ArticleInsights into imaging2018
Telemedicine-based system for quality management and peer review in radiology.
Article in Insights into imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Auditing the Audits: A Systematic Review on Different Procedures in Telemedicine.International journal of environmental research and public health · 2023Pooled it
- The impact of teleradiology: balancing clinical benefits and operational limitations.Neuroradiology · 2026Review
- Diagnostic error and bias in the department of radiology: a pictorial essay.Insights into imaging · 2023Review
- AI-Based CXR First Reading: Current Limitations to Ensure Practical Value.Diagnostics (Basel, Switzerland) · 2023Article
- Quality Assurance of a Cross-Border and Sub-Specialized Teleradiology Service.Healthcare (Basel, Switzerland) · 2022Article
- Evaluation is Key: Providing Appropriate Evaluation Measures for Participatory and User-Centred Design Processes of Healthcare IT.International journal of integrated care · 2021Article
- Assessing information sources to elucidate diagnostic process errors in radiologic imaging - a human factors framework.Journal of the American Medical Informatics Association : JAMIA · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesQuality assurance is the key component of modern radiology. A telemedicine-based quality assurance system helps to overcome the "scoring" approach and makes the quality control more accessible and objective.
methodsA concept for quality assurance in radiology is developed. Its realization is a set of strategies, actions, and tools. The latter is based on telemedicine-based peer review of 23,199 computed tomography (CT) and magnetic resonance imaging (MRI) images.
resultsThe conception of the system for quality management in radiology represents a chain of actions: "discrepancies evaluation - routine support - quality improvement activity - discrepancies evaluation". It is realized by an audit methodology, telemedicine, elearning, and other technologies. After a year of systemic telemedicine-based peer reviews, the authors have estimated that clinically significant discrepancies were detected in 6% of all cases, while clinically insignificant ones were found in 19% of cases. Most often, problems appear in musculoskeletal records; 80% of the examinations have diagnostic or technical imperfections. The presence of routine telemedicine support and personalized elearning allowed improving the diagnostics quality. The level of discrepancies has decreased significantly (p < 0.05).
conclusionThe telemedicine-based peer review system allows improving radiology departments' network effectiveness. MAIN MESSAGES: • "Scoring" approach to radiologists' performance assessment must be changed. • Telemedicine peer review and personalized elearning significantly decrease the number of discrepancies. • Teleradiology allows linking all primary-level hospitals to a common peer review network.
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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.