Aims: The search for signals of drug-induced adverse events using spontaneous reporting databases are used in clinical practice and pharmacovigilance research. However, it is difficult for pharmacists unfamiliar with programming to analyze large databases. Therefore, we developed an analysis method that does not require user programming.Methods: An analysis flow was created using KNIME, which allows for intuitive operation of the R language. In addition, the time required to calculate the signal scores was compared for the three personal computers (PCs) with different specifications.Results: The KNIME workflow for this analysis was created using as little R programming as possible and, in principle, only the functions contained within KNIME. Therefore, the KNIME workflow is redundant. However, the analysis results can be obtained instantly on PCs of any specifications. Furthermore, unlike previously reported applications for calculating safety signal scores from spontaneous reporting databases, the signal scores can be calculated using Bayesian statistical methods. Signal scores can be calculated for the Information Component, a measure for single drugs, and for the Ω shrinkage measure, a measure for drug-drug interactions.Conclusion: KNIME can be implemented at a low cost and can be used by users who are unaware of R language programming to calculate signal scores. Furthermore, hawse have demonstrated sufficient scalability of KNME to allow for a more advanced analysis compared with previously reported applications.

Objective: Confirmation by pharmacists of brought-in drugs is not only limited to identification of drugs, but also requires prescription design and proposals based on the background of patients and evaluation of associated information. In this study, we analyzed the content of brought-in drugs related PreAVOID reports in our hospital according to the years of pharmacist experience in order to help educate pharmacist for better brought-in drugs confirmation.Method: Various interventions regarding brought-in drugs were compared between two groups: pharmacists with >6 years of experience (group H) and those with < 5 years of experience (group L). PreAVOID reports, which related to drugs brought in by patients admitted to the Ogaki Municipal Hospital between April 1, 2018 and March 31, 2019 were assessed.Result:The PreAVOID reporting rate for the number of brought-in drugs confirmed was higher in group H (1.56%) than in group L (1.12%) (odds ratio 1.399, p = 0.023). The PreAVOID reporting rate when reporting was based solely on prescription information did not differ between these two groups, but when patient background, including disease-related information, was included with prescription information, the rate was higher in group H (0.80%) than in group L (0.30%) (odds ratio 2.725, p < 0.001). Group H provided more reports related to unnecessary drugs.Conclusion: The involvement of pharmacists in the evaluation of brought-in drugs is important when reviewing subsequent medical treatments. Our findings suggest that to improve the quality of treatment, it is necessary to provide appropriate newcomer education, such as conducting case studies using PreAVOID cases.