Optimizing radiologist productivity and efficiency: Work smarter, not harder

Optimizing radiology remote reading: leveraging technology to improve efficiency

Remote work has been increasingly utilized in the profession of radiology over recent years. Setting up your individual workstation offers an opportunity to tailor it to suit your preferences without the restriction of a universal setup to accommodate multiple users. Important considerations when setting up a home workstation include selecting the optimal work location, choosing the proper desk and chair, and configuring an ideal computer monitor layout. The use of peripheral devices, such as programmable mice and hands-free dictation tools can improve efficiency and reduce repetitive strain injuries. This article also explores the use of smart home devices and programmable scripts using AutoHotKey to further streamline workflow and maximize the benefits of a remote workstation.

Streamlining Radiology Workflows Through the Development and Deployment of Automated Microservices

Microservices are a software development approach where an application is structured as a collection of loosely coupled, independently deployable services, each focusing on executing a specific purpose. The development of microservices could have a significant impact on radiology workflows, allowing routine tasks to be automated and improving the efficiency and accuracy of radiologic tasks. This technical report describes the development of several microservices that have been successfully deployed in a tertiary cancer center, resulting in substantial time savings for radiologists and other staff involved in radiology workflows. These microservices include the automatic generation of shift emails, notifying administrative staff and faculty about fellows on rotation, notifying referring physicians about outside examinations, and populating report templates with information from PACS and RIS. The report outlines the common thought process behind developing these microservices, including identifying a problem, connecting various APIs, collecting data in a database, writing a prototype and deploying it, gathering feedback and refining the service, putting it in production, and identifying staff who are in charge of maintaining the service. The report concludes by discussing the benefits and challenges of microservices in radiology workflows, highlighting the importance of multidisciplinary collaboration, interoperability, security, and privacy.

Quick Guide: Programming a Gaming Mouse for PACS to Optimize Radiology Workflow

The increasing demand for diagnostic imaging has added to the radiologists' workload, highlighting the shortcomings of conventional computer mice. Radiologists grapple with inefficiencies from frequent mouse clicks and keyboard shortcuts required for various PACS functions. These inefficiencies contribute to cognitive strain, errors, and repetitive strain injuries. High-performance gaming mice, known for their precision in the gaming world, offer multiple custom buttons and superior tracking. These features can streamline radiology tasks. Utilizing a gaming mouse tailored for radiology tasks can substantially enhance efficiency. Our guide offers a step-by-step approach to harnessing the gaming mouse's capabilities for radiology tasks, ensuring radiologists can enhance their workflow and minimize injury risks. Although the guide uses a Logitech gaming mouse for demonstration, it is designed to be intuitive, helping users adapt to their unique needs across different modalities, subspecialties, and various radiology viewer software. Importantly, its fundamental concepts are transferrable to other mouse brands or models with similar customization software.

Pearls and Pitfalls of Interpretation of Automated Breast US

Dense breast tissue is an independent risk factor for breast cancer and reduces the sensitivity of mammography. Patients with dense breast tissue are more likely to present with interval cancers and higher-stage disease. Successful breast cancer screening outcomes rely on detection of early-stage breast cancers; therefore, several supplemental screening modalities have been developed to improve cancer detection in dense breast tissue. US is the most widely used supplemental screening modality worldwide and has been proven to demonstrate additional mammographically occult cancers that are predominantly invasive and node negative. According to the American College of Radiology, intermediate-risk women with dense breast tissue may benefit from adjunctive screening US due to the limitations of mammography. Several studies have demonstrated handheld US (HHUS) and automated breast US (AUS) to be comparable in the screening setting. The advantages of AUS over HHUS include lack of operator dependence and a formal training requirement, image reproducibility, and ability for temporal comparison. However, AUS exhibits unique features that can result in high false-positive rates and long interpretation times for new users. Familiarity with the common appearance of benign mammographic findings and artifacts, technical challenges, and unique AUS features is essential for fast, efficient, and accurate interpretation. The goals of this article are to (a) examine the role of AUS as a supplemental screening modality and (b) review the pearls and pitfalls of AUS interpretation. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

New Trends and Advances in MRI and PET Hybrid Imaging in Diagnostics

Imaging holds an irreplaceable role in routine clinical practice [...].

Incorporation of Macropads Into Abdominal Imaging to Improve Radiologist Productivity and Efficiency

Shifting demographics within the United States population will likely continue to increase workplace demands of radiologists over the next decade, highlighting the importance of improving radiologist productivity and efficiency. Macropads may serve to optimize radiologist workstations by assigning complex repetitive tasks to a single press of a button. Information technology (IT) restrictions in many radiology practices limit the use of macropads, which often require software installation for use. duckyPad is a unique macropad that circumvents the obstacle of workstation software installation. As a proof of concept, a duckyPad was modified for use in an academic abdominal imaging department. Specifically, commonly accessed online resources (ie, incidentaloma management tables) were programmed to open in a new Google Chrome window when the assigned key on the duckyPad was pressed. The device was replicated and successfully used in several reading rooms within the department. Despite reported benefits of device use, widespread implementation of duckyPad is limited by a number of factors, including required assembly. Future efforts of macropad incorporation into radiology workflows will focus on collaboration with IT departments to potentially facilitate use of more robust devices that can be managed centrally using proprietary software.

Exploring Radiologists' Burnout in the COVID-19 Era: A Narrative Review

Since its beginning in March 2020, the COVID-19 pandemic has claimed an exceptionally high number of victims and brought significant disruption to the personal and professional lives of millions of people worldwide. Among medical specialists, radiologists have found themselves at the forefront of the crisis due to the pivotal role of imaging in the diagnostic and interventional management of COVID-19 pneumonia and its complications. Because of the disruptive changes related to the COVID-19 outbreak, a proportion of radiologists have faced burnout to several degrees, resulting in detrimental effects on their working activities and overall wellbeing. This paper aims to provide an overview of the literature exploring the issue of radiologists' burnout in the COVID-19 era.

Improving diagnostic performance of rib fractures for the night shift in radiology department using a computer-aided diagnosis system based on deep learning: A clinical retrospective study

OBJECTIVE

To investigate the application value of a computer-aided diagnosis (CAD) system based on deep learning (DL) of rib fractures for night shifts in radiology department.

METHODS

Chest computed tomography (CT) images and structured reports were retrospectively selected from the picture archiving and communication system (PACS) for 2,332 blunt chest trauma patients. In all CT imaging examinations, two on-duty radiologists (radiologists I and II) completed reports using three different reading patterns namely, P1 = independent reading during the day shift; P2 = independent reading during the night shift; and P3 = reading with the aid of a CAD system as the concurrent reader during the night shift. The locations and types of rib fractures were documented for each reading. In this study, the reference standard for rib fractures was established by an expert group. Sensitivity and false positives per scan (FPS) were counted and compared among P1, P2, and P3.

RESULTS

The reference standard verified 6,443 rib fractures in the 2,332 patients. The sensitivity of both radiologists decreased significantly in P2 compared to that in P1 (both p <  0.017). The sensitivities of both radiologists showed no statistical difference between P3 and P1 (both p >  0.017). Radiologist I's FPS increased significantly in P2 compared to P1 (p <  0.017). The FPS of radiologist I showed no statistically significant difference between P3 and P1 (p >  0.017). The FPS of Radiologist II showed no statistical difference among all three reading patterns (p >  0.05).

CONCLUSIONS

DL-based CAD systems can be integrated into the workflow of radiology departments during the night shift to improve the diagnostic performance of CT rib fractures.