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Abstract Background Teaching of evidence-based medicine (EBM) has become widespread in medical education. Teaching the teachers (TTT) courses address the increased teaching demand and the need to improve effectiveness of EBM teaching. We conducted a systematic review of assessment tools for EBM TTT courses. To summarise and appraise existing assessment methods for teaching the teachers courses in EBM by a systematic review. Methods We searched PubMed, BioMed, EmBase, Cochrane and Eric databases without language restrictions and included articles that assessed its participants. Study selection and data extraction were conducted independently by two reviewers. Results Of 1230 potentially relevant studies, five papers met the selection criteria. There were no specific assessment tools for evaluating effectiveness of EBM TTT courses. Some of the material available might be useful in initiating the development of such an assessment tool. Conclusion There is a need for the development of educationally sound assessment tools for teaching the teachers courses in EBM, without which it would be impossible to ascertain if such courses have the desired effect.

Abstract Background Evidence based medicine (EBM) is considered an integral part of medical training, but integration of teaching various EBM steps in everyday clinical practice is uncommon. Currently EBM is predominantly taught through theoretical courses, workshops and e-learning. However, clinical teachers lack confidence in teaching EBM in workplace and are often unsure of the existing opportunities for teaching EBM in the clinical setting. There is a need for continuing professional development (CPD) courses that train clinical trainers to teach EBM through on-the-job training by demonstration of applied EBM real time in clinical practice. We developed such a course to encourage clinically relevant teaching of EBM in post-graduate education in various clinical environments. Methods We devised an e-learning course targeting trainers with EBM knowledge to impart educational methods needed to teach application of EBM teaching in commonly used clinical settings. The curriculum development group comprised experienced EBM teachers, clinical epidemiologists, clinicians and educationalists from institutions in seven European countries. The e-learning sessions were designed to allow participants (teachers) to undertake the course in the workplace during short breaks within clinical activities. An independent European steering committee provided input into the process. Results The curriculum defined specific learning objectives for teaching EBM by exploiting educational opportunities in six different clinical settings. The e-modules incorporated video clips that demonstrate practical and effective methods of EBM teaching in everyday clinical practice. The course encouraged focussed teaching activities embedded within a trainer's personal learning plan and documentation in a CPD portfolio for reflection. Conclusion This curriculum will help senior clinicians to identify and make the best use of available opportunities in everyday practice in clinical situations to teach various steps of EBM and demonstrate their applicability to clinical practice. Once fully implemented, the ultimate outcome of this pilot project will be a European qualification in teaching EBM, which will be used by doctors, hospitals, professional bodies responsible for postgraduate qualifications and continuing medical education.

Abstract Background To evaluate the educational effects of a clinically integrated e-learning course for teaching basic evidence-based medicine (EBM) among postgraduates compared to a traditional lecture-based course of equivalent content. Methods We conducted a cluster randomised controlled trial in the Netherlands and the UK involving postgraduate trainees in six obstetrics and gynaecology departments. Outcomes (knowledge gain and change in attitude towards EBM) were compared between the clinically integrated e-learning course (intervention) and the traditional lecture based course (control). We measured change from pre- to post-intervention scores using a validated questionnaire assessing knowledge (primary outcome) and attitudes (secondary outcome). Results There were six clusters involving teaching of 61 postgraduate trainees (28 in the intervention and 33 in the control group). The intervention group achieved slightly higher scores for knowledge gain compared to the control, but these results were not statistically significant (difference in knowledge gain: 3.5 points, 95% CI -2.7 to 9.8, p = 0.27). The attitudinal changes were similar for both groups. Conclusion A clinically integrated e-learning course was at least as effective as a traditional lecture based course and was well accepted. Being less costly than traditional teaching and allowing for more independent learning through materials that can be easily updated, there is a place for incorporating e-learning into postgraduate EBM curricula that offer on-the-job training for just-in-time learning. Trial registration Trial registration number: ACTRN12609000022268.

Abstract Background We developed and evaluated the outcomes of an e-learning course for evidence based medicine (EBM) training in postgraduate medical education in different languages and settings across five European countries. Methods We measured changes in knowledge and attitudes with well-developed assessment tools before and after administration of the course. The course consisted of five e-learning modules covering acquisition (formulating a question and search of the literature), appraisal, application and implementation of findings from systematic reviews of therapeutic interventions, each with interactive audio-visual learning materials of 15 to 20 minutes duration. The modules were prepared in English, Spanish, German and Hungarian. The course was delivered to 101 students from different specialties in Germany (psychiatrists), Hungary (mixture of specialties), Spain (general medical practitioners), Switzerland (obstetricians-gynaecologists) and the UK (obstetricians-gynaecologists). We analysed changes in scores across modules and countries. Results On average across all countries, knowledge scores significantly improved from pre- to post-course for all five modules (p < 0.001). The improvements in scores were on average 1.87 points (14% of total score) for module 1, 1.81 points (26% of total score) for module 2, 1.9 points (11% of total score) for module 3, 1.9 points (12% of total score) for module 4 and 1.14 points (14% of total score) for module 5. In the country specific analysis, knowledge gain was not significant for module 4 in Spain, Switzerland and the UK, for module 3 in Spain and Switzerland and for module 2 in Spain. Compared to pre-course assessment, after completing the course participants felt more confident that they can assess research evidence and that the healthcare system in their country should have its own programme of research about clinical effectiveness. Conclusion E-learning in EBM can be harmonised for effective teaching and learning in different languages, educational settings and clinical specialties, paving the way for development of an international e-EBM course.

Abstract Background Over the last years key stake holders in the healthcare sector have increasingly recognised evidence based medicine (EBM) as a means to improving the quality of healthcare. However, there is considerable uncertainty about the best way to disseminate basic knowledge of EBM. As a result, huge variation in EBM educational provision, setting, duration, intensity, content, and teaching methodology exists across Europe and worldwide. Most courses for health care professionals are delivered outside the work context ('stand alone') and lack adaptation to the specific needs for EBM at the learners' workplace. Courses with modern 'adaptive' EBM teaching that employ principles of effective continuing education might fill that gap. We aimed to develop a course for post-graduate education which is clinically integrated and allows maximum flexibility for teachers and learners. Methods A group of experienced EBM teachers, clinical epidemiologists, clinicians and educationalists from institutions from eight European countries participated. We used an established methodology of curriculum development to design a clinically integrated EBM course with substantial components of e-learning. An independent European steering committee provided input into the process. Results We defined explicit learning objectives about knowledge, skills, attitudes and behaviour for the five steps of EBM. A handbook guides facilitator and learner through five modules with clinical and e-learning components. Focussed activities and targeted assignments round off the learning process, after which each module is formally assessed. Conclusion The course is learner-centred, problem-based, integrated with activities in the workplace and flexible. When successfully implemented, the course is designed to provide just-in-time learning through on-the-job-training, with the potential for teaching and learning to directly impact on practice.

Abstract Background Numerous methods for classifying brain tumours based on magnetic resonance spectra and imaging have been presented in the last 15 years. Generally, these methods use supervised machine learning to develop a classifier from a database of cases for which the diagnosis is already known. However, little has been published on developing classifiers based on mixed modalities, e.g. combining imaging information with spectroscopy. In this work a method of generating probabilities of tumour class from anatomical location is presented. Methods The method of "belief networks" is introduced as a means of generating probabilities that a tumour is any given type. The belief networks are constructed using a database of paediatric tumour cases consisting of data collected over five decades; the problems associated with using this data are discussed. To verify the usefulness of the networks, an application of the method is presented in which prior probabilities were generated and combined with a classification of tumours based solely on MRS data. Results Belief networks were constructed from a database of over 1300 cases. These can be used to generate a probability that a tumour is any given type. Networks are presented for astrocytoma grades I and II, astrocytoma grades III and IV, ependymoma, pineoblastoma, primitive neuroectodermal tumour (PNET), germinoma, medulloblastoma, craniopharyngioma and a group representing rare tumours, "other". Using the network to generate prior probabilities for classification improves the accuracy when compared with generating prior probabilities based on class prevalence. Conclusion Bayesian belief networks are a simple way of using discrete clinical information to generate probabilities usable in classification. The belief network method can be robust to incomplete datasets. Inclusion of a priori knowledge is an effective way of improving classification of brain tumours by non-invasive methods.

Background: Patients with diabetes are at an increased risk of readmission and mortality when discharged from hospital. Existing research identifies statistically significant risk factors that are thought to underpin these outcomes. Increasingly, these risk factors are being used to create risk prediction models, and target risk modifying interventions. These risk factors are typically reported in the literature accompanied by unstandardized effect sizes, which makes comparisons difficult. We demonstrate an assessment of variation between standardised effect sizes for such risk factors across care outcomes and patient cohorts. Such an approach will support development of more rigorous risk stratification tools and better targeting of intervention measures.Methods: Data was extracted from the electronic health record of a major tertiary referral centre, over a 3-year period, for all patients discharged from hospital with a concurrent diagnosis of diabetes mellitus. Risk factors selected for extraction were pre-specified according to a systematic review of the research literature. Standardised effect sizes were calculated for all statistically significant risk factors, and compared across patient cohorts and both readmission & mortality outcome measures.Results: Data was extracted for 46,357 distinct admissions patients, creating a large dataset of approximately 10,281,400 data points. The calculation of standardized effect size measures allowed direct comparison. Effect sizes were noted to be larger for mortality compared to readmission, as well as for being larger for surgical and type 1 diabetes cohorts of patients.Conclusions: The calculation of standardised effect sizes is an important step in evaluating risk factors for healthcare events. This will improve our understanding of risk and support the development of more effective risk stratification tools to support patients to make better informed decisions at discharge from hospital. [ABSTRACT FROM AUTHOR]

Background: Assurance of digital health interventions involves, amongst others, clinical validation, which requires large datasets to test the application in realistic clinical scenarios. Development of such datasets is time consuming and challenging in terms of maintaining patient anonymity and consent. Objective: The development of synthetic datasets that maintain the statistical properties of the real datasets. Method: An artificial neural network based, generative adversarial network was implemented and trained, using numerical and categorical variables, including ICD-9 codes from the MIMIC III dataset, to produce a synthetic dataset. Results: The synthetic dataset, exhibits a correlation matrix highly similar to the real dataset, good Jaccard similarity and passing the KS test. Conclusions: The proof of concept was successful with the approach being promising for further work. [ABSTRACT FROM AUTHOR]