Objective: It is important to prevent dispensing errors for the safety of patients.  For this purpose, it is necessary to improve the dispensing process on the basis of risk priority.  Quantitative evaluation of risk in each operation makes it possible to compare the risk relatively.  In this study, we analyzed data reported in the Pharmacy Risk Error Management System (PREM-S) to evaluate the risk priority in the medicine preparation step of the dispensing process.
Methods: Data of 1,202 cases concerning medicine preparation step were extracted from in 2,458 cases reported into PREM-S between May 2010 and April 2011.  We classified the medicine preparation step into 31 categories based on the operation.  We analyzed the levels in each category as adverse effects on patient’s health by the errors and also the relationship between adverse effect and detection time.  The level represents the degree of adverse effect for the patient.
Results: Information on levels of the 31 categories was obtained from the analysis.  Eight categories included reports of effects on the health of patients.  Three categories such as calculation of powder medicine included level 4 cases.  Detection time was significantly different between level 1 and levels 2 to 4, but there was no significant difference between levels 2 to 4.
Conclusion: The results suggest that analysis of data reported in PREM-S enables evaluation of the risk priority systematically and efficiently.  Evaluation of the risk priority will contribute to prevention of dispensing errors and health effects derived from them.

Objective: To attain optimal blood concentration rapidly, it is needed to perform initial dose setting appropriately when vancomycin (VCM) used.  In order to design initial dose settings of VCM more currently, we compared the predictive performance of two types of VCM therapeutic drug monitoring (TDM) analysis software retrospectively.
Method: We utilized two TDM analysis software, SHIONOGI-VCM-TDM ver.2009 (VCM-TDM) and “Vancomycin MEEK TDM analysis software Ver. 2.0” (MEEK), based on patient’s background. 112 patients who received VCM and performed TDM were analyzed during the period from October 2011 through September 2012 and compared the actual trough level with the predictive trough level.  The predictive performance was evaluated by calculating ME (mean prediction error), MAE (mean absolute prediction error), and RMSE (root mean squared error).  Age, gender, and a renal function were evaluated as patient’s background.
Results: VCM-TDM gave good predictive performance for patients overall.  When classified patient’s background complexly (sex, age, and renal function), as for male patients, VCM-TDM showed good predictive performance except for the group over 65 years old and CCr over 85 mL/min.  For female patients, the difference of predictive performance was not accepted by all groups.
Conclusion: These results suggest, for male patients, we should use VCM-TDM for initial dose settings except for the group over 65 years old and over CCr 85 mL/min.  For the other patients, we consider that both of software can be used.  These new findings seem to contribute to proper dosage settings of VCM.

Objective: It is very important that, to avoid, pharmacists-check medication being taken by patient.  In the Hokkaido University Hospital we used commercial drug identification software at the start of outpatient prescription identification duty and reported the outcome.  Furthermore, we filled in another hand-written check sheet with the drug’s name, whether or not it is used in our hospital, alternative drugs, and the dosage and administration.  Because of the risk of drugs being entered by mistake, we built a database for drug identification and distinguished the outpatient’s prescriptions.  With this system it is possible integrate identification reports and check sheet using one style, automatically.  We also to smoothly rationalize duties by planning correct communication between the medical staff.  At the same time, we analyzed the case that was able to intervene in reasonable use of medical supplies with a past identification report as a result of pharmacists distinguishing outpatient prescriptions.
Design and Methods: This system was constructed using Microsoft® Access, which is a general-purpose database software.  Also, the medical supply database that we used for this system uses “Drugs in Japan Ethical Drugs DB (supervised by Drugs in Japan Forum)” published by JIHO Co., Ltd.
Results: By using this system, we were able to reduce the time required to identify the drugs and make the report.  The result of a questionnaire carried out on doctors and a nurses and medical staff revealed that more than 90% of the respondents claimed, “the report is easy to refer.”  Likewise, we analyzed a report of the previous year and recognized that medical staff could not find the inappropriate use of prescriptions for outpatients in about 17.5%.
Conclusion: This system improved the efficiency of outpatient prescriptions practices, and it became clear that it could be used convincingly as a tool to share appropriate drug information between medical staff and pharmacists, more precisely.  In addition, feedback from medical staff suggested that it might prevent the risk of problems surrounding outpatient prescriptions, from the viewpoint of the pharmacist.

Abstract
Objective : In this study, we conducted a survey on both the use of supplements by customers and information provided by pharmacists, to clarify customer understanding. We also sought to ascertain the actual current state of information provided by pharmacists to customers, as well as investigating both how, and to what extent, pharmacists should be involved in customers 'use of supplements.
Methodology : During regular pharmacy visits, pharmacists used a questionnaire to interview 1,253 customers, in 14 community pharmacies, in Tokyo and Hokkaido, respectively. A different questionnaire, designed for pharmacists, was also given to 289 pharmacists who were either working in those same pharmacies, or who attended the Conference on Pharmaceutical Sciences in Hokkaido (2011, Sapporo).
Results : The results of the survey showed that approximately 50％ of consumers greatly desired the provision of safety and efficacy information about supplements by pharmacists. However, few pharmacists answered customers' questions satisfactorily (only 7.3％ of total responses).
 The results also indicated that only 30％ of pharmacists actively gathered information about supplements, despite the fact that 67.5％ of pharmacists were aware that they were expected to do so by customers, in their roles as primary information providers regarding such treatments. Furthermore, even those pharmacists who checked information regarding supplements depended mostly on information acquired from the Internet.
Conclusion : There is a large gap between customers' expectations for explanation of supplements and the reality of such explanations, and the information actually provided by pharmacists. In order to live up to their customers' expectations, pharmacists should foster both wider dissemination and better understanding of evidence-based information about supplements. Pharmacists should also provide integrated management of drugs and supplements for patients.

Objective: Hematological toxicity, including neutropenia and thrombocytopenia, is a typical side effect of ganciclovir (GCV). We previously developed a risk-prediction model for GCV-induced neutropenia using decision tree (DT) analysis. By employing the DT model, which is a flowchart-like framework, users can predict the combination of factors that may increase neutropenia risk. However, a risk-prediction model for thrombocytopenia has not been established. Here, we aimed to identify the risk factors associated with GCV-induced thrombocytopenia and construct risk-prediction models.Method: We retrospectively evaluated the medical records of 386 patients who received GCV between April 2008 and March 2018 at Hokkaido University Hospital. Thrombocytopenia is defined as a decrease in the platelet count (PLT) to ＜50,000 cells/mm3 and to a ＜75% decrease. Risk factors of thrombocytopenia were extracted from the medical records using a multiple logistic regression analysis. Moreover, we employed chi-squared automatic interaction detection (CHAID) and classification and regression tree (CRT) algorithms to develop the DT models. The accuracies of the established models were evaluated to assess their reliability.Results: Thrombocytopenia occurred in 47 (12.2%) patients. In the multiple logistic regression analysis, data of patients with white blood cells ＜7,000 cells/mm3,PLT＜101,000 cells/mm3 and total bilirubin ≥ 0.8 mg/dL were extracted. Two risk-prediction models were constructed, and patients were divided into six and seven subgroups. In both algorithms, data on hematopoietic stem cell transplantations, PLT ＜101,000 cells/mm3, serum albumin ＜ 2.8 g/dL, total bilirubin ≥ 0.8 mg/dL, and residence in intensive care unit were extracted. The predictive accuracy of both the CHAID algorithm and the logistic regression models was 87.8% and that of the CRT algorithm was 88.3%, indicating they were reliable.Conclusion: We successfully identified the factors associated with GCV-induced thrombocytopenia and constructed useful flowchartlike risk-prediction models.