Impact of Improved Screening Mammography Recall Lay Letter Readability on Patient Follow-Up

[Computed tomography coronary angiography : What does the nonradiologist expect from the radiologist?]

BACKGROUND

Coronary computed tomography angiography (CCTA) has become a central tool for the primary diagnosis of stable coronary artery disease (CAD). Its integration into the service catalog of the German statutory health insurance will not only transform the way patients are examined and treated but also enhance the collaboration between nonradiologists and radiologists.

OBJECTIVE

This article explores the requirements nonradiologists have for CCTA and identifies ways to promote successful interdisciplinary communication.

MATERIALS AND METHODS

The study addresses criteria for proper patient selection and preparation for CCTA. It considers the perspectives and needs of patients and various medical specialties, highlighting essential aspects of interdisciplinary communication.

RESULTS

CCTA enables precise clarification of CAD and should be used for patients with a pretest probability of chronic CAD between 15 and 50%. Clear action plans in the diagnostic report are crucial to assist general practitioners and cardiologists in treatment planning. Patients expect clear information about the procedure, possible risks, and results.

CONCLUSION

Close collaboration between various medical disciplines is essential for the successful implementation of CCTA. Clear, structured diagnostic reports with annotated images, along with regular case discussions and feedback loops, can improve report interpretation and interdisciplinary communication. Patient-friendly reports can make diagnostic results more understandable and enhance patient adherence.

Patient-centered radiology: a roadmap for outpatient imaging

Creating a patient-centered experience is becoming increasingly important for radiology departments around the world. The goal of patient-centered radiology is to ensure that radiology services are sensitive to patients' needs and desires. This article provides a framework for addressing the patient's experience by dividing their imaging journey into three distinct time periods: pre-exam, day of exam, and post-exam. Each time period has aspects that can contribute to patient anxiety. Although there are components of the patient journey that are common in all regions of the world, there are also unique features that vary by location. This paper highlights innovative solutions from different parts of the world that have been introduced in each of these time periods to create a more patient-centered experience. CLINICAL RELEVANCE STATEMENT: Adopting innovative solutions that help patients understand their imaging journey and decrease their anxiety about undergoing an imaging examination are important steps in creating a patient centered imaging experience. KEY POINTS: • Patients often experience anxiety during their imaging journey and decreasing this anxiety is an important component of patient centered imaging. • The patient imaging journey can be divided into three distinct time periods: pre-exam, day of exam, and post-exam. • Although components of the imaging journey are common, there are local differences in different regions of the world that need to be considered when constructing a patient centered experience.

Forgotten clientele: A systematic review of patient-centered pathology reports

CONTEXT

Patient portals, designed to give ready access to medical records, have led to important improvements in patient care. However, there is a downside: much of the information available on portals is not designed for lay people. Pathology reports are no exception. Access to complex reports often leaves patients confused, concerned and stressed. We conducted a systematic review to explore recommendations and guidelines designed to promote a patient centered approach to pathology reporting.

DESIGN

In consultation with a research librarian, a search strategy was developed to identify literature regarding patient-centered pathology reports (PCPR). Terms such as "pathology reports," "patient-centered," and "lay-terms" were used. The PubMed, Embase and Scopus databases were searched during the first quarter of 2023. Studies were included if they were original research and in English, without date restrictions.

RESULTS

Of 1,053 articles identified, 17 underwent a full-text review. Only 5 studies (≈0.5%) met eligibility criteria: two randomized trials; two qualitative studies; a patient survey of perceived utility of potential interventions. A major theme that emerged from the patient survey/qualitative studies is the need for pathology reports to be in simple, non-medical language. Major themes of the quantitative studies were that patients preferred PCPRs, and patients who received PCPRs knew and recalled their cancer stage/grade better than the control group.

CONCLUSION

Pathology reports play a vital role in the decision-making process for patient care. Yet, they are beyond the comprehension of most patients. No framework or guidelines exist for generating reports that deploy accessible language. PCPRs should be a focus of future interventions to improve patient care.

