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      Update on establishing and managing an overnight emergency radiology division

      review-article

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          Abstract

          Emergency department (ED) radiology divisions that serve to provide overnight attending coverage have become an increasingly common feature of radiology departments. The purpose of this article is to review the common ED radiology coverage models, describe desirable traits of emergency radiologists, and discuss workflow in the ED radiology setting. ED radiologists may be trained as ED radiologists or may develop the necessary skills and adopt the subspecialty. Choosing radiologists with the correct traits such as being a “night owl” and remaining calm under pressure and implementing an acceptable work schedule such as shift length of 9–10 h and a “one week on, two weeks off” schedule contribute to sustainability of the position. Strategies to address the unique stressors and workflow challenges of overnight emergency radiology coverage are also presented. Workflow facilitators including trainees, PAs, radiology assistants, and clerks all have roles to play in managing high case volumes and in making sure that the service is well staffed. Usage of artificial intelligence software is the latest technique to streamline workflow by identifying cases which should be prioritized on a busy worklist. Implementing such strategies will maintain quality of care for patients regardless of time of day as well as sustainability and quality of life for overnight emergency radiologists.

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          Most cited references37

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          Burnout and satisfaction with work-life balance among US physicians relative to the general US population.

          Despite extensive data about physician burnout, to our knowledge, no national study has evaluated rates of burnout among US physicians, explored differences by specialty, or compared physicians with US workers in other fields. We conducted a national study of burnout in a large sample of US physicians from all specialty disciplines using the American Medical Association Physician Masterfile and surveyed a probability-based sample of the general US population for comparison. Burnout was measured using validated instruments. Satisfaction with work-life balance was explored. Of 27 276 physicians who received an invitation to participate, 7288 (26.7%) completed surveys. When assessed using the Maslach Burnout Inventory, 45.8% of physicians reported at least 1 symptom of burnout. Substantial differences in burnout were observed by specialty, with the highest rates among physicians at the front line of care access (family medicine, general internal medicine, and emergency medicine). Compared with a probability-based sample of 3442 working US adults, physicians were more likely to have symptoms of burnout (37.9% vs 27.8%) and to be dissatisfied with work-life balance (40.2% vs 23.2%) (P < .001 for both). Highest level of education completed also related to burnout in a pooled multivariate analysis adjusted for age, sex, relationship status, and hours worked per week. Compared with high school graduates, individuals with an MD or DO degree were at increased risk for burnout (odds ratio [OR], 1.36; P < .001), whereas individuals with a bachelor's degree (OR, 0.80; P = .048), master's degree (OR, 0.71; P = .01), or professional or doctoral degree other than an MD or DO degree (OR, 0.64; P = .04) were at lower risk for burnout. Burnout is more common among physicians than among other US workers. Physicians in specialties at the front line of care access seem to be at greatest risk.
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            Is Open Access

            Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning

            Significance Computed tomography (CT) of the head is the workhorse medical imaging modality used worldwide to diagnose neurologic emergencies. However, these gray scale images are limited by low signal-to-noise, poor contrast, and a high incidence of image artifacts. A unique challenge is to identify tiny subtle abnormalities in a large 3D volume with near-perfect sensitivity. We used a single-stage, end-to-end, fully convolutional neural network to achieve accuracy levels comparable to that of highly trained radiologists, including both identification and localization of abnormalities that are missed by radiologists.
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              Automated Segmentation of Tissues Using CT and MRI: A Systematic Review

              The automated segmentation of organs and tissues throughout the body using computed tomography (CT) and magnetic resonance imaging (MRI) has been rapidly increasing. Research into many medical conditions has benefited greatly from these approaches by allowing the development of more rapid and reproducible quantitative imaging markers. These markers have been used to help diagnose disease, determine prognosis, select patients for therapy, and follow responses to therapy. Because some of these tools are now transitioning from research environments to clinical practice, it is important for radiologists to become familiar with various methods used for automated segmentation. The Radiology Research Alliance of the Association of University Radiologists convened an Automated Segmentation Task Force to conduct a systematic review of the peer-reviewed literature on this topic. The systematic review presented here includes 408 studies and discusses various approaches to automated segmentation using CT and MRI for neurologic, thoracic, abdominal, musculoskeletal, and breast imaging applications. These insights should help prepare radiologists to better evaluate automated segmentation tools and apply them not only to research, but eventually to clinical practice.
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                Author and article information

                Contributors
                mscheinf@montefiore.org
                rjd253@njms.rutgers.edu
                Journal
                Emerg Radiol
                Emerg Radiol
                Emergency Radiology
                Springer International Publishing (Cham )
                1070-3004
                1438-1435
                21 April 2021
                : 1-9
                Affiliations
                [1 ]GRID grid.251993.5, ISNI 0000000121791997, Department of Radiology, Division of Emergency Radiology, Montefiore Medical Center, , Albert Einstein College of Medicine, ; 111 East 210 Street, Bronx, NY 10467 USA
                [2 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Department of Radiology, Division of Emergency Radiology, University Hospital, , Rutgers New Jersey Medical School, ; 185 South Orange Avenue, MSB F508B, Newark, NJ 07103 USA
                Author information
                http://orcid.org/0000-0001-8880-4318
                Article
                1935
                10.1007/s10140-021-01935-0
                8059109
                33881670
                b7aa80ba-3d80-4a6f-ae06-fdbc239fcf04
                © American Society of Emergency Radiology 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 21 February 2021
                : 13 April 2021
                Categories
                Review Article

                Emergency medicine & Trauma
                emergency radiology,ed radiology,overnight,staffing,burnout
                Emergency medicine & Trauma
                emergency radiology, ed radiology, overnight, staffing, burnout

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