Image Exchange in the Middle East: a Survey.

Developing an enterprise approach to imaging technology rather than a radiology focus has recently increased. The communicator needs to be aware of this shift.The Middle East countries participated in the survey have confirmed the following major benefits of Medical Image Exchange: ✔ Fast access to both image and report ✔ Enable tele-services for second opinion, consulting and reporting ✔ Improve patient journey, workflow and diagnosis ✔ Allowed more patient engagement to be in place The Middle East countries that participated in this survey have agreed on the following shared challenges regarding Medical Imaging Exchange: ✔ Lack of enterprise imaging governance at the early stage of implementation. It will organize the who, when, and how. In addition, any fees and or payment involved for physicians ✔ Infrastructure availability to handle such large volume of data. Growing from mega-byte to petabyte per year is challenge for infrastructure. Cloud against On Premises-Installation implementation model ✔ Interoperability and integration to connect multi specialties from different systems. In addition, how far existing systems are ready for that. A standard-based framework is mature for image exchange, but what follows for other domains? There is a need to move beyond radiology images so as to include images from pathology, ophthalmology, and dermatology There are other countries in the region requiring guidance, support, and funding to move forward from the compact disc into internet-based interoperable image exchange. This should be considered part of the World Health Organization and the United Nation development to the region in the healthcare sector.

The Effect of Mask Wearing on the Accuracy of Radiology Reports in an Academic Hospital Setting.

PURPOSE: In response to the pandemic, some public health agencies recommend the wearing of surgical masks in indoor spaces including radiology common reporting rooms. We aim to demonstrate whether mask wearing may lead to increased errors incidence in radiology reports. MATERIALS AND METHODS: Our prospective studywas conveyed in 2 parts. Firstly, the participants were surveyed if they believed that mask affected dictation. Then participants performed a dictation: they read artificial radiology reports using a commercial voice recognition (VR) system. They performed this task 5 times, each time donning a different mask in random order: a surgical mask, surgical visor, N-95, combination of 2 surgical masks and no mask. Error rates were compared with the Friedman test followed by pairwise Wilcoxon with bootstrapping. Multivariate Poisson regression was performed to test for interaction effects between potential predictors. RESULTS: 52 members of an academic radiology department participatedin the study (January - March 2021) . 65.4% of survey participants did not think or were not sure whether mask wearing could affect dictation process. Treating the no-mask condition as baseline, our study found that mean error rates significantly increased up to 2 times the baseline rate when a surgical mask, surgical visor, N-95 or a combination of 2 masks was donned (p < 0.0001). No significant differences in error rates were found between the different mask types (p > 0.05). Error rates were higher for participants with shorter VR training time (p < 0.0001) or who were non-native English speakers (p < 0.0001). There were no interaction effects between mask type, VR training time or English nativity, suggesting these variables to be independent predictors for error rate. Academic rank did not significantly affect the error rate. CONCLUSION: radiologists underestimate the influence of masks on dictation accuracy. mask wearing may lead to significant increase in dictational errors.

Radiology Dictation Errors with COVID-19 Protective Equipment: Does Wearing a Surgical Mask Increase the Dictation Error Rate?

Our aim was to determine the effect of wearing a surgical mask on the number and type of dictation errors in unedited radiology reports. IRB review was waived for this prospective matched-pairs study in which no patient data was used. Model radiology reports (n = 40) simulated those typical for an academic medical center. Six randomized radiologists dictated using speech-recognition software with and without a surgical mask. Dictations were compared to model reports and errors were classified according to type and severity. A statistical model was used to demonstrate that error rates for all types of errors were greater when masks are worn compared to when they are not (unmasked: 21.7 ± 4.9 errors per 1000 words, masked: 27.1 ± 2.2 errors per 1000 words; adjusted p < 0.0001). A sensitivity analysis was performed, excluding a reader with a large number of errors. The sensitivity analysis found a similar difference in error rates for all types of errors, although significance was attenuated (unmasked: 16.9 ± 1.9 errors per 1000 words, masked: 20.1 ± 2.2 errors per 1000 words; adjusted p = 0.054). We conclude that wearing a mask results in a near-significant increase in the rate of dictation errors in unedited radiology reports created with speech-recognition, although this difference may be accentuated in some groups of radiologists. Additionally, we find that most errors are minor single incorrect words and are unlikely to result in a medically relevant misunderstanding.

Diagnostic Performance of COVID-19 Reporting and Data System Classification Across Residents and Radiologists: A Retrospective Study.

