Faculty Bibliography

CT technologists, like radiologists, are at risk of increased stress and burnout due to ever increasing clinical and workload demands. To mitigate these issues, radiology facilities need to be prepared to actively address and resolve issues that impact the technologist satisfaction. At our institution, a Process Improvement Committee was formed to identify and alleviate workplace stressors faced by CT technologists. As a result of the initiative, our CT department has evolved into a technologist-driven department in which experienced and effective technologists play a large role in fostering efficient and patient-centered care, while feeling empowered to function as leaders in their work environment. In this article, a senior CT technologist provides a first-hand account of the process changes from the technologist's perspective, focusing on strategies for establishing a supportive system that allows technologists to thrive in providing patient-centered care even in the busiest of clinical contexts.

OBJECTIVE. The purpose of this study is to assess the perceptions of radiologists and emergency medicine (EM) providers regarding the quality, value, and challenges associated with using outside imaging (i.e., images obtained at facilities other than their own institution). MATERIALS AND METHODS. We surveyed radiologists and EM providers at a large academic medical center regarding their perceptions of the availability and utility of outside imaging. RESULTS. Thirty-four of 101 radiologists (33.6%) and 38 of 197 EM providers (19.3%) responded. A total of 32.4% of radiologists and 55.3% of EM providers had confidence in the quality of images from outside community facilities; 20.6% and 44.7%, respectively, had confidence in the interpretations of radiologists from these outside facilities. Only 23.5% of radiologists and 5.3% of EM physicians were confident in their ability to efficiently access reports (for outside images, 47.1% and 5.3%). Very few radiologists and EM providers had accessed imaging reports from outside facilities through an available stand-alone portal. A total of 40.6% of radiologists thought that outside reports always or frequently reduced additional imaging recommendations (62.5% for outside images); 15.6% thought that reports changed interpretations of new examinations (37.5% for outside images); and 43.8% thought that reports increased confidence in interpretations of new examinations (75.0% for outside images). A total of 29.4% of EM providers thought that access to reports from outside facilities reduced repeat imaging (64.7% for outside images), 41.2% thought that they changed diagnostic or management plans (50.0% for outside images), and 50.0% thought they increased clinical confidence (67.6% for outside images). CONCLUSION. Radiologists and EM providers perceive high value in sharing images from outside facilities, despite quality concerns. Substantial challenges exist in accessing these images and reports from outside facilities, and providers are unlikely to do so using separate systems. However, even if information technology solutions for seamless image integration are adopted, providers' lack of confidence in outside studies may remain an important barrier.

OBJECTIVE:Guideline-concordant follow-up of incidental lung nodules (ILNs) is suboptimal. We aimed to improve communication and tracking for follow-up of these common incidental findings detected on imaging examinations. METHODS:We implemented a process improvement program for reporting and tracking ILNs at a large urban academic health care system. A multidisciplinary committee designed, tested, and implemented a multipart tracking system in the electronic health record (EHR) that included Fleischner Society management recommendations for each patient. Plan-do-study-act cycles addressed gaps in the follow-up of ILNs, broken into phases of developing and testing components of the conceived EHR toolkit. RESULTS:The program resulted in standardized text macros with discrete categories and recommendations for ILNs, with ability to track each case in a work list within the EHR. The macros incorporated evidence-based guidelines and also input of collaborating clinical referrers in the respective specialty. The ILN macro was used 3,964 times over the first 2 years, increasing from 104 to over 300 uses per month. Usage spread across all subspecialty divisions, with nonthoracic radiologists currently accounting for 80% (56 of 70) of the radiologists using the system and 31% (1,230 of 3,964) of all captured ILNs. When radiologists indicated ILNs as warranting telephone communication to provider offices, completion was documented in 100% of the cases captured in the EHR-embedded tracking report. CONCLUSION/CONCLUSIONS:An EHR-based system for managing incidental nodules enables case tracking with exact recommendations, provider communication, and completion of follow-up testing. Future efforts will target consistent radiologist use of the system and follow-up completion.

Magnetic resonance imaging (MRI) is a leading image modality for the assessment of musculoskeletal (MSK) injuries and disorders. A significant drawback, however, is the lengthy data acquisition. This issue has motivated the development of methods to improve the speed of MRI. The field of artificial intelligence (AI) for accelerated MRI, although in its infancy, has seen tremendous progress over the past 3 years. Promising approaches include deep learning methods for reconstructing undersampled MRI data and generating high-resolution from low-resolution data. Preliminary studies show the promise of the variational network, a state-of-the-art technique, to generalize to many different anatomical regions and achieve comparable diagnostic accuracy as conventional methods. This article discusses the state-of-the-art methods, considerations for clinical applicability, followed by future perspectives for the field.

A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.

OBJECTIVE:To augment the educational resources available to training programs and trainees in musculoskeletal (MSK) radiology by creating a comprehensive series of Web-based open-access core curriculum lectures. MATERIALS AND METHODS/METHODS:Speakers with recognized content and lecturing expertise in MSK radiology were invited to create digitally recorded lecture presentations across a series of 42 core curriculum topics in MSK imaging. Resultant presentation recordings, organized under curriculum subject headings, were archived as open-access video file recordings for online viewing on a dedicated Web page ( http://radiologycorelectures.org/msk/ ). Information regarding the online core curriculum lecture series was distributed to members of the International Skeletal Society, Society of Skeletal Radiology, Society of Chairs of Academic Radiology Departments, and the Association of Program Directors in Radiology. Web page and online lecture utilization data were collected using Google Analytics (Alphabet, Mountain View, CA, USA). RESULTS:Forty-two lectures, by 38 speakers, were recorded, edited and hosted online. Lectures spanned ACGME curriculum categories of musculoskeletal trauma, arthritis, metabolic diseases, marrow, infection, tumors, imaging of internal derangement of joints, congenital disorders, and orthopedic imaging. Online access to the core curriculum lectures was opened on March 4, 2018. As of January 20, 2019, the core curriculum lectures have had 77,573 page views from 34,977 sessions. CONCLUSIONS:To date, the MSK core curriculum lecture series lectures have been widely accessed and viewed. It is envisioned that the initial success of the project will serve to promote ongoing content renewal and expansion to the lecture materials over time.

INTRODUCTION/BACKGROUND:Communication and physician burn out are major issues within Radiology. This study is designed to determine the utilization and cost benefit of a hybrid computer/human communication tool to aid in relay of clinically important imaging findings. MATERIAL AND METHODS/METHODS:Analysis of the total number of tickets, (requests for assistance) placed, the type of ticket and the turn-around time was performed. Cost analysis of a hybrid computer/human communication tool over a one-year period was based on human costs as a multiple of the time to close the ticket. Additionally, we surveyed a cohort of radiologists to determine their use of and satisfaction with this system. RESULTS:14,911 tickets were placed in the 6-month period, of which 11,401 (76.4%) were requests to "Get the Referring clinician on the phone." The mean time to resolution (TTR) of these tickets was 35.3 (±17.4) minutes. Ninety percent (72/80) of radiologists reported being able to interpret a new imaging study instead of waiting to communicate results for the earlier study, compared to 50% previously. 87.5% of radiologists reported being able to read more cases after this system was introduced. The cost analysis showed a cost savings of up to $101.12 per ticket based on the length of time that the ticket took to close and the total number of placed tickets. CONCLUSIONS:A computer/human communication tool can be translated to significant time savings and potentially increasing productivity of radiologists. Additionally, the system may have a cost savings by freeing the radiologist from tracking down referring clinicians prior to communicating findings.

PURPOSE/OBJECTIVE:To determine the utility of an automated radiology-pathology feedback tool. METHODS:We previously developed a tool that automatically provides radiologists with pathology results related to imaging examinations they interpreted. The tool also allows radiologists to mark the results as concordant or discordant. Five abdominal radiologists prospectively scored their own discordant results related to their previously interpreted abdominal ultrasound, CT, and MR interpretations between August 2017 and June 2018. Radiologists recorded whether they would have followed up on the case if there was no automated alert, reason for the discordance, whether the result required further action, prompted imaging rereview, influenced future interpretations, enhanced teaching files, or inspired a research idea. RESULTS:There were 234 total discordances (range 30-66 per radiologist), and 70.5% (165 of 234) of discordances would not have been manually followed up in the absence of the automated tool. Reasons for discordances included missed findings (10.7%; 25 of 234), misinterpreted findings (29.1%; 68 of 234), possible biopsy sampling error (13.3%; 31 of 234), and limitations of imaging techniques (32.1%; 75/234). In addition, 4.7% (11 of 234) required further radiologist action, including report addenda or discussion with referrer or pathologist, and 93.2% (218 of 234) prompted radiologists to rereview the images. Radiologists reported that they learned from 88% (206 of 234) of discordances, 38.6% (90 of 233) of discordances probably or definitely influenced future interpretations, 55.6% (130 of 234) of discordances prompted the radiologist to add the case to his or her teaching files, and 13.7% (32 of 233) inspired a research idea. CONCLUSION/CONCLUSIONS:Automated pathology feedback provides a valuable opportunity for radiologists across experience levels to learn, increase their skill, and improve patient care.

OBJECTIVE. The objective of this article is to show how artificial intelligence (AI) has impacted different components of the imaging value chain thus far as well as to describe its potential future uses. CONCLUSION. The use of AI has the potential to greatly enhance every component of the imaging value chain. From assessing the appropriateness of imaging orders to helping predict patients at risk for fracture, AI can increase the value that musculoskeletal imagers provide to their patients and to referring clinicians by improving image quality, patient centricity, imaging efficiency, and diagnostic accuracy.