Adapting to a new normal? 5 key operational principles for a radiology service facing the COVID-19 pandemic

Chen, Robert Chun; Tan, Thuan Tong; Chan, Lai Peng

doi:10.1007/s00330-020-06862-1

Cited by 27 publications

(26 citation statements)

References 8 publications

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“…One of our initial steps was to segregate our radiographer workforce into multiple independently functioning subunits, so we would be able to continue general radiography services in the event of a quarantine situation to any one team. 9 The different teams are segregated based on clinical location. The emergency and inpatient locations are considered high-risk working areas where COVID-19 patients are more likely to aggregate, whereas outpatient areas are deemed lower risk.…”

Section: Reorganisation Of Radiography Workforce/safe Distancingmentioning

confidence: 99%

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How to safely and sustainably reorganise a large general radiography service facing the COVID-19 pandemic

Sim

Chen

et al. 2020

Radiography

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Objectives: This paper aims to share our experience in reorganising our general radiography service during the coronavirus disease (COVID-19) pandemic from the viewpoint of a large tertiary referral medical centre. Key findings: Reorganization of the radiography workforce, patient segregation, and modification of routine radiographic practices are key measures to help radiographic services deal with the COVID-19 pandemic. Specific emphasis on deploying more mobile radiographic units, segregating equipment, developing consistent image acquisition workflows, and strict adherence to infection control protocols are paramount to minimize the possibility of in-hospital transmission and ensure a safe environment for both patients and staff. Streamlining communication channels between leadership and ground staff allows quick dissemination of information to ultimately facilitate safe provision of services. Conclusion: COVID-19 has drastically altered the way general radiography teams provide services. The institution of several key measures will allow hospitals to safely and sustainably provide radiographic services. To date, there have been zero incidences of radiographer healthcare worker transmission within our institution during the course of work. Implication for practice: Radiographers are facing the challenge of providing high-quality services while simultaneously minimizing pathogen exposure to staff and patients. Our experience may lend support to other radiographic services responding to the COVID-19 outbreak and serve as a blueprint for future infectious disease outbreak contingency plans.

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Section: Reorganisation Of Radiography Workforce/safe Distancingmentioning

confidence: 99%

“…The radiologist on duty will be informed to screen the patient in the isolation room. 9 If necessary, the radiologist will consult the Infectious Disease (ID) physician. A chest radiograph can be requested on-site and performed immediately in the adjacent radiography room (Fig.…”

Section: Outpatient Imaging Centrementioning

confidence: 99%

How to safely and sustainably reorganise a large general radiography service facing the COVID-19 pandemic

Sim

Chen

et al. 2020

Radiography

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“…Since the outbreak of corona virus disease 19 (COVID-19) and its declaration by the WHO as a pandemic on March 11, 2020, various measures have been implemented worldwide in order to achieve containment of the disease [ [1] , [2] , [3] ]. One key factor has been proven to be the rapid and accurate identification of infected patients and their isolation among with social distancing for the general population [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography

Javor

Kaplan

et al. 2020

European Journal of Radiology

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Introduction Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease. Methods A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive (>10) and low negative (<0.01) likelihood ratios to stratify the risk of COVID-19 being present were identified and validated. Results The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4% and 93.3% and did not differ from both radiologists (p > 0.05). At the rule-out threshold, sensitivity (100%) and specificity (60%) differed significantly from the radiologists (p < 0.05). Likelihood ratios and a Fagan nomogram provide prevalence independent test performance estimates. Conclusion Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present.

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“…Prior publications have elaborated on safe distancing of patients and accompanying persons in radiology departments through reconfiguring of radiology departments, screening questionnaires, triaging scan urgency in symptomatic patients, and reducing the number of accompanying persons 3,4 . One new area we are exploring to further facilitate physical distancing between patients is a "Precision Appointment" (PrApp) model (Table 1).…”

Section: Patients and Visitorsmentioning

confidence: 99%

Touch Me Not: Safe Distancing in Radiology During Coronavirus Disease 2019 (COVID-19)

Chen

Cheng

Lim

et al. 2020

Journal of the American College of Radiology

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All of the above authors attests to:1. Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work 2. Drafting the work or revising it critically for important intellectual content 3. Final approval of the version to be published 4. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Adapting to a new normal? 5 key operational principles for a radiology service facing the COVID-19 pandemic

Cited by 27 publications

References 8 publications

How to safely and sustainably reorganise a large general radiography service facing the COVID-19 pandemic

How to safely and sustainably reorganise a large general radiography service facing the COVID-19 pandemic

Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography

Touch Me Not: Safe Distancing in Radiology During Coronavirus Disease 2019 (COVID-19)

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