ArticleJournal of digital imaging2017
Radiologists' Variation of Time to Read Across Different Procedure Types.
Article in Journal of digital imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Review
- Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Radiology AI and sustainability paradox: environmental, economic, and social dimensions.Insights into imaging · 2025Article
- HIPPO artificial intelligence: Correlating automated radiographic femoroacetabular measurements with patient-reported outcomes in developmental hip dysplasia.World journal of experimental medicine · 2024Article
- Applying queueing theory to evaluate wait-time-savings of triage algorithms.Queueing systems · 2024Article
- From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images.Cureus · 2023Review
- A Global Review of the Impacts of the Coronavirus (COVID-19) Pandemic on Radiology Practice, Finances, and Operations.Life (Basel, Switzerland) · 2023Review
- Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region.European radiology · 2022Article
- Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.Radiology. Artificial intelligence · 2022Review
- Improved assessment of middle ear recurrent/residual cholesteatomas using temporal subtraction CT.Japanese journal of radiology · 2022Article
- Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography.Quantitative imaging in medicine and surgery · 2021Article
- Subspecialized radiological reporting reduces radiology report turnaround time.Insights into imaging · 2020Article
- Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation.The Lancet. Digital health · 2020Article
- Artificial Intelligence for the Future Radiology Diagnostic Service.Frontiers in molecular biosciences · 2020Review
- Integration of fully automated computer-aided pulmonary nodule detection into CT pulmonary angiography studies in the emergency department: effect on workflow and diagnostic accuracy.Emergency radiology · 2019Article
- Who PACS a Punch? The Role of the Picture Archiving and Communication System/Radiology Information System (PACS/RIS) in Quantifying Experiential Learning in Radiology Residency.Journal of digital imaging · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The workload of US radiologists has increased over the past two decades as measured through total annual relative value units (RVUs). This increase in RVUs generated suggests that radiologists' productivity has increased. However, true productivity (output unit per input unit; RVU per time) is at large unknown since actual time required to interpret and report a case is rarely recorded. In this study, we analyzed how the time to read a case varies between radiologists over a set of different procedure types by retrospectively extracting reading times from PACS usage logs. Specifically, we tested two hypotheses that; i) relative variation in time to read per procedure type increases as the median time to read a procedure type increases, and ii) relative rankings in terms of median reading speed for individual radiologists are consistent across different procedure types. The results that, i) a correlation of -0.25 between the coefficient of variation and median time to read and ii) that only 12 out of 46 radiologists had consistent rankings in terms of time to read across different procedure types, show both hypotheses to be without support. The results show that workload distribution will not follow any general rule for a radiologist across all procedures or a general rule for a specific procedure across many readers. Rather the findings suggest that improved overall practice efficiency can be achieved only by taking into account radiologists' individual productivity per procedure type when distributing unread cases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.