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1.
Article in Japanese | WPRIM | ID: wpr-689467

ABSTRACT

Drug dispensing is a statutory and designated duty of a pharmacist. We aimed to examine the changes in the nature of drug dispensing using a text mining method. Our corpus consisted of text documents from “Chozai Shishin”, the most standard manual for dispensing drugs in Japan, Editions 1 to 13 (Japan Pharmaceutical Association), and we used the KH Coder software for text mining. We constructed networks showing the association between frequent word co-occurrence and edition number, and co-occurrence relations for frequent words in each edition. We found that “patient” superseded “dispensing” as a frequent term over time. “Dispensing” was another frequent term with a highly centralized node in each edition. Accordingly, we targeted the term “dispensing” for network analysis to depict its co-occurrence relations. We found that the range of related words for “dispensing” broadened from “preparