Burnout in Radiology

Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting

Background Radiology practices have a high volume of unremarkable chest radiographs and artificial intelligence (AI) could possibly improve workflow by providing an automatic report. Purpose To estimate the proportion of unremarkable chest radiographs, where AI can correctly exclude pathology (ie, specificity) without increasing diagnostic errors. Materials and Methods In this retrospective study, consecutive chest radiographs in unique adult patients (≥18 years of age) were obtained January 1-12, 2020, at four Danish hospitals. Exclusion criteria included insufficient radiology reports or AI output error. Two thoracic radiologists, who were blinded to AI output, labeled chest radiographs as "remarkable" or "unremarkable" based on predefined unremarkable findings (reference standard). Radiology reports were classified similarly. A commercial AI tool was adapted to output a chest radiograph "remarkableness" probability, which was used to calculate specificity at different AI sensitivities. Chest radiographs with missed findings by AI and/or the radiology report were graded by one thoracic radiologist as critical, clinically significant, or clinically insignificant. Paired proportions were compared using the McNemar test. Results A total of 1961 patients were included (median age, 72 years [IQR, 58-81 years]; 993 female), with one chest radiograph per patient. The reference standard labeled 1231 of 1961 chest radiographs (62.8%) as remarkable and 730 of 1961 (37.2%) as unremarkable. At 99.9%, 99.0%, and 98.0% sensitivity, the AI had a specificity of 24.5% (179 of 730 radiographs [95% CI: 21, 28]), 47.1% (344 of 730 radiographs [95% CI: 43, 51]), and 52.7% (385 of 730 radiographs [95% CI: 49, 56]), respectively. With the AI fixed to have a similar sensitivity as radiology reports (87.2%), the missed findings of AI and reports had 2.2% (27 of 1231 radiographs) and 1.1% (14 of 1231 radiographs) classified as critical (P = .01), 4.1% (51 of 1231 radiographs) and 3.6% (44 of 1231 radiographs) classified as clinically significant (P = .46), and 6.5% (80 of 1231) and 8.1% (100 of 1231) classified as clinically insignificant (P = .11), respectively. At sensitivities greater than or equal to 95.4%, the AI tool exhibited less than or equal to 1.1% critical misses. Conclusion A commercial AI tool used off-label could correctly exclude pathology in 24.5%-52.7% of all unremarkable chest radiographs at greater than or equal to 98% sensitivity. The AI had equal or lower rates of critical misses than radiology reports at sensitivities greater than or equal to 95.4%. These results should be confirmed in a prospective study. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yoon and Hwang in this issue.

A National Survey Evaluating Burnout and Wellness During Radiology Residency

RATIONALE AND OBJECTIVES

Burnout is a serious problem during medical residency and can contribute to poorer resident and patient health. A thorough understanding of factors associated with burnout can provide insight into supporting resident well-being. The purpose of this study is to assess the prevalence of burnout and ascertain its associated factors among radiology residents in the U.S.

MATERIALS AND METHODS

This cross-sectional study involved sending an anonymous survey to radiology program directors, coordinators, and residents across the U.S. Data regarding demographics, burnout levels, and burnout-associated factors were collected in the month of August 2023. Multivariable linear regression models evaluated the association of demographic and burnout-associated variables with burnout scores in the dimensions of Emotional Exhaustion, Depersonalization, and Personal Accomplishment. Chi-square analyses with Bonferroni correction and Kruskal-Wallis analyses were used to assess associations between program types and burnout as well as between program type and program effectiveness in managing burnout. Resident suggestions on addressing burnout were qualitatively assessed.

RESULTS

147 radiology residents responded to the survey. Emotional Exhaustion was positively associated with seeking social support (p = .03) and negatively associated with perceived program effectiveness in addressing burnout (p < .001). Respondents who identified as male experienced greater Depersonalization (p = .02). Increased frequency of physical activity was associated with higher Personal Accomplishment scores (p = .04). The most common resident suggestions related to Work Burden, Program Support, and Protected Wellness Time.

CONCLUSION

Radiology programs should consider designing interventions addressing burnout, such as enhancing avenues for feedback and tailoring resident training based on individual preferences for remote work. Understanding the unique challenges faced by radiology residents is essential to tackle burnout and improve wellness.

Impact of Interruptions During Screening Mammography on Physician Well-Being and Patient Care

OBJECTIVE

To assess the impact of interruptions on radiologists' efficiency, accuracy, and job satisfaction in interpreting screening mammograms.

METHODS

This institutional review board-approved retrospective reader study recruited nine breast radiologists from a single academic institution [name withheld] to interpret 150 screening mammograms performed between December 1, 2008, and December 31, 2015 under two different reading conditions, as follows: (1) uninterrupted batch reading and (2) interrupted reading. The 150 cases consisted of 125 normal mammograms and 25 mammograms with subtle breast cancers. Cases were divided into two groups of 75 cases each (cohort 1 and cohort 2), with a comparable distribution of cancer cases. Four rounds of 75 cases each were conducted with a 6-week washout period between rounds 2 and 3. After completing each interpretation session, readers completed a seven-question survey, assessing perceptions of mental and physical effort, level of frustration, and performance satisfaction. Clinical performance metrics (reading time, recall rate, sensitivity, specificity, accuracy, and positive predictive value 1) were calculated.

RESULTS

Recall rates were significantly (P = .04) higher during interrupted reading sessions (35.4%) than they were during uninterrupted batch reading sessions (31.4%). Accuracy was significantly (P = .049) worse in the interrupted reading sessions (69.5%), compared with uninterrupted sessions (73.6%). Differences in overall image interpretation times were not statistically significant (P = .065). Compared with uninterrupted batch reading sessions, readers during interrupted sessions reported feeling busier (P < .001), encountered higher levels of cognitive demand (P = .005), experienced elevated levels of physical fatigue (P = .004), and expressed lower levels of satisfaction with their performance (P = .041).

CONCLUSION

Interruptions during interpretation of screening mammography have deleterious effects on physician performance and their sense of well-being.

Imagine there is no paperwork… it's easy if you try

Artificial Intelligence (AI) applied to radiology is so vast that it provides applications ranging from becoming a complete replacement for radiologists (a potential threat) to an efficient paperwork-saving time assistant (an evident strength). Nowadays, there are AI applications developed to facilitate the diagnostic process of radiologists without directly influencing (or replacing) the proper diagnostic decision step. These tools may help to reduce administrative workload, in different scenarios ranging from assisting in scheduling, study prioritization, or report communication, to helping with patient follow-up, including recommending additional exams. These are just a few of the highly time-consuming tasks that radiologists have to deal with every day in their routine workflow. These tasks hinder the time that radiologists should spend evaluating images and caring for patients, which will have a direct and negative impact on the quality of reports and patient attention, increasing the delay and waiting list of studies pending to be performed and reported. These types of AI applications should help to partially face this worldwide shortage of radiologists.

Doctor's Orders-Why Radiologists Should Consider Adjusting Commercial Machine Learning Applications in Chest Radiography to Fit Their Specific Needs

This retrospective study evaluated a commercial deep learning (DL) software for chest radiographs and explored its performance in different scenarios. A total of 477 patients (284 male, 193 female, mean age 61.4 (44.7-78.1) years) were included. For the reference standard, two radiologists performed independent readings on seven diseases, thus reporting 226 findings in 167 patients. An autonomous DL reading was performed separately and evaluated against the gold standard regarding accuracy, sensitivity and specificity using ROC analysis. The overall average AUC was 0.84 (95%-CI 0.76-0.92) with an optimized DL sensitivity of 85% and specificity of 75.4%. The best results were seen in pleural effusion with an AUC of 0.92 (0.885-0.955) and sensitivity and specificity of each 86.4%. The data also showed a significant influence of sex, age, and comorbidity on the level of agreement between gold standard and DL reading. About 40% of cases could be ruled out correctly when screening for only one specific disease with a sensitivity above 95% in the exploratory analysis. For the combined reading of all abnormalities at once, only marginal workload reduction could be achieved due to insufficient specificity. DL applications like this one bear the prospect of autonomous comprehensive reporting on chest radiographs but for now require human supervision. Radiologists need to consider possible bias in certain patient groups, e.g., elderly and women. By adjusting their threshold values, commercial DL applications could already be deployed for a variety of tasks, e.g., ruling out certain conditions in screening scenarios and offering high potential for workload reduction.

Thriving In and Out of the Reading Room: A Faculty Development Workshop

RATIONALE AND OBJECTIVES

Given the immense time and energy radiologists dedicate to their profession, the experience at work should be a major contributor to a meaningful and fulfilling life. In pursuit of this vision, our department launched a novel faculty development workshop entitled "Thriving In and Out of the Reading Room: What They Didn't Teach Us in Training." We report on the design, implementation and initial outcomes of this faculty development workshop.

MATERIALS AND METHODS

The workshop drew upon positive psychology research and the PERMA model of well-being, which encompasses five key elements: Positive emotion, Engagement, Relationships, Meaning, and Achievement. These elements have been shown to enhance work satisfaction and foster resilience. Using interactive, small group exercises, the workshop provided strategies for incorporating PERMA elements into daily life. At the conclusion of each workshop, an anonymous voluntary electronic survey was distributed to participants.

RESULTS

The final version of the workshop was offered to 58 faculty over eight sessions between September 2022 and May 2023. Survey results indicate that participants found the workshop to be highly valuable and practical. They also found the workshop to promote camaraderie and peer learning. Developing the workshop internally allowed us to customize it to our faculty's unique experiences and engage a large number of participants.

CONCLUSION

The workshop shows promise in improving job satisfaction and addressing burnout among academic radiologists.

A Meta-analysis of Burnout in Radiology Trainees and Radiologists: Insights From the Maslach Burnout Inventory

RATIONALE AND OBJECTIVES

Burnout, stemming from chronic work stress, is a significant issue in the medical field, especially among radiologists. It leads to compromised patient care and reduced job satisfaction. Addressing burnout in radiology is essential for the well-being of radiologists and, in turn, for improving patient care. This study aimed to assess the prevalence and dimensions of burnout among radiology trainees (RTs) and practicing radiologists (PRs).

MATERIALS AND METHODS

A systematic review and meta-analysis were conducted in accordance with established guidelines. The search encompassed PubMed, Scopus, Web of Science, and Embase databases up to June 20, 2023. Eligible studies that assessed the rate of burnout syndrome and/or its subscales, including depersonalization (DP), emotional exhaustion (EE), and personal accomplishment (PA), among RTs and/or PRs using the Maslach Burnout Inventory (MBI), were included. Relevant data were extracted and analyzed using R and STATA.

RESULTS

Among the 22 included studies, the pooled rates of positive MBI subscales for RTs and PRs were as follows: 54.7% (95% confidence interval [CI]: 43.8%-65.1%, I2 = 95.2%) for DP, 57.2% (95% CI: 48.7%-65.4%, I2 = 92.9%) for EE, and 38.6% (95% CI: 27%-51.7%, I2 = 95.5%) for low PA. The pooled rate indicating the presence of at least one positive MBI subscale was 82.9% (95% CI: 79.2%-86.1%, I2 = 57.4%). For two or more positive MBI subscales, the pooled rate was 55.5% (95% CI: 49.7%-61.3%, I2 = 60.2%), and for three positive MBI subscales, it was 16.7% (95% CI: 11.7%-23.3%, I2 = 82.7%).

CONCLUSION

This study emphasizes a notable prevalence of burnout in the radiology specialty, with 8 of 10 individuals exhibiting positive results in at least one MBI subscale. This highlights the urgent need for interventions and support systems to protect the well-being of both trainees and practitioners and uphold the quality of patient care.