[SIMULATION AS A TRAINING AND ASSESSMENT TOOL FOR COMPETENCY BASED MEDICAL EDUCATION - A REGULATORY CHALLENGE].

INTRODUCTION: Competency Based Medical Education (CBME) is an educational approach that occupies a central place in medical education. Medical education is accountable for the graduates' professional level, ensuring they are skilled and competent in all key areas of their profession. Adopting CBME underscores the importance of simulation-based training. Experiential training provides, among other things: standardization of training, controlled exposure to extreme events and soft skills, such as patient-caregiver communication and teamwork training. Unlike the traditional apprentice approach, accountability reinforces the choice of a preliminary encounter with simulated patients prior to real-life care, as a complimentary tool for improving patient safety. Incorporating a practical exam is self-evident in CBME because of the need to ensure that the examinees are competent to provide unsupervised safe and quality care. Implementation of a national CBME program, likewise, incorporating simulation into national training programs, requires involvement and supervision on health system regulators. In this paper, we describe simulation-based national training programs that to date integrate competency-based training in the various medical sectors. As national programs, they are implemented under the guidance and in cooperation with the regulators. On the one hand, CBME is a new approach and its implementation will require time and the cooperation of many stakeholders. On the other hand, simulation is an existing, well-established training and assessment tool that can be used as an anchor around which you can start building the competency-based training programs.

MILANO--custom annotation of microarray results using automatic literature searches.

BACKGROUND: High-throughput genomic research tools are becoming standard in the biologist's toolbox. After processing the genomic data with one of the many available statistical algorithms to identify statistically significant genes, these genes need to be further analyzed for biological significance in light of all the existing knowledge. Literature mining--the process of representing literature data in a fashion that is easy to relate to genomic data--is one solution to this problem. RESULTS: We present a web-based tool, MILANO (Microarray Literature-based Annotation), that allows annotation of lists of genes derived from microarray results by user defined terms. Our annotation strategy is based on counting the number of literature co-occurrences of each gene on the list with a user defined term. This strategy allows the customization of the annotation procedure and thus overcomes one of the major limitations of the functional annotations usually provided with microarray results. MILANO expands the gene names to include all their informative synonyms while filtering out gene symbols that are likely to be less informative as literature searching terms. MILANO supports searching two literature databases: GeneRIF and Medline (through PubMed), allowing retrieval of both quick and comprehensive results. We demonstrate MILANO's ability to improve microarray analysis by analyzing a list of 150 genes that were affected by p53 overproduction. This analysis reveals that MILANO enables immediate identification of known p53 target genes on this list and assists in sorting the list into genes known to be involved in p53 related pathways, apoptosis and cell cycle arrest. CONCLUSIONS: MILANO provides a useful tool for the automatic custom annotation of microarray results which is based on all the available literature. MILANO has two major advances over similar tools: the ability to expand gene names to include all their informative synonyms while removing synonyms that are not informative and access to the GeneRIF database which provides short summaries of curated articles relevant to known genes. MILANO is available at http://milano.md.huji.ac.il.