other-race neonates (OR, 2.66; P < .001) and for patients with public insurance (OR, 1.78; P = .002). IMCOs were more likely for appointments at the main hospital compared with satellites (P < .001), during work hours (P = .021), and on weekends (P < .001). Statistical significance for primary and secondary analyses was quantitatively similar and qualitatively identical.

CONCLUSIONS

Marginalized racial groups and those with public insurance had a higher rate of IMCOs in neonatal ultrasound. This likely represents structural inequities faced by these communities, and more research is needed to identify interventions to address these inequities in care delivery for vulnerable neonatal populations.

The Impact of Social Determinants of Health on Lung Cancer Screening Utilization

PURPOSE

The purpose of this study was to understand how social determinants of health might influence lung cancer screening (LCS) adherence.

METHODS

All LCS low-dose CT appointments scheduled at an urban, tertiary care academic medical center in the New England region between January 1, 2015, and December 31, 2018, were included. Demographics, insurance type, information on social determinants of health, and appointment status were obtained from the electronic medical records. Multivariate logistic regression was performed to evaluate the associations between the appointment status and the explanatory variables.

RESULTS

During the study period, 2,797 patients had 4,747 scheduled LCS appointments. Forty-one percent of patients had at least one missed appointment, and 32.7% of all scheduled appointments were missed. The retention rate of patients after the baseline examination was approximately 50%. Self-reported Black race was independently associated with 1.5 times the odds of missing appointments compared with White race (P = .012). Patients with Medicaid had 6.1 times the odds of missing appointments compared with patients with private insurance and 4.6 times the odds of missing appointments compared with patients with Medicare (P < .0001). Housing insecurity was a risk factor for failing to follow up after the baseline examination, with an odds ratio of 5.3 (P = .0013).

CONCLUSIONS

The high rate of missed LCS appointments underscores the need to improve screening compliance. The identification of specific social determinants of health that contribute to disparities in access to LCS could empower policymakers, hospital systems, and providers to use targeted interventions to promote more equitable access.

Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation

The aim of the study was to evaluate the performance of the Prophet forecasting procedure, part of the Facebook open-source Artificial Intelligence portfolio, for forecasting variations in radiological examination volumes. Daily CT and MRI examination volumes from our institution were extracted from the radiology information system (RIS) database. Data from January 1, 2015, to December 31, 2019, was used for training the Prophet algorithm, and data from January 2020 was used for validation. Algorithm performance was then evaluated prospectively in February and August 2020. Total error and mean error per day were evaluated, and computational time was logged using different Markov chain Monte Carlo (MCMC) samples. Data from 610,570 examinations were used for training; the majority were CTs (82.3%). During retrospective testing, prediction error was reduced from 19 to < 1 per day in CT (total 589 to 17) and from 5 to < 1 per day (total 144 to 27) in MRI by fine-tuning the Prophet procedure. Prospective prediction error in February was 11 per day in CT (9934 predicted, 9667 actual) and 1 per day in MRI (2484 predicted, 2457 actual) and was significantly better than manual weekly predictions (p = 0.001). Inference with MCMC added no substantial improvements while vastly increasing computational time. Prophet accurately models weekly, seasonal, and overall trends paving the way for optimal resource allocation for radiology exam acquisition and interpretation.

Optimization of Radiology Workflow with Artificial Intelligence

The potential of artificial intelligence (AI) in radiology goes far beyond image analysis. AI can be used to optimize all steps of the radiology workflow by supporting a variety of nondiagnostic tasks, including order entry support, patient scheduling, resource allocation, and improving the radiologist's workflow. This article discusses several principal directions of using AI algorithms to improve radiological operations and workflow management, with the intention of providing a broader understanding of the value of applying AI in the radiology department.

Education of Radiologists in Healthcare Disparities

Disparities exist in access to a multitude of screening and diagnostic imaging examinations and procedures. To address these disparities within radiology, emphasis so far has been placed upon diversifying the workforce and formally educating trainees on healthcare disparities. Currently, there is no organized and nationally accepted educational program or content for practicing radiologists specific to diversity and healthcare disparity. This void can be addressed by providing an educational curriculum framework for practicing radiologists based on three key factors: individual efforts, calling for institutional change, and national collaboration. Individual efforts should focus on acknowledging the existence of disparities, understanding the contribution of one's implicit bias in perpetuating disparities, understanding and highlighting issues related to insurance coverage of radiology examinations, and participating in radiology political action committees. These efforts can be facilitated by a consolidated web-based training program for practicing radiologists. To pave the way for meaningful systemic change, the implementation of institutional change like that initiated by the Culture of Safety movement in 2002 is needed. A national collaborative effort initiated by radiology organizations to empower radiologists and recognize positive changes would further provide support. SUMMARY: A three-pronged educational framework combining individual radiologist education, institutional change, and national collaboration will enable radiologists to play a role in addressing imaging-related disparities in healthcare.

Sociodemographic and Geographic Factors Associated With Non-Obstetrical Ultrasound Imaging Utilization: A Population-Based Study

OBJECTIVE

Ultrasound is one of the most commonly used imaging modalities, though some populations face barriers in accessing ultrasound services, potentially resulting in disparities in utilization. The objective of this study was to assess the association between sociodemographic and geographic factors and non-obstetrical ultrasound utilization in the province of Saskatchewan, Canada.

METHODS

All non-obstetrical ultrasound exams performed from 2014 to 2018 in Saskatchewan, Canada were retrospectively identified from province-wide databases. Univariate and multivariate Poisson regression analyses were performed to assess the association between ultrasound utilization and sex, age, First Nations status, Charlson Comorbidity Index, urban vs. rural residence, geographic remoteness, and neighborhood income.

RESULTS

A total of 1,324,846 individuals (5,857,044 person-years) were included in the analysis. Female sex (adjusted incidence rate ratio [aIRR], 2.20; 95% confidence interval [CI], 2.19-2.22), age (aIRR, 4.97; 95% CI, 4.90-5.05 for ≥57 years vs. <11 years), comorbidities (aIRR, 4.36 for Charlson Comorbidity Index >10 vs. 0; 95% CI, 3.78-5.03), and higher neighborhood income (aIRR, 1.04; 95% CI, 1.02-1.05 for highest vs. lowest quintile) were associated with higher rates of ultrasound utilization. Individuals who were status First Nations (aIRR, 0.91; 95% CI, 0.90-0.92) or resided in geographically remote areas (aIRR, 0.87 for most vs. least remote; 95% CI, 0.83-0.91) had lower rates of ultrasound utilization. Individuals who lived in a rural area also had lower rates of ultrasound utilization (aIRR, 0.93; 95% CI, 0.92-0.94).

CONCLUSION

Substantial disparities exist in non-obstetrical ultrasound utilization among individuals in low-income neighborhoods, status First Nations individuals, and individuals in rural and remote communities.

Machine learning prediction of diabetic foot ulcers in the inpatient population

BACKGROUND

The objective of this study was to create an algorithm that could predict diabetic foot ulcer (DFU) incidence in the in-patient population.

MATERIALS AND METHODS

The Nationwide Inpatient Sample datasets were examined from 2008 to 2014. The International Classification of Diseases 9th Edition Clinical Modification (ICD-9-CM) and the Agency for Healthcare Research and Quality comorbidity codes were used to assist in the data collection. Chi-square testing was conducted, using variables that positively correlated with DFUs. For descriptive statistics, the Student T-test, Wilcoxon rank sum test, and chi-square test were used. There were six predictive variables that were identified. A decision tree model CTREE was utilized to help develop an algorithm.

