Telemedicine-based system for quality management and peer review in radiology
| Autor: | A.V. Vladzymyrskyy, N. V. Ledikhova, Sergey Morozov, Ekaterina Guseva, Dmitry Safronov |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
lcsh:Medical physics. Medical radiology. Nuclear medicine
medicine.medical_specialty Telemedicine Quality management Teleradiology media_common.quotation_subject lcsh:R895-920 Audit 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine medicine Radiology Nuclear Medicine and imaging Quality (business) media_common Diagnostic errors medicine.diagnostic_test business.industry Interventional radiology Quality Radiology business Quality assurance 030217 neurology & neurosurgery |
| Zdroj: | Insights into Imaging, Vol 9, Iss 3, Pp 337-341 (2018) |
| ISSN: | 1869-4101 |
| Popis: | Objectives Quality 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. Methods A 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. Results The 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 |
| Databáze: | OpenAIRE |
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