Patient Characteristics Impact Performance of AI Algorithm in Interpreting Negative Screening Digital Breast Tomosynthesis Studies

Background Artificial intelligence (AI) is increasingly used to manage radiologists' workloads. The impact of patient characteristics on AI performance has not been well studied. Purpose To understand the impact of patient characteristics (race and ethnicity, age, and breast density) on the performance of an AI algorithm interpreting negative screening digital breast tomosynthesis (DBT) examinations. Materials and Methods This retrospective cohort study identified negative screening DBT examinations from an academic institution from January 1, 2016, to December 31, 2019. All examinations had 2 years of follow-up without a diagnosis of atypia or breast malignancy and were therefore considered true negatives. A subset of unique patients was randomly selected to provide a broad distribution of race and ethnicity. DBT studies in this final cohort were interpreted by a U.S. Food and Drug Administration-approved AI algorithm, which generated case scores (malignancy certainty) and risk scores (1-year subsequent malignancy risk) for each mammogram. Positive examinations were classified based on vendor-provided thresholds for both scores. Multivariable logistic regression was used to understand relationships between the scores and patient characteristics. Results A total of 4855 patients (median age, 54 years [IQR, 46-63 years]) were included: 27% (1316 of 4855) White, 26% (1261 of 4855) Black, 28% (1351 of 4855) Asian, and 19% (927 of 4855) Hispanic patients. False-positive case scores were significantly more likely in Black patients (odds ratio [OR] = 1.5 [95% CI: 1.2, 1.8]) and less likely in Asian patients (OR = 0.7 [95% CI: 0.5, 0.9]) compared with White patients, and more likely in older patients (71-80 years; OR = 1.9 [95% CI: 1.5, 2.5]) and less likely in younger patients (41-50 years; OR = 0.6 [95% CI: 0.5, 0.7]) compared with patients aged 51-60 years. False-positive risk scores were more likely in Black patients (OR = 1.5 [95% CI: 1.0, 2.0]), patients aged 61-70 years (OR = 3.5 [95% CI: 2.4, 5.1]), and patients with extremely dense breasts (OR = 2.8 [95% CI: 1.3, 5.8]) compared with White patients, patients aged 51-60 years, and patients with fatty density breasts, respectively. Conclusion Patient characteristics influenced the case and risk scores of a Food and Drug Administration-approved AI algorithm analyzing negative screening DBT examinations. © RSNA, 2024.

Evaluating the Use of ChatGPT to Accurately Simplify Patient-centered Information about Breast Cancer Prevention and Screening

Purpose To evaluate the use of ChatGPT as a tool to simplify answers to common questions about breast cancer prevention and screening. Materials and Methods In this retrospective, exploratory study, ChatGPT was requested to simplify responses to 25 questions about breast cancer to a sixth-grade reading level in March and August 2023. Simplified responses were evaluated for clinical appropriateness. All original and simplified responses were assessed for reading ease on the Flesch Reading Ease Index and for readability on five scales: Flesch-Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, Automated Readability Index, and the Simple Measure of Gobbledygook (ie, SMOG) Index. Mean reading ease, readability, and word count were compared between original and simplified responses using paired t tests. McNemar test was used to compare the proportion of responses with adequate reading ease (score of 60 or greater) and readability (sixth-grade level). Results ChatGPT improved mean reading ease (original responses, 46 vs simplified responses, 70; P < .001) and readability (original, grade 13 vs simplified, grade 8.9; P < .001) and decreased word count (original, 193 vs simplified, 173; P < .001). Ninety-two percent (23 of 25) of simplified responses were considered clinically appropriate. All 25 (100%) simplified responses met criteria for adequate reading ease, compared with only two of 25 original responses (P < .001). Two of the 25 simplified responses (8%) met criteria for adequate readability. Conclusion ChatGPT simplified answers to common breast cancer screening and prevention questions by improving the readability by four grade levels, though the potential to produce incorrect information necessitates physician oversight when using this tool. Keywords: Mammography, Screening, Informatics, Breast, Education, Health Policy and Practice, Oncology, Technology Assessment Supplemental material is available for this article. © RSNA, 2023.

Artificial Intelligence to Improve Patient Understanding of Radiology Reports

Diagnostic imaging reports are generally written with a target audience of other providers. As a result, the reports are written with medical jargon and technical detail to ensure accurate communication. With implementation of the 21st Century Cures Act, patients have greater and quicker access to their imaging reports, but these reports are still written above the comprehension level of the average patient. Consequently, many patients have requested reports to be conveyed in language accessible to them. Numerous studies have shown that improving patient understanding of their condition results in better outcomes, so driving comprehension of imaging reports is essential. Summary statements, second reports, and the inclusion of the radiologist's phone number have been proposed, but these solutions have implications for radiologist workflow. Artificial intelligence (AI) has the potential to simplify imaging reports without significant disruptions. Many AI technologies have been applied to radiology reports in the past for various clinical and research purposes, but patient focused solutions have largely been ignored. New natural language processing technologies and large language models (LLMs) have the potential to improve patient understanding of their imaging reports. However, LLMs are a nascent technology and significant research is required before LLM-driven report simplification is used in patient care.

