Radiologists’ Variation of Time to Read Across Different Procedure Types

Forsberg, Daniel; Rosipko, Beverly; Sunshine, Jeffrey L.

doi:10.1007/s10278-016-9911-z

Cited by 25 publications

(21 citation statements)

References 17 publications

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“…Similarly, to other recently published surveys, the present study demonstrated an annual increase of 3.9% in the number of radiology reports and imaging examinations during the evaluation period in 2018 compared to 2017 (Table 1 ) [ 3 , 12 , 13 ]. Interestingly, there was an increase from 37,247 to 38,754 reports but a decrease in reports with a second signature from 16,632 to 15,967 reports (Table 1 ).…”

Section: Discussionsupporting

confidence: 89%

Subspecialized radiological reporting reduces radiology report turnaround time

et al. 2020

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Objectives The objective of this study was to compare the radiology report turnaround time (RTAT) between decentralized/modality-based and centralized/subspecialized radiological reporting at a multi-center radiology enterprise. Methods RTAT values for MRI, CT, and conventional radiography were compared between decentralized/modality-based (04 September 2017–22 December 2017) and centralized/subspecialized radiology (03 September 2018–21 December 2018) reporting grouped into three subspecializations (body radiology, musculoskeletal radiology, and neuroradiology) at eleven sites of a multi-center radiology enterprise. For the objective of this investigation, hospitals were defined as major and minor hospitals. The Mann-Whitney U test served for statistical analyses. Results Change of reporting system from decentralized/modality-based to centralized/subspecialized radiology resulted overall in a significant decrease of the RTAT: from 82 to 77 min for the first signature (p < 0.001), and 119 to 107 min and 295 to 238 min for the second signature (p < 0.001). Subgroup analyses demonstrate a significant decrease of the RTAT for MRI reports (e.g., second signature RTAT, 1051 to 401 min; p < 0.001) and conventional radiographs (e. g., second signature RTAT, 278 to 171 min; p < 0.001). The RTAT at major hospitals decreased from 288 to 245 min (second signature; p < 0.001) while the corresponding RTAT of minor hospitals decreased more remarkably, from 300 to 198 min (p < 0.001). However, the results were heterogenous; in some analyses, the RTAT even increased. The effect size analyses represent small effects. Conclusions Change of reporting system from decentralized/modality-based to centralized/subspecialized radiology was associated with a significant decreased RTAT. Specifically, the RTAT for MRI reports and conventional radiographs was significantly reduced. A pronounced RTAT decrease was observed at minor hospitals.

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Section: Discussionsupporting

confidence: 89%

Subspecialized radiological reporting reduces radiology report turnaround time

et al. 2020

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“…In the United States, a recent paper by Forsberg, Rospiko and Sunshine directly measured radiologist reporting time for a range of studies reported in 2015 within a system of several linked hospitals. The focus of the paper was radiologist reading time variability.…”

Section: Discussionmentioning

confidence: 99%

Measuring radiologist workload: Progressing from RVUs to study ascribable times

et al. 2018

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“…At treatment hospitals, radiologists need to carefully compare opacities on CT scans across time to alert cases suspected of deterioration. 19 Time pressure, heavy workload, and a shortage of experienced radiologists resulted in challenges for imaging-based management of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation

Wang

Chen

Huang

et al. 2020

The Lancet Digital Health

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Background Prompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics. Methods We trained a U-Net-based model on unenhanced chest CT scans obtained from 2447 patients admitted to Tongji Hospital (Wuhan, China) between Feb 1, 2020, and March 3, 2020 (1647 patients with RT-PCR-confirmed COVID-19 and 800 patients without COVID-19) to segment lung opacities and alert cases with COVID-19 imaging manifestations. The ability of artificial intelligence (AI) to triage patients suspected to have COVID-19 was assessed in a large external validation set, which included 2120 retrospectively collected consecutive cases from three fever clinics inside and outside the epidemic centre of Wuhan (Tianyou Hospital [Wuhan, China; area of high COVID-19 prevalence], Xianning Central Hospital [Xianning, China; area of medium COVID-19 prevalence], and The Second Xiangya Hospital [Changsha, China; area of low COVID-19 prevalence]) between Jan 22, 2020, and Feb 14, 2020. To validate the sensitivity of the algorithm in a larger sample of patients with COVID-19, we also included 761 chest CT scans from 722 patients with RT-PCR-confirmed COVID-19 treated in a makeshift hospital (Guanggu Fangcang Hospital, Wuhan, China) between Feb 21, 2020, and March 6, 2020. Additionally, the accuracy of AI was compared with a radiologist panel for the identification of lesion burden increase on pairs of CT scans obtained from 100 patients with COVID-19. Findings In the external validation set, using radiological reports as the reference standard, AI-aided triage achieved an area under the curve of 0·953 (95% CI 0·949–0·959), with a sensitivity of 0·923 (95% CI 0·914–0·932), specificity of 0·851 (0·842–0·860), a positive predictive value of 0·790 (0·777–0·803), and a negative predictive value of 0·948 (0·941–0·954). AI took a median of 0·55 min (IQR: 0·43–0·63) to flag a positive case, whereas radiologists took a median of 16·21 min (11·67–25·71) to draft a report and 23·06 min (15·67–39·20) to release a report. With regard to the identification of increases in lesion burden, AI achieved a sensitivity of 0·962 (95% CI 0·947–1·000) and a specificity of 0·875 (95 %CI 0·833–0·923). The agreement between AI and the radiologist panel was high (Cohen's kappa coefficient 0·839, 95% CI 0·718–0·940). Interpretation A deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated. Given its high accuracy across populations with varied COVID-19 prevalence, integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of COVID-19. Funding Special Project for Emergency of the Science and Technology Department of Hubei Province, China.

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Radiologists’ Variation of Time to Read Across Different Procedure Types

Cited by 25 publications

References 17 publications

Subspecialized radiological reporting reduces radiology report turnaround time

Subspecialized radiological reporting reduces radiology report turnaround time

Measuring radiologist workload: Progressing from RVUs to study ascribable times

Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation

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