RESULTS

326,853 patients were noted to have DFU. The major variables that contributed to this diagnosis (both with p < 0.001) were cellulitis (OR 63.87, 95% CI [63.87-64.49]) and Charcot joint (OR 25.64, 95% CI [25.09-26.20]). The model performance of the six-variable testing data was 79.5% (80.6% sensitivity and 78.3% specificity). The area under the curve (AUC) for the 6-variable model was 0.88.

CONCLUSION

We developed an algorithm with a 79.8% accuracy that could predict the likelihood of developing a DFU.

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.

Interventional Radiology ex-machina: impact of Artificial Intelligence on practice

Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process data, understand its meaning and provide the desired outcome, continuously redefining its logic. AI was mainly introduced via artificial neural networks, developed in the early 1950s, and with its evolution into "computational learning models." Machine Learning analyzes and extracts features in larger data after exposure to examples; Deep Learning uses neural networks in order to extract meaningful patterns from imaging data, even deciphering that which would otherwise be beyond human perception. Thus, AI has the potential to revolutionize the healthcare systems and clinical practice of doctors all over the world. This is especially true for radiologists, who are integral to diagnostic medicine, helping to customize treatments and triage resources with maximum effectiveness. Related in spirit to Artificial intelligence are Augmented Reality, mixed reality, or Virtual Reality, which are able to enhance accuracy of minimally invasive treatments in image guided therapies by Interventional Radiologists. The potential applications of AI in IR go beyond computer vision and diagnosis, to include screening and modeling of patient selection, predictive tools for treatment planning and navigation, and training tools. Although no new technology is widely embraced, AI may provide opportunities to enhance radiology service and improve patient care, if studied, validated, and applied appropriately.

Notification System for Overdue Radiology Recommendations Improves Rates of Follow-Up and Diagnosis

OBJECTIVE. The purpose of this study was to quantify improved rates of follow-up and additional important diagnoses made after notification for overdue workups recommended by radiologists. MATERIALS AND METHODS. Standard reports from imaging studies performed at our institution from October through November 2016 were searched for the words "recommend" or "advised," yielding 9784 studies. Of these, 5245 were excluded, yielding 4539 studies; reports for 1599 of these 4539 consecutive studies were reviewed to identify firm or soft recommendations or findings requiring immediate management. If recommended follow-ups were incomplete within 1 month of the advised time, providers were notified. Compliance was calculated before and after notification and was compared using a one-sample test of proportion. RESULTS. Of 1599 patients, 92 were excluded because they had findings requiring immediate management, and 684 were excluded because of soft recommendations, yielding 823 patients. Of these patients, 125 were not yet overdue for follow-up and were excluded, and 18 were excluded because of death or transfer to another institution. Of the remaining 680 patients, follow-up was completed for 503 (74.0%). A total of 177 (26.0%) of the 680 patients were overdue for follow-up, and providers were notified. Of these 177 patients, 36 (20.3%) completed their follow-ups after notification, 34 (19.2%) had follow-up designated by the provider as nonindicated, and 107 (60.5%) were lost to follow-up, yielding four clinically important diagnoses: one biopsy-proven malignancy, one growing mass, and two thyroid nodules requiring biopsy. The rate of incomplete follow-ups after communication decreased from 26.0% (177/680) to 20.7% (141/680) (95% CI, 17.7-23.9%; p = .002), with a 20.4% reduction in relative risk of noncompliance, and 39.5% (70/177) of overdue cases were resolved when nonindicated studies were included. CONCLUSION. Notification of overdue imaging recommendations reduces incomplete follow-ups and yields clinically important diagnoses.

Impact of Patient Socioeconomic Disparities on Time to Tympanostomy Tube Placement

OBJECTIVES

Extensive literature exists documenting disparities in access to healthcare for patients with lower socioeconomic status (SES). The objective of this study was to examine access disparities and differences in surgical wait times in children with the most common pediatric otolaryngologic surgery, tympanostomy tubes (TT).

METHODS

A retrospective cohort study was performed at a tertiary children's hospital. Children ages <18 years who received a first set of tympanostomy tubes during 2015 were studied. Patient demographics and markers of SES including zip code, health insurance type, and appointment no-shows were recorded. Clinical measures included risk factors, symptoms, and age at presentation and first TT.

RESULTS

A total of 969 patients were included. Average age at surgery was 2.11 years. Almost 90% were white and 67.5% had private insurance. Patients with public insurance, ≥1 no-show appointment, and who lived in zip codes with the median income below the United States median had a longer period from otologic consult and preoperative clinic to TT, but no differences were seen in race. Those with public insurance had their surgery at an older age than those with private insurance (P < .001) and were more likely to have chronic otitis media with effusion as their indication for surgery (OR: 1.8, 95% CI: 1.2-2.5, P = .003).

CONCLUSIONS

Lower SES is associated with chronic otitis media with effusion and a longer wait time from otologic consult and preoperative clinic to TT placement. By being transparent in socioeconomic disparities, we can begin to expose systemic problems and move forward with interventions.

LEVEL OF EVIDENCE

4.

Practical Tips for Creating a Diversity, Equity, and Inclusion Committee: Experience From a Multicenter, Academic Radiology Department

PURPOSE

Coronavirus disease 2019 and the publicly documented deaths of countless Black individuals have highlighted the need to confront systemic racism, address racial/ethnic disparities, and improve diversity and inclusion in radiology. Several radiology departments have begun to create diversity, equity, and inclusion (DEI) committees to systematically address DEI issues in radiology. However, there are few articles that provide departments with guidance on how to create DEI committees to comprehensively address DEI issues in radiology. The purpose of this review is to provide readers with a framework and practical tips for creating a comprehensive, institutionally aligned radiology DEI committee.

METHODS

The authors describe key components of the strategic planning process and lessons learned in the creation of a radiology DEI committee, on the basis of the experience of an integrated, academic northeastern radiology department.

RESULTS

A hospital-based strategic planning process defining the DEI vision, mission, goals, and strategies was used to inform the formation of the radiology department DEI committee. The radiology department performed gap analyses by conducting internal and external research. Strengths, weaknesses, opportunities, and threats analyses were performed on the basis of consultations with institutional and other departmental DEI leaders as well as DEI leaders from other academic medical centers. This framework served as the basis for the creation of the radiology departmental DEI committee, including a steering committee and four task forces (education, research, patient experience, and workforce development), each charged with addressing specific institutional goals and strategies.

CONCLUSIONS

This review provides academic radiology departments with a blueprint to create a comprehensive, institutionally aligned radiology DEI committee.

Public Health Foundations for Radiology Resident Education: Healthcare Disparities in Radiology

Increasing recognition within the medical literature and by the Accreditation Council for Graduate Medical Education has been attributed to the need for enhanced resident education on concepts related to public health and health equity. Despite increasing documentation of pervasive inequalities within the scope of radiology, dedicated curricula designed to improve cultural competency and understanding of healthcare disparities among radiology trainees remains sparse. With relatively fewer patient interactions, radiology trainees are particularly susceptible to insufficient contextual awareness of how socioeconomic factors influence patient health and behaviors, physician recommendations, and ultimately clinical outcomes. The purpose of this article is to provide a high-yield background of foundational health equity and disparity concepts for radiology trainees, from which additional educational curricula may be derived. Specifically, this article will discuss the fundamental socioeconomic factors known to contribute to discrepant access and use of imaging services, followed by areas in radiology with well-documented disparities of which trainees should be aware. Lastly, previous and current strategies for addressing disparities in radiology will be discussed with the ultimate goal of stimulating trainee participation and the development of novel approaches.

The impact of common variables on non-attendance at a radiology centre in Singapore

INTRODUCTION

With the increasing demand for medical imaging, non-attendance inhibits private and public radiology practices in Singapore from providing timely care and achieving maximal efficiency. Missed radiological appointments adversely affect clinical and economic outcomes and strain the finite healthcare resources. We examined the prevalence and predictors of patient non-attendance for radiological services at a regional public hospital in Singapore and compared them against other medical imaging centres globally.

METHODS

Outpatient records of patients who were scheduled for specialised medical imaging obtained from Radiological Information System (RIS) were retrospectively reviewed. Analysed variables include patient demographics, radiology modalities, visit statuses and appointment lead times where Pearson's chi-square test and Fisher's exact test were used for categorical variables, and independent sample t-test was used for continuous variables. The association between each patient characteristic and non-attendance status was assessed using Binary Logistics Regression. Variables that showed statistical significance in univariate analysis were included in the multivariate logistic regression model to identify the independent risk factors associated with non-attendance.

RESULTS

Among the 59,748 outpatient appointments with medical imaging requests, 15.5% did not turn up for their appointments. Logistic regression indicated that patient's age, ethnicity, subsidy status, house ownership, living vicinity to regional hospital cluster, appointment wait times, appointment hours and appointment months were significant factors associated with the failure to attend scheduled radiological examinations.

CONCLUSION

Even though predictors of non-attendance remained consistent across medical imaging centres worldwide, Singapore reported a higher prevalence of missed appointments calling for future exploratory studies to understand the population's health-seeking behaviours and ordering patterns of clinicians.

IMPLICATIONS FOR PRACTICE

Comparison and identification of these predictors will assist in the design of targeted interventions that may improve patient's adherence and utilisation of imaging services.

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

The application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve clinical processes and the provision of healthcare. A central motivation in this regard is to advance patient treatment by reducing human error and increasing the accuracy of prognosis, diagnosis and therapy decisions. There is, however, also increasing awareness about bias in ML technologies and its potentially harmful consequences. Biases refer to systematic distortions of datasets, algorithms, or human decision making. These systematic distortions are understood to have negative effects on the quality of an outcome in terms of accuracy, fairness, or transparency. But biases are not only a technical problem that requires a technical solution. Because they often also have a social dimension, the 'distorted' outcomes they yield often have implications for equity. This paper assesses different types of biases that can emerge within applications of ML in radiology, and discusses in what cases such biases are problematic. Drawing upon theories of equity in healthcare, we argue that while some biases are harmful and should be acted upon, others might be unproblematic and even desirable-exactly because they can contribute to overcome inequities.

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.

Compliance with Short-Interval Follow-up MRI after Benign Concordant MRI-guided Breast Biopsy

OBJECTIVE

To evaluate compliance with short-interval follow-up MRI after benign concordant MRI-guided breast biopsy.

METHODS

This institutional review board-approved retrospective study included all benign concordant MRI-guided biopsies performed between January 1, 2010, and May 1, 2018. The following was collected from the electronic medical record: patient and lesion characteristics, short-interval follow-up MRI recommendation, communication to referring physician, follow-up imaging, repeat biopsies, biopsy outcome, and patient insurance status. Compliance with recommendations was defined as follow-up MRI within 9 months of biopsy.

RESULTS

Among 98 patients, there were 107 lesions with benign concordant MRI-guided biopsy results that met study criteria. After excluding 7 patients who underwent subsequent mastectomy, 50/91 (54.9%) patients were recommended short-interval follow-up MRI. Of these, 33/50 (66.0%) had a short-interval follow-up MRI. Direct communication of the short-interval follow-up recommendation was documented in the biopsy report for 4/50 (8%) patients. Subsequent MRI was available for 77/107 (72%) lesions at a median of 29 months following MRI-guided biopsy (range, 3-96 months). Subsequent mammography was available for 21/30 (70%) remaining lesions at a median of 47 months following MRI-guided biopsy (range, 23-88 months). There were two repeat biopsies, with one subsequent malignancy, resulting in a false-negative rate of 0.9% (1/107).

CONCLUSION

When short-interval follow-up MRI was recommended following benign concordant MRI-guided breast biopsy, compliance was 66.0%. Lack of communication of the recommendation may at least partially explain the low compliance. The low false-negative rate (0.9%) suggests routine short-interval follow-up MRI may be unnecessary following benign concordant MRI-guided biopsy.

Analysis of socioeconomic and demographic factors and imaging exam characteristics associated with missed appointments in pediatric radiology

BACKGROUND

Missed appointments can have an adverse impact on health outcomes by delaying appropriate imaging, which can be critical in influencing treatment decisions.

OBJECTIVE

To assess for socioeconomic and imaging exam factors associated with missed appointments among children scheduled for diagnostic imaging.

MATERIALS AND METHODS

We retrospectively analyzed children (<18 years) scheduled for outpatient diagnostic imaging during a 12-month period. In doing so, we obtained socioeconomic and radiology exam characteristics (modality, intravenous contrast administration, radiation and use of sedation) data from the electronic medical record. We employed multivariate logistic regression to assess the association of socioeconomic, demographic and imaging exam characteristics with imaging missed appointments.

RESULTS

In total, 7,275 children met inclusion criteria. The mean age was 8.8 years (standard deviation [SD] = 6.2 years) and the study population consisted of 52% female gender, 69% White race, 38% adolescent age group and 32% with a median household income by ZIP-code category of <$50,000. Logistic regression showed increased likelihood of missed appointments among children of Black/African-American race (odds ratio [OR] = 1.9; 95% confidence interval [CI] = 1.4-2.5); with insurance categories including Medicaid (OR=2.0; 95% CI=1.6-2.4), self-pay (OR=2.1; 95% CI=1.3-3.6) and other (OR=2.7; 95% CI=1.3-5.4); with <$50,000 median household income by ZIP-code category (OR=1.7; 95% CI=1.4-2.0); and with examination wait time of 7-21 days (OR=2.7; 95% CI=2.1-3.5) and >21 days (OR=3.7; 95% CI=2.9-4.8). The use of radiation, intravenous contrast agent or sedation was not associated with increased likelihood of missed appointments.

CONCLUSION

Expanding our knowledge of how different socioeconomic and imaging-related factors influence missed appointments among children can serve as a foundational step to better understand existing and emerging disparities and inform strategies to advance health equity efforts in radiology.

Patient Race/Ethnicity and Diagnostic Imaging Utilization in the Emergency Department: A Systematic Review

PURPOSE

Diagnostic imaging often is a critical contributor to clinical decision making in the emergency department (ED). Racial and ethnic disparities are widely reported in many aspects of health care, and several recent studies have reported a link between patient race/ethnicity and receipt of imaging in the ED.

METHODS

The authors conducted a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching three databases (PubMed, Embase, and the Cochrane Library) through July 2020 using keywords related to diagnostic imaging, race/ethnicity, and the ED setting, including both adult and pediatric populations and excluding studies that did not control for the important confounders of disease severity and insurance status.

RESULTS

The search strategy identified 7,313 articles, of which 5,668 underwent title and abstract screening and 238 full-text review, leaving 42 articles meeting the inclusion criteria. Studies were predominately conducted in the United States (41), split between adult (13) and pediatric (17) populations or both (12), and spread across a variety of topics, mostly focusing on specific anatomic regions or disease processes. Most studies (30 of 42 [71.4%]) reported an association between Black, African American, Hispanic, or nonwhite race/ethnicity and decreased receipt of imaging.

CONCLUSIONS

Despite heterogeneity among studies, patient race/ethnicity is linked with receipt of diagnostic imaging in the ED. The strength and directionality of this association may differ by specific subpopulation and disease process, and more efforts to understand potential underlying factors are needed.

Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics

There is great potential for artificial intelligence (AI) applications, especially machine learning and natural language processing, in medical imaging. Much attention has been garnered by the image analysis tasks for diagnostic decision support and precision medicine, but there are many other potential applications of AI in radiology and have potential to enhance all levels of the radiology workflow and practice, including workflow optimization and support for interpretation tasks, quality and safety, and operational efficiency. This article reviews the important potential applications of informatics and AI related to process improvement and operations in the radiology department.

Demographic factors associated with missed follow-up among solid tumor patients treated at a large multi-site academic institution

Aim: To identify demographic predictors of patients who miss oncology follow-up, considering that missed follow-up has not been well studies in cancer patients. Methods: Patients with solid tumors diagnosed from 2007 to 2016 were analyzed (n = 16,080). Univariate and multivariable logistic regression models were constructed to examine predictors of missed follow-up. Results: Our study revealed that 21.2% of patients missed ≥1 follow-up appointment. African-American race (odds ratio [OR] 1.33; 95% CI: 1.17-1.51), Medicaid insurance (OR 1.59; 1.36-1.87), no insurance (OR 1.66; 1.32-2.10) and rural residence (OR 1.78; 1.49-2.13) were associated with missed follow-up. Conclusion: Many cancer patients miss follow-up, and inadequate follow-up may influence cancer outcomes. Further research is needed on how to address disparities in follow-up care in high-risk patients.

Disparities in PET Imaging for Prostate Cancer at a Tertiary Academic Medical Center

The purpose of this study was to evaluate differences between patients receiving ¹⁸F-fluciclovine and ⁶⁸Ga-prostate-specific membrane antigen (⁶⁸Ga-PSMA-11) for biochemically recurrent prostate cancer at a tertiary medical center. Methods: All ¹⁸F-fluciclovine and ⁶⁸Ga-PSMA-11 PET studies performed at the University of California San Francisco from October 2015 to January 2020 were reviewed. Age, race/ethnicity, primary language, body mass index, insurance type, and home address were obtained through the electronic medical record. A logistic regression model was used to evaluate the predictor variables. Results: In total, 1,502 patients received ⁶⁸Ga-PSMA-11 and 254 patients received ¹⁸F-fluciclovine. Black patients had increased odds of receiving imaging with ¹⁸F-fluciclovine versus ⁶⁸Ga-PSMA-11 compared with non-Hispanic White patients (odds ratio, 3.88; 95% CI, 1.90-7.91). There were no other statistically significant differences. Conclusion: In patients receiving molecular imaging for prostate cancer at a single U.S. tertiary medical center, access to ⁶⁸Ga-PSMA-11 for Black patients was limited, compared with non-Hispanic White patients, by a factor of nearly 4.

Analysis of the Effects of a Patient-Centered Rideshare Program on Missed Appointments and Timeliness for MRI Appointments at an Academic Medical Center

PURPOSE

The aim of this study was to assess the differences in timeliness to MRI appointments and missed MRI appointment rates before and after the implementation of a rideshare program.

METHODS

Retrospective analysis of a rideshare program was performed 9 months after implementation to compare the effects before and after implementation. Variables obtained included demographics, MRI appointment variables, and data related to rideshare use. Descriptive statistics and linear and logistic regression analyses were used to compare demographic characteristics among patients using the rideshare program with (1) those who did not use the rideshare program after implementation and (2) patients before rideshare implementation. Rates of missed appointments derived from patient-related, same-day appointment cancellations were analyzed using logistic regression analyses. Timeliness was analyzed using linear regression analyses. All analyses were adjusted for potential confounders.

RESULTS

Of 7,707 patients scheduled for MRI appointments during the postintervention period, 151 patients used the rideshare service (1.95%). There were no statistically significant differences in missed appointment rates after rideshare implementation (adjusted odds ratio, 1.09; 95% confidence interval, 0.93-1.27; P = .275). Patients using the rideshare service were more likely to be on time (adjusted coefficient = 13.0; 95% confidence interval, 5.4-20.5; P = .001). Older patients (P = .001), unemployed patients (P < .001), and patients without commercial insurance (P < .001) were more likely to use the rideshare service.

CONCLUSIONS

Implementation of a rideshare program did not significantly decrease missed appointment rates, but it significantly improved timeliness to MRI appointments while assisting at-risk patient populations reporting transportation barriers.

Pelvic MRI in the diagnosis and staging of pelvic endometriosis: added value of structured reporting and expertise

PURPOSE

To compare the diagnostic characteristics of routine-read (RR), structured-reported read (SR), and structured expert-read pelvic (SER) MRI for staging of pelvic endometriosis in a tertiary care academic center.

METHODS

Of 530 patients with endometriosis (2013-2018), 59/530 (11.1%) were staged surgically and underwent pelvic MRI. Radiology reports were considered RR; MRI studies were independently reassessed by SR and SER. Involvement was recorded by compartment [anterior (AC), middle (MC), posterior (PC), adnexal (AX), and other (OC)]. Diagnostic discrepancy between review methods was assessed with McNemar's test. Interobserver agreement was assessed using Cohen's unweighted kappa.

RESULTS

Of 295 compartments in 59 women (mean age = 38.8 years; range 20-69), 147/295 (49.8%) had confirmed endometriosis. Overall sensitivity: RR = 42.9%; SR = 86.4%; SER = 74.2%. SR's increased sensitivity was significant for PC (p < 0.001), MC (p < 0.001), AC (p = 0.001), AX (p = 0.038). Higher sensitivity by SER was significant for PC (p < 0.001), MC (p = 0.004) and AC (p < 0.001), but not AX (p > 0.05). Overall specificity: RR = 95.3%; SR = 45.9%; SER = 81.8%. SER specificity was no different than RR for PC or AX (p > 0.5). RR sensitivity relied heavily on detection of AX involvement, whereas SR and SER showed additional sites of disease while maintaining comparable specificity for SER. Overall agreement between SR and SER was fair [k = 0.342 (95% CI 0.25, 0.44)].

CONCLUSIONS

Even at a tertiary care academic center, SER outperforms both SR and RR in the assessment of pelvic endometriosis. Although lack of expertise may negatively impact specificity, use of structured reporting is significantly more sensitive than RR. Therefore, its use can be of assistance in surgical planning and patient counseling.

Analysis of Out-of-Pocket Cost of Lung Cancer Screening for Uninsured Patients Among ACR-Accredited Imaging Centers

PURPOSE

To determine the variability in out-of-pocket costs of lung cancer screening (LCS) for uninsured patients and assess accessibility of this information by telephone or Internet.

METHODS

LCS centers from the ACR's LCS database were randomly selected. Centers were called between July and August 2019 to determine out-of-pocket cost. Telephone call variables, accessibility of cost information on screening centers' websites, screening centers' chargemasters, and publicly available facility and state insurance coverage variables were obtained. Cost information was summarized using descriptive analyses. Multiple variable linear regression analyses were conducted to evaluate effects of facility and state-level characteristics on out-of-pocket costs.

RESULTS

Fifty-five ACR-accredited LCS centers were included with 78% (43 of 55) willing to provide out-of-pocket cost. Average out-of-pocket cost was $583 ± $607 (mean ± standard deviation), range $49 to $2,409. Average telephone call length 6 ± 3.8 min. Two of fifty-five screening centers' websites provided out-of-pocket cost information, and one matched cost given over the telephone. A chargemaster was found for 30 of 55 screening centers. No statistically significant differences in out-of-pocket costs were found by geographic region, state percentages of uninsured residents, state percentages of residents with public insurance, or facility safety net hospital affiliation.

DISCUSSION

Out-of-pocket LCS costs for uninsured patients and availability of this information is highly variable. Radiology practices should be aware of this variability that may influence participation rates among uninsured patients.