Addressing Disparities Related to Access of Multimodality Breast Imaging Services Before and During the COVID-19 Pandemic

Despite technological advancements focused on reducing breast cancer mortality through early detection, there have been reported disparities in the access to these imaging services with underserved patient populations (including racial minority groups and patients of low socioeconomic status) showing underutilization compared to other patient groups. These underserved populations tend to have more advanced breast cancer presentations, in part due to delays in diagnosis resulting in later stage of disease presentation. To make matters worse, the COVID-19 pandemic declared in March 2020 has resulted in significant healthcare disruptions leading to extensive delays in breast imaging services which are expected to negatively impact breast cancer mortality long-term. Given the worsening disparity in breast cancer mortality among racial/ethnic minorities and financially disadvantaged groups, it is vital to address these disparity gaps with the goal of reducing the barriers to timely breast cancer diagnosis and addressing breast cancer mortality differences among breast cancer patients. Therefore, this review aims to provide a discussion highlighting the disparities related to breast imaging access, the effects of the COVID-19 pandemic on these disparities, current targeted interventions implemented in breast imaging practices to reduce these disparities, and future directions on the journey to reducing disparity gaps for breast imaging patients. Tackling the root cause factors of the persistent breast cancer-related disparities is critical to meeting the needs of patients who are disadvantaged and can lead to continued improvement in the quality of individualized care for patients who have higher breast cancer morbidity and mortality risks.

Delays in Follow-up Care for Abnormal Mammograms in Mobile Mammography Versus Fixed-Clinic Patients

BACKGROUND

Mobile mammographic services (MM) have been shown to increase breast cancer screening in medically underserved women. However, little is known about MM patients' adherence to follow-up of abnormal mammograms and how this compares with patients from traditional, fixed clinics.

OBJECTIVES

To assess delays in follow-up of abnormal mammograms in women screened using MM versus fixed clinics.

DESIGN

Electronic medical record review of abnormal screening mammograms.

SUBJECTS

Women screened on a MM van or at a fixed clinic with an abnormal radiographic result in 2019 (N = 1,337).

MAIN MEASURES

Our outcome was delay in follow-up of an abnormal mammogram of 60 days or greater. Guided by Andersen's Behavioral Model of Health Services Utilization, we assessed the following: predisposing (age, ethnicity, marital status, preferred language), enabling (insurance, provider referral, clinic site), and need (personal breast cancer history, family history of breast/ovarian cancer) factors.

KEY RESULTS

Only 45% of MM patients had obtained recommended follow-up within 60 days of an abnormal screening compared to 72% of fixed-site patients (p < .001). After adjusting for predisposing, enabling, and need factors, MM patients were 2.1 times more likely to experience follow-up delays than fixed-site patients (CI: 1.5-3.1; p < .001). African American (OR: 1.5; CI: 1.0-2.1; p < .05) and self-referred (OR: 1.8; CI: 1.2-2.8; p < .01) women were significantly more likely to experience delays compared to Non-Hispanic White women or women with a provider referral, respectively. Women who were married (OR: 0.63; CI: 0.5-0.9; p < .01), had breast cancer previously (OR: 0.37; CI: 0.2-0.8; p < .05), or had a family history of breast/ovarian cancer (OR: 0.76; CI: 0.6-0.9; p < .05) were less likely to experience delayed care compared to unmarried women, women with no breast cancer history, or women without a family history of breast/ovarian cancer, respectively.

CONCLUSIONS

A substantial proportion of women screened using MM had follow-up delays. Women who are African American, self-referred, or unmarried are particularly at risk of experiencing delays in care for an abnormal mammogram.

The added value of an artificial intelligence system in assisting radiologists on indeterminate BI-RADS 0 mammograms

OBJECTIVES

To investigate the value of an artificial intelligence (AI) system in assisting radiologists to improve the assessment accuracy of BI-RADS 0 cases in mammograms.

METHODS

We included 34,654 consecutive digital mammography studies, collected between January 2011 and January 2019, among which, 1088 cases from 1010 unique patients with initial BI-RADS 0 assessment who were recalled during 2 years of follow-up were used in this study. Two mid-level radiologists retrospectively re-assessed these BI-RADS 0 cases with the assistance of an AI system developed by us previously. In addition, four entry-level radiologists were split into two groups to cross-read 80 cases with and without the AI. Diagnostic performance was evaluated using the follow-up diagnosis or biopsy results as the reference standard.

RESULTS

Of the 1088 cases, 626 were actually normal (BI-RADS 1 and no recall required). Assisted by the AI system, 351 (56%) and 362 (58%) normal cases were correctly identified by the two mid-level radiologists hence can be avoided for unnecessary follow-ups. However, they would have missed 12 (10 invasive cancers and 2 ductal carcinoma in situ cancers) and 6 (invasive cancers) malignant lesions respectively as a result. These missed lesions were not highly malignant tumors. The inter-rater reliability of entry-level radiologists increased from 0.20 to 0.30 (p < 0.005) by introducing the AI.

CONCLUSION

The AI system can effectively assist mid-level radiologists in reducing unnecessary follow-ups of mammographically indeterminate breast lesions and reducing the benign biopsy rate without missing highly malignant tumors.

KEY POINTS

• The artificial intelligence system could assist mid-level radiologists in effectively reducing unnecessary BI-RADS 0 mammogram recalls and the benign biopsy rate without missing highly malignant tumors. • The artificial intelligence system was capable of detecting low suspicion lesions from heterogeneously and extremely dense breasts that radiologists tended to miss. • The use of an artificial intelligence system may improve the inter-rater reliability and sensitivity, and reduce the reading time of entry-level radiologists in assessing potential lesions in BI-RADS 0 mammograms.

Disparities Associated With Patient Adherence of Post-Breast-Conserving Surgery Surveillance Imaging Protocols

OBJECTIVE

Currently, national and international breast imaging practices utilize variable postsurgical surveillance protocols without uniform recommendations. Due to the innate differences between screening versus diagnostic mammography from scheduling flexibility to out-of-pocket costs, this creates the opportunity for lapses in patient adherence, which has the potential to impact clinical outcomes. The purpose of this study is to evaluate the relationship between sociodemographic factors and postsurgical surveillance imaging protocols on patient adherence rates.

METHODS

This retrospective study reviewed 3 years of surveillance imaging for all patients having breast-conserving surgery at our institution from January 2011 to December 2016. Follow-up adherence was defined as returning for all of the first 3 years of annual follow-up after breast-conserving surgery (institutional surveillance protocol). Associations between adherence to surveillance imaging and patient sociodemographic characteristics were evaluated using univariate and multivariate logistic regression.

RESULTS

The study included 1,082 patients after breast surgery, 715 of whom adhered completely to the first 3 years of annual follow-up (66.1%). Black women were 1.36 times less likely to follow up annually compared with White women (95% confidence interval 1.02-1.80). Similarly, patients with Medicare were 1.84 times less likely to follow up annually compared with patients with private insurance (95% confidence interval 1.34-2.51). Women with benign breast disease after breast-conserving surgery were significantly less likely to adhere to annual surveillance than women with breast cancer.

CONCLUSION

Sociodemographic disparities exist as barriers for annual mammography surveillance in patients after breast-conserving surgery.

Current Gaps in Breast Cancer Screening Among Asian and Asian American Women in the United States

Over the past two decades, the US Asian population has increased 72%, representing the fastest growth rate of any major racial group. Currently, there are over 20 million Asian and Asian American women in the United States, who identify with at least 1 of 19 different origin groups. Although women of Asian ancestry have traditionally been considered low risk for experiencing adverse breast cancer-specific outcomes, aggregated data may mask health disparities seen among subgroups. In the United States, recent data demonstrate that the burden of breast cancer among Asian women has increased each year over the past decade. We aim to characterize challenges faced by Asian and Asian American women in the United States related to cultural stigma, socioeconomic status, and overall access to breast cancer care. An increased understanding of barriers to breast cancer prevention and treatment efforts is needed to develop more effective strategies aimed at reducing disparities in care among segments of this heterogenous population.

Handoffs in Radiology: Minimizing Communication Errors and Improving Care Transitions

Handoffs are essential to achieving safe care transitions. In radiology practice, frequent transitions of care responsibility among clinicians, radiologists, and patients occur between moments of care such as determining protocol, imaging, interpreting, and consulting. Continuity of care is maintained across these transitions with handoffs, which are the process of communicating patient information and transferring decision-making responsibility. As a leading cause of medical error, handoffs are a major communication challenge that is exceedingly common in both diagnostic and interventional radiology practice. The frequency of handoffs in radiology underscores the importance of using evidence-based strategies to improve patient safety in the radiology department. In this article, reliability science principles and handoff improvement tools are adapted to provide radiology-focused strategies at individual, team, and organizational levels with the goal of minimizing handoff errors and improving care transitions.