OBJECTIVE: The aim of the study was to evaluate the interobserver agreement and diagnostic accuracy of COVID-19 Reporting and Data System (CO-RADS), in patients suspected COVID-19 pneumonia. METHODS: Two hundred nine nonenhanced chest computed tomography images of patients with clinically suspected COVID-19 pneumonia were included. The images were evaluated by 2 groups of observers, consisting of 2 residents-radiologists, using CO-RADS. Reverse transcriptase-polymerase chain reaction (PCR) was used as a reference standard for diagnosis in this study. Sensitivity, specificity, area under receiver operating characteristic curve (AUC), and intraobserver/interobserver agreement were calculated. RESULTS: COVID-19 Reporting and Data System was able to distinguish patients with positive PCR results from those with negative PCR results with AUC of 0.796 in the group of residents and AUC of 0.810 in the group of radiologists. There was moderate interobserver agreement between residents and radiologist with κ values of 0.54 and 0.57. CONCLUSIONS: The diagnostic performance of CO-RADS for predicting COVID-19 pneumonia showed moderate interobserver agreement between residents and radiologists.

Chest CT in COVID-19 at the ED: Validation of the COVID-19 Reporting and Data System (CO-RADS) and CT Severity Score: A Prospective, Multicenter, Observational Study.

BACKGROUND: CT is thought to play a key role in coronavirus disease 2019 (COVID-19) diagnostic workup. The possibility of comparing data across different settings depends on the systematic and reproducible manner in which the scans are analyzed and reported. The COVID-19 Reporting and Data System (CO-RADS) and the corresponding CT severity score (CTSS) introduced by the Radiological Society of the Netherlands (NVvR) attempt to do so. However, this system has not been externally validated. RESEARCH QUESTION: We aimed to prospectively validate the CO-RADS as a COVID-19 diagnostic tool at the ED and to evaluate whether the CTSS is associated with prognosis. STUDY DESIGN AND METHODS: We conducted a prospective, observational study in two tertiary centers in The Netherlands, between March 19 and May 28, 2020. We consecutively included 741 adult patients at the ED with suspected COVID-19, who received a chest CT and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR (PCR). Diagnostic accuracy measures were calculated for CO-RADS, using PCR as reference. Logistic regression was performed for CTSS in relation to hospital admission, ICU admission, and 30-day mortality. RESULTS: Seven hundred forty-one patients were included. We found an area under the curve (AUC) of 0.91 (CI, 0.89-0.94) for CO-RADS using PCR as reference. The optimal CO-RADS cutoff was 4, with a sensitivity of 89.4% (CI, 84.7-93.0) and specificity of 87.2% (CI, 83.9-89.9). We found a significant association between CTSS and hospital admission, ICU admission, and 30-day mortality; adjusted ORs per point increase in CTSS were 1.19 (CI, 1.09-1.28), 1.23 (1.15-1.32), 1.14 (1.07-1.22), respectively. Intraclass correlation coefficients for CO-RADS and CTSS were 0.94 (0.91-0.96) and 0.82 (0.70-0.90). INTERPRETATION: Our findings support the use of CO-RADS and CTSS in triage, diagnosis, and management decisions for patients presenting with possible COVID-19 at the ED.

Optimizing Radiology Reading Room Design: The Eudaimonia Radiology Machine.

Physical and mental stressors on radiologists can result in burnout. Although current efforts seek to target the issues of burnout and stress for radiologists, the impact of their physical workspace is often overlooked. By combining evidence-based design, human factors, and the architectural concept of the Eudaimonia Machine, we have developed a redesign of the radiology reading room that aims to create an optimal workspace for the radiologist. Informed by classical principles of well-being and contemporary work theory, Eudaimonia integrates concerns for individual wellness and efficiency to create an environment that fosters productivity. This layout arranges a work environment into purposeful spaces, each hosting tasks of varying degrees of intensity. The improved design addresses the radiologist's work requirements while also alleviating cognitive and physical stress, fatigue, and burnout. This new layout organizes the reading room into separate areas, each with a distinct purpose intended to support the range of radiologists' work, from consultation with other health care providers to reading images without interruption. The scientific principles that undergird evidence-based design and human factors considerations ensure that the Eudaimonia Radiology Machine is best suited to support the work of the radiologists and the entire radiology department.

Radiological Society of North America (RSNA) Expert Consensus Statement Related to Chest CT Findings in COVID-19 Versus CO-RADS: Comparison of Reporting System Performance Among Chest Radiologists and End-User Preference.

PURPOSE: The RSNA expert consensus statement and CO-RADS reporting system assist radiologists in describing lung imaging findings in a standardized manner in patients under investigation for COVID-19 pneumonia and provide clarity in communication with other healthcare providers. We aim to compare diagnostic performance and inter-/intra-observer among chest radiologists in the interpretation of RSNA and CO-RADS reporting systems and assess clinician preference. METHODS: Chest CT scans of 279 patients with suspected COVID-19 who underwent RT-PCR testing were retrospectively and independently examined by 3 chest radiologists who assigned interpretation according to the RSNA and CO-RADS reporting systems. Inter-/intra-observer analysis was performed. Diagnostic accuracy of both reporting systems was calculated. 60 clinicians participated in a survey to assess end-user preference of the reporting systems. RESULTS: Both systems demonstrated almost perfect inter-observer agreement (Fleiss kappa 0.871, P < 0.0001 for RSNA; 0.876, P < 0.0001 for CO-RADS impressions). Intra-observer agreement between the 2 scoring systems using the equivalent categories was almost perfect (Fleiss kappa 0.90-0.92, P < 0.001). Positive predictive values were high, 0.798-0.818 for RSNA and 0.891-0.903 CO-RADS. Negative predictive value were similar, 0.573-0.585 for RSNA and 0.573-0.58 for CO-RADS. Specificity differed between the 2 systems, 68-73% for CO-RADS and 52-58% for RSNA with superior specificity of CO-RADS. Of 60 survey participants, the majority preferred the RSNA reporting system rather than CO-RADS for all options provided (66.7-76.7%; P < 0.05). CONCLUSIONS: RSNA and CO-RADS reporting systems are consistent and reproducible with near perfect inter-/intra-observer agreement and excellent positive predictive value. End-users preferred the reporting language in the RSNA system.

CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation.

Background A categorical CT assessment scheme for suspicion of pulmonary involvement of coronavirus disease 2019 (COVID-19 provides a basis for gathering scientific evidence and improved communication with referring physicians. Purpose To introduce the COVID-19 Reporting and Data System (CO-RADS) for use in the standardized assessment of pulmonary involvement of COVID-19 on unenhanced chest CT images and to report its initial interobserver agreement and performance. Materials and Methods The Dutch Radiological Society developed CO-RADS based on other efforts for standardization, such as the Lung Imaging Reporting and Data System or Breast Imaging Reporting and Data System. CO-RADS assesses the suspicion for pulmonary involvement of COVID-19 on a scale from 1 (very low) to 5 (very high). The system is meant to be used in patients with moderate to severe symptoms of COVID-19. The system was evaluated by using 105 chest CT scans of patients admitted to the hospital with clinical suspicion of COVID-19 and in whom reverse transcription-polymerase chain reaction (RT-PCR) was performed (mean, 62 years ± 16 [standard deviation]; 61 men, 53 with positive RT-PCR results). Eight observers used CO-RADS to assess the scans. Fleiss κ value was calculated, and scores of individual observers were compared with the median of the remaining seven observers. The resulting area under the receiver operating characteristics curve (AUC) was compared with results from RT-PCR and clinical diagnosis of COVID-19. Results There was absolute agreement among observers in 573 (68.2%) of 840 observations. Fleiss κ value was 0.47 (95% confidence interval [CI]: 0.45, 0.47), with the highest κ value for CO-RADS categories 1 (0.58, 95% CI: 0.54, 0.62) and 5 (0.68, 95% CI: 0.65, 0.72). The average AUC was 0.91 (95% CI: 0.85, 0.97) for predicting RT-PCR outcome and 0.95 (95% CI: 0.91, 0.99) for clinical diagnosis. The false-negative rate for CO-RADS 1 was nine of 161 cases (5.6%; 95% CI: 1.0%, 10%), and the false-positive rate for CO-RADS category 5 was one of 286 (0.3%; 95% CI: 0%, 1.0%). Conclusion The coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) is a categorical assessment scheme for pulmonary involvement of COVID-19 at unenhanced chest CT that performs very well in predicting COVID-19 in patients with moderate to severe symptoms and has substantial interobserver agreement, especially for categories 1 and 5. © RSNA, 2020 Online supplemental material is available for this article.

Response to the COVID-19 Pandemic: Practical Guide to Rapidly Deploying Home Workstations to Guarantee Radiology Services During Quarantine, Social Distancing, and Stay Home Orders.

OBJECTIVE. The purpose of this article is to share an experience in the rapid deployment of home workstations that illustrates a creative solution that transcended typical administrative barriers. CONCLUSION. In response to the global coronavirus disease (COVID-19) pandemic, radiology departments need to rapidly deploy home PACS workstations to facilitate physical distancing and to guarantee radiologic expertise despite possible home quarantining or stay home, work safe orders.

Remote reporting in the COVID-19 era: from pilot study to practice.

AIM: To assess the benefits and challenges of remote reporting using an intra-departmental teleradiology system. MATERIALS AND METHODS: A pilot of an in-hospital Trust radiologist reporting on in-hospital Trust patients via a remote login was undertaken. Reporting output, training impact, and quality improvement were measured. RESULTS: Reporting output increased by 140%. Trainee satisfaction was high in a qualitative survey, particularly for out-of-hours support and teaching. Clinicians found the service to be similar to the same service provided by a locally based radiologist. CONCLUSION: In the COVID-19 era, remote working has developed rapidly. This study shows that radiology departments can provide remote reporting that is equal in standard to reporting from within the hospital, and in addition, that there are advantages to output and training.