Background: Although Clinical Decision Support System (CDSS) has a potential to improve quality of health care, reports about CDSS are rare from Japan.
 Purpose: To report CDSS implementation in Electronic Medical Record System (EMRS) and its utilization and accuracy.
 System Overview: Patient-specific notifications were generated automatically from clinical information stored in and out of EMRS. CDSS provided patient-specific recommended actions for clinicians at the time of decision-making, and supported to take recommended action or record reasons for not following the recommendation into EMRS.
 Result: For 9 months from December 2011, 1710 notification were generated in 7 fields, and 1,628 of them were delivered. Its utilization rate is 89.2%, and 94.8% of them supported decision making correctly.
 Conclusion: The CDSS with high execution rate was implemented in the existing EMRS. Further studies are needed in order to evaluate whether this CDSS is related to improve quality of health care and to change user's behavior.

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 For diagnosis and treatment of chronic hepatitis C, there have been studies to predict the stage of liver fibrosis using the results of clinical blood and urine tests. Although many of conventional studies used the present test results, given the mechanism of liver fibrosis progress via the time-varying inflammation, it is considered to be effective to use the time series of test results from past to present. We propose a combination of the mean, standard deviation, and linear predictive coding cepstrum of the time series of clinical blood and urine test results as a feature. We then examine the effectiveness of the proposed feature through experiments to evaluate the prediction performance of the proposed feature and various competitive features. We used clinical dataset on hepatitis that was publicly provided at international conferences as inputs. The nearest neighbor method was adopted as a classifier. For each feature, we estimated the prediction performance by the leave-one-out cross-validation in two-class problems, one on the seriousness of fibrosis and the other on the existence of cirrhosis. The proposed feature consistently outperformed the conventional and other competitive features for different kinds of clinical tests. Especially under the conditions using the combination of clinical tests, the proposed feature achieved higher prediction performances at 5.32 to 13.83% than did the conventional features. The results supported the highest effectiveness of the proposed feature.

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 Semi-automatic learning of Bayesian network (BN) for inference about blood pressure (BP) was performed by using data gathered in a telemedicine experiment. In that experiment, data of medical electronic devices for home use that were placed in the participants' home were measured, collected through the Internet, and used for telemedicine. In the present study, for the aim of supporting users to judge different kinds of data like them as a whole on the client side, we tried to construct model for integrating data of 3 devices and additional data of weather statistics. 12 month data of a median per month for each subject (39 subjects) were used for graph structure learning. The resultant BN consisted of weight, number of steps, temperature, and systolic BP. Parameter estimation was performed individually for each of the 8 selected subjects. We tried to infer causally the best way of medical intervention for each subject by using those BNs, with a mind to prevention of hypertension. Different advice was obtained for different individual such as more walking and less weight.

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 As a part of revision of medical fee scheme in 2006, a new nursing distribution standard has been introduced to achieve proper distribution of nursing personnel for acute setting, which is so-called 7 : 1 nursing. Since then, number of hospitals introduced 7 : 1 nursing have been increasing. In these hospitals, the increased basic fee for hospitalization should lead to an increase in hospital revenue. However, hospital revenue may decrease because of the excess personnel expense increased. Though hospitals can reduce personnel expenses by employing novice or part-time nurses to increase their revenues, however, it is essential to employ experienced nurses in hospitals that provide highly advanced medical treatment; this leads to an increase in personnel expenses. Consequently, revenues have to be increased to compensate for this. In this study, we estimated the in come and expenditure for converting general ward rooms (15 beds) in a municipal hospital's Department of Haematology to aseptic cleanrooms. Furthermore, we analyzed the effects of such room conversion on hospital management. According to our estimation, although introducing the 7 : 1 nursing leads to a decrease in revenue, the room conversion may significantly increase the revenue and markedly improve the income and expenditure of the entire hospital. Converting general ward rooms to aseptic cleanrooms and employing nurses who can provide highly advanced medical treatment could increase the treatment fee per patient, facilitate the patients with advanced diseases, improve the operating ratio of beds, and reduce hospital days, and increase hospital revenues.

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