PubMed · 8439015
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J F Arens. 1993. A practice parameters overview.. https://doi.org/10.1097/00000542-199302000-00003
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Changes in medical practice that limit instruction time and patient availability, the expanding options for diagnosis and management, and advances in technology are contributing to greater use of simulation technology in medical education. Four areas of high-technology simulations currently being used are laparoscopic techniques, which provide surgeons with an opportunity to enhance their motor skills without risk to patients; a cardiovascular disease simulator, which can be used to simulate cardiac conditions; multimedia computer systems, which includes patient-centered, case-based programs that constitute a generalist curriculum in cardiology; and anesthesia simulators, which have controlled responses that vary according to numerous possible scenarios. Some benefits of simulation technology include improvements in certain surgical technical skills, in cardiovascular examination skills, and in acquisition and retention of knowledge compared with traditional lectures. These systems help to address the problem of poor skills training and proficiency and may provide a method for physicians to become self-directed lifelong learners.
Oral practice examinations (OPEs) are used in many anaesthesiology programmes to familiarize anaesthesiology residents with the format of the oral examination administered by the American Board of Anesthesiology. The OPE outcome (final grade) consists of 'Definite Not Pass', 'Probable Not Pass', 'Probable Pass' and 'Definite Pass'. In our study to assess the validity of the OPE, residents took an average of two (ranging from one to six) OPEs, each of which was evaluated by two board certified anaesthesiologists randomly selected from a pool of 12. A key question of interest was to identify factors, for example, the length of training, didactic experience and other characteristics, that most influence OPE outcome. In addition, we were interested in assessing the reliability of the final grade, that is, the covariance parameters are of interest as well. However, estimating variance components in multi-level data with an unequal number of repeated ordinal outcomes presents several statistical challenges, such as how to estimate high dimensional random effects parameters, especially for ordinal outcomes. We propose a Bayesian hierarchical proportional odds model for data with such complexity. The flexibility of such a model allows us to make inference on the association of OPE outcomes with other factors and to estimate the variance components as well.
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