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E-learning for medical imaging specialists: introducing blended learning in a nuclear medicine specialist course.

ABSTRACT: While e-learning has become an important tool in teaching medical students, the training of specialists in medical imaging is still dominated by lecture-based courses.To assess the potential of e-learning in specialist education in medical imaging.An existing lecture-based five-day course in Clinical Nuclear Medicine (NM) was enhanced by e-learning resources and activities, including practical exercises. An anonymized survey was conducted after participants had completed and passed the multiple choice electronic course examination.Twelve out of 15 course participants (80%) responded. Overall satisfaction with the new course format was high, but 25% of the respondents wanted more interactive elements such as discussions and practical exercises. The importance of lecture handouts and supplementary online material such as selected original articles and professional guidelines was affirmed by all the respondents (92% fully, 8% partially), while 75% fully and 25% partially agreed that the lectures had been interesting and relevant.E-learning represents a hitherto unrealized potential in the education of medical specialists. It may expedite training of medical specialists while at the same time containing costs.

SUBMITTER: Haslerud T

PROVIDER: S-EPMC5533268 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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E-learning for medical imaging specialists: introducing blended learning in a nuclear medicine specialist course.

Haslerud Torjan T Tulipan Andreas Julius AJ Gray Robert M RM Biermann Martin M

Acta radiologica open 20170725 7

<h4>Background</h4>While e-learning has become an important tool in teaching medical students, the training of specialists in medical imaging is still dominated by lecture-based courses.<h4>Purpose</h4>To assess the potential of e-learning in specialist education in medical imaging.<h4>Material and methods</h4>An existing lecture-based five-day course in Clinical Nuclear Medicine (NM) was enhanced by e-learning resources and activities, including practical exercises. An anonymized survey was con ...[more]

PMID: 28804642

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Project description:BackgroundConflicts of interest (COIs), including those arising from interactions with pharmaceutical companies, may lead to bias in medical data. Although medical students are now requesting more education on COIs and bias, they are still not adequately taught during medical school, and few published courses on this topic exist. The objective of our study was therefore to evaluate the feasibility and effectiveness of a blended-learning course for detecting and avoiding bias in medical data, with a special focus on COIs.MethodsWe developed a blended learning course on bias detection, COIs, and risk communication. It was piloted in the Fall Semester of 2019/2020 using a pre/post-test design. The primary outcome was a gain in bias detection skills, tested by a novel key feature test. Secondary outcomes were (i) skepticism (tested using an attitude questionnaire), (ii) the intention to manage COIs in a professional way so as to avoid bias (tested using a situational judgment test) and (iii) the course evaluation by the students.ResultsSeventeen students participated in the study. The key feature test showed a significant improvement in bias detection skills at post-testing, with a difference in means of 3.1 points (95%-CI: 1.7-4.4, p-value: < 0.001; highest possible score: 16 points). The mean score after the course was 6.21 (SD: 2.62). The attitude questionnaire and situational judgment test also showed an improvement in skepticism and intentions to manage COIs, respectively. Students evaluated the course as having been worthwhile (Median: 5, IQR: 0.75, Likert-Scale 1-6, 6 = fully applicable).ConclusionsThe blended learning format of the course was feasible and effective. The results suggest a relevant learning gain; however, the low mean score on the key feature test after the course reflects the difficulty of the subject matter. Although a single course has the potential to induce significant short-term improvements in bias detection skills, the complexity of this important subject necessitates its longitudinal integration into medical curricula. This concept should include specific courses such as that presented here as well as an integration of the topic into clinical courses to improve context-related understanding of COIs and medical data bias.

Project description:BackgroundBlended learning, which combines face-to-face learning and e-learning, has grown rapidly to be commonly used in education. Nevertheless, the effectiveness of this learning approach has not been completely quantitatively synthesized and evaluated using knowledge outcomes in health education.ObjectiveThe aim of this study was to assess the effectiveness of blended learning compared to that of traditional learning in health education.MethodsWe performed a systematic review of blended learning in health education in MEDLINE from January 1990 to July 2019. We independently selected studies, extracted data, assessed risk of bias, and compared overall blended learning versus traditional learning, offline blended learning versus traditional learning, online blended learning versus traditional learning, digital blended learning versus traditional learning, computer-aided instruction blended learning versus traditional learning, and virtual patient blended learning versus traditional learning. All pooled analyses were based on random-effect models, and the I2 statistic was used to quantify heterogeneity across studies.ResultsA total of 56 studies (N=9943 participants) assessing several types of learning support in blended learning met our inclusion criteria; 3 studies investigated offline support, 7 studies investigated digital support, 34 studies investigated online support, 8 studies investigated computer-assisted instruction support, and 5 studies used virtual patient support for blended learning. The pooled analysis comparing all blended learning to traditional learning showed significantly better knowledge outcomes for blended learning (standardized mean difference 1.07, 95% CI 0.85 to 1.28, I2=94.3%). Similar results were observed for online (standardized mean difference 0.73, 95% CI 0.60 to 0.86, I2=94.9%), computer-assisted instruction (standardized mean difference 1.13, 95% CI 0.47 to 1.79, I2=78.0%), and virtual patient (standardized mean difference 0.62, 95% CI 0.18 to 1.06, I2=78.4%) learning support, but results for offline learning support (standardized mean difference 0.08, 95% CI -0.63 to 0.79, I2=87.9%) and digital learning support (standardized mean difference 0.04, 95% CI -0.45 to 0.52, I2=93.4%) were not significant.ConclusionsFrom this review, blended learning demonstrated consistently better effects on knowledge outcomes when compared with traditional learning in health education. Further studies are needed to confirm these results and to explore the utility of different design variants of blended learning.

Project description:'See what you treat and treat what you see, at a molecular level', could be the motto of theranostics. The concept implies diagnosis (imaging) and treatment of cells (usually cancer) using the same molecule, thus guaranteeing a targeted cytotoxic approach of the imaged tumor cells while sparing healthy tissues. As the brilliant late Sam Gambhir would say, the imaging agent acts like a 'molecular spy' and reveals where the tumoral cells are located and the extent of disease burden (diagnosis). For treatment, the same 'molecular spy' docks to the same tumor cells, this time delivering cytotoxic doses of radiation (treatment). This duality represents the concept of a 'theranostic pair', which follows the scope and fundamental principles of targeted precision and personalized medicine. Although the term theranostic was noted in medical literature in the early 2000s, the principle is not at all new to nuclear medicine. The first example of theranostic dates back to 1941 when Dr. Saul Hertz first applied radioiodine for radionuclide treatment of thyroid cells in patients with hyperthyroidism. Ever since, theranostics has been an integral element of nuclear medicine and molecular imaging. The more we understand tumor biology and molecular pathology of carcinogenesis, including specific mutations and receptor expression profiles, the more specific these 'molecular spies' can be developed for diagnostic molecular imaging and subsequent radionuclide targeted therapy (radiotheranostics). The appropriate selection of the diagnostic and therapeutic radionuclide for the 'theranostic pair' is critical and takes into account not only the type of cytotoxic radiation emission, but also the linear energy transfer (LET), and the physical half-lives. Advances in radiochemistry and radiopharmacy with new radiolabeling techniques and chelators are revolutionizing the field. The landscape of cytotoxic systemic radionuclide treatments has dramatically expanded through the past decades thanks to all these advancements. This article discusses present and promising future theranostic applications for various types of diseases such as thyroid disorders, neuroendocrine tumors (NET), pediatric malignancies, and prostate cancer (PC), and provides an outlook for future perspectives.

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E-learning for medical imaging specialists: introducing blended learning in a nuclear medicine specialist course.

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E-learning for medical imaging specialists: introducing blended learning in a nuclear medicine specialist course.

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