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Radio Remember

Muziek kan een drager zijn van herinneringen en emoties. In onze jeugd en vroege volwassenheid ontwikkelen we onze persoonlijke muzieksmaak. Voor mensen met dementie, meestal 80-plussers, is dat ruim zestig jaar geleden. Voor hen is er nu een online muziekstation dat muziek uit die periode draait: Radio Remember.

Compliance to Diagnostic Reference Levels for radiation exposure in common radiological procedures in Dutch hospitals

INTRODUCTION: In the Netherlands, hospitals have difficulty in implementing the formal procedure of comparing radiation dose values to Diagnostic Reference Levels (DRLs).METHODS: To support the hospitals, train radiography students, and carry out a nationwide dose survey, diagnostic radiography students performed 125 DRL comparisons for nine different procedures in 29 radiology departments. Students were instructed at three Dutch Universities of Applied Sciences with a radiography programme and supervised by medical physicists from the participating hospitals.RESULTS: After a pilot study in the western part of the country in eight hospitals, this study was enlarged to involve 21 hospitals from all over the Netherlands. The 86 obtained dose comparisons fall below the DRLs in 97% of all cases. This very high compliance may have been enhanced by the voluntary participation of hospitals that are confident about their performance.CONCLUSION: The results indicate that the current DRLs that were not based on a national survey, may need to be updated, sometimes to half their current value. For chest and pelvis examinations the DRLs could be lowered from 12 and 300 μGy·m 2 to the 75-percentile values found in this study of 5,9 and 188 μGy·m 2, respectively.

Variation of in-hospital trauma team staffing: new resuscitation, new team

Background: Non-technical errors, such as insufficient communication or leadership, are a major cause of medical failures during trauma resuscitation. Research on staffing variation among trauma teams on teamwork is still in their infancy. In this study, the extent of variation in trauma team staffing was assessed. Our hypothesis was that there would be a high variation in trauma team staffing. Methods: Trauma team composition of consecutive resuscitations of injured patients were evaluated using videos. All trauma team members that where part of a trauma team during a trauma resuscitation were identified and classified during a one-week period. Other outcomes were number of unique team members, number of new team members following the previous resuscitation and new team members following the previous resuscitation in the same shift (Day, Evening, Night). Results: All thirty-two analyzed resuscitations had a unique trauma team composition and 101 unique members were involved. A mean of 5.71 (SD 2.57) new members in teams of consecutive trauma resuscitations was found, which was two-third of the trauma team. Mean team members present during trauma resuscitation was 8.38 (SD 1.43). Most variation in staffing was among nurses (32 unique members), radiology technicians (22 unique members) and anesthetists (19 unique members). The least variation was among trauma surgeons (3 unique members) and ER physicians (3 unique members). Conclusion: We found an extremely high variation in trauma team staffing during thirty-two consecutive resuscitations at our level one trauma center which is incorporated in an academic teaching hospital. Further research is required to explore and prevent potential negative effects of staffing variation in trauma teams on teamwork, processes and patient related outcomes.

The don't know option in progress testing

Formula scoring (FS) is the use of a don't know option (DKO) with subtraction of points for wrong answers. Its effect on construct validity and reliability of progress test scores, is subject of discussion. Choosing a DKO may not only be affected by knowledge level, but also by risk taking tendency, and may thus introduce construct-irrelevant variance into the knowledge measurement. On the other hand, FS may result in more reliable test scores. To evaluate the impact of FS on construct validity and reliability of progress test scores, a progress test for radiology residents was divided into two tests of 100 parallel items (A and B). Each test had a FS and a number-right (NR) version, A-FS, B-FS, A-NR, and B-NR. Participants (337) were randomly divided into two groups. One group took test A-FS followed by B-NR, and the second group test B-FS followed by A-NR. Evidence for impaired construct validity was sought in a hierarchical regression analysis by investigating how much of the participants' FS-score variance was explained by the DKO-score, compared to the contribution of the knowledge level (NR-score), while controlling for Group, Gender, and Training length. Cronbach's alpha was used to estimate NR and FS-score reliability per year group. NR score was found to explain 27 % of the variance of FS [F(1,332) = 219.2, p < 0.0005], DKO-score, and the interaction of DKO and Gender were found to explain 8 % [F(2,330) = 41.5, p < 0.0005], and the interaction of DKO and NR 1.6 % [F(1,329) = 16.6, p < 0.0005], supporting our hypothesis that FS introduces construct-irrelevant variance into the knowledge measurement. However, NR-scores showed considerably lower reliabilities than FS-scores (mean year-test group Cronbach's alphas were 0.62 and 0.74, respectively). Decisions about FS with progress tests should be a careful trade-off between systematic and random measurement error.

OPTIMAX 2018: a focus on education in Radiology

ESR Essentials

Abstract: AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large number of developed AI tools and those translated into daily clinical practice, which can be primarily attributed to limited usefulness and trust in current AI tools. Instead of technically driven development, little effort has been put into value-based development to ensure AI tools will have a clinically relevant impact on patient care. An iterative comprehensive value evaluation process covering the complete AI tool lifecycle should be part of radiology AI development. For value assessment of health technologies, health technology assessment (HTA) is an extensively used and comprehensive method. While most aspects of value covered by HTA apply to radiology AI, additional aspects, including transparency, explainability, and robustness, are unique to radiology AI and crucial in its value assessment. Additionally, value assessment should already be included early in the design stage to determine the potential impact and subsequent requirements of the AI tool. Such early assessment should be systematic, transparent, and practical to ensure all stakeholders and value aspects are considered. Hence, early value-based development by incorporating early HTA will lead to more valuable AI tools and thus facilitate translation to clinical practice. Clinical relevance statement: This paper advocates for the use of early value-based assessments. These assessments promote a comprehensive evaluation on how an AI tool in development can provide value in clinical practice and thus help improve the quality of these tools and the clinical process they support. Key Points: Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains. Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools. Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.