Since invasive techniques affect the patient’s body, doctors have to acquire knowledge and skills to implement such invasive techniques safely. In addition, to assure quality in the implementation of invasive techniques, we need to assess doctors’ competence in relation to these techniques. The purpose of this study was to develop a model to evaluate competence of medical doctors for invasive techniques at hospitals. It is essential that such a model is equipped with functions, procedures, and tools that are based on the existing literature related to competence evaluation. In this study, we reconstructed competence evaluation items used in a previous study. Competence evaluation items should evaluate concisely, specify the knowledge and skills to be acquired, and be comprehensible for the evaluator. To fulfill these requirements, we introduced the concept of the PDCA-cycle in the evaluation items, and applied it to invasive techniques. Based on these evaluation items, we designed procedures and tools for the implementation and management of competence evaluation. The procedure comprises 11 steps that need to be implemented by doctors and secretaries. Finally, we tested the model by utilizing it for deriving competence evaluation criteria in nine invasive techniques in Hospital A.

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Taguchi’s T-method is a part of the Mahalanobis-Taguchi (MT) system. The T-method is a technique for making predictions, and it has applications in various fields, including economic forecasting and predicting demand. The T-method can be summarized as follows: Prepare many weak learners that each predict one output value. Then, use the signal to noise ratio (S/N) to perform a weighted integration of these predictions and derive an overall estimate.
If the accuracies of each of the individual estimates are increased, the accuracy of the integrated estimate will be increased. Therefore, in this paper, we propose a way to improve the accuracies of the individual estimates. Our method uses the concept of a generalized inverse regression estimator for each of the individual estimators.
We use Monte Carlo simulations under various conditions to compare the prediction accuracies of the proposed method and that of the existing method. In many cases, our results show that the prediction accuracy of the proposal method is better than that of the original T-method. In particular, when the number of samples is small, the prediction accuracy of the proposal method is much better than that of the original T-method. Thus, we conclude that our proposed method is effective.

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Recently, a parametric estimation method for principal points for a multivariate binary distribution using a log-linear model has been proposed, and Akaike information criterion (AIC) has been applied to model selection for log-linear model. This paper compares three model selection methods based on AIC, Bayesian information criterion (BIC), and the likelihood ratio test (LRT) for estimating principal points for a multivariate binary distribution. The performances of the model selection methods are shown through numerical simulation studies

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In sports, a wide variety of match systems have been adopted to rank the participants. Well-known systems include the tournament system and the league match system. In order to overcome faults in these systems, a system known as cyclic design has been proposed.
A cyclic design is able to rank all of the participants and requires only one more game than those required by a tournament. We let the size of the block be 2, and we use an experimental design that compares v treatments. By calculating the observational equations that are derived from the design of the experiments, estimators (for example, estimations of an individual's strength) can be obtained.
In this paper, we validated a cyclic design with an actual dataset. We analyzed all combinations of the actual data, and we showed that there were discrepancies between the estimated ranks and the real ranks. In a cyclic design, the accuracy of the estimated rankings (AER) depends on the initial design, and so it was necessary to improve this.
We conducted a Monte Carlo simulation to compare the AER of a cyclic design to those of two common competitors: a tournament and a league match. In addition, we proposed a cyclic design combined with the Swiss system to compare the AER to other competitors. We found that the estimated ranking produced by the proposed cyclic design is more accurate than that produced by a tournament. Furthermore, we conducted a Monte Carlo simulation in which we used the similar model by adding interaction effects. We found that the AER of all methods were reduced compared to the previous results. In particular, interaction effects have a strong influence on the results of the cyclic designs. To improve performance, we proposed a further extended cyclic design that is robust to the interaction effects. We performed a Monte Carlo simulation that confirmed that the extended cyclic design is superior to the original cyclic design.

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Medical technology has been consistently improving in recent years. With that, people are more concerned with medial safety and its quality. In medical service provided by hospitals, quality is greatly influenced by human knowledge and skills. Therefore, it is fundamental to improve these factors. Actually, at many hospitals, medical safety education is provided to employees. To conduct more efficient education, the effectiveness of the education must be evaluated, and the specific contents must be revised after its administration. It is said that effects of an education can be evaluated in terms of learning (knowledge and skill) and the degree of behavior modification and results. In evaluation, we should begin by evaluating learning, and then move to behavior and results. However, the methodology for measuring these has not been established. In this study, we analyzed the long-term findings in a hospital and implemented an evaluation model for medical safety education. Further, we designed the effectiveness measurement tool based on that model. We then propose a method for measuring the effects of healthcare safety education. Using this method, medical workers can evaluate students’ learning free of bias and extensive work after providing education. In addition, they can review the contents of education and provide sufficient feedback on the test results to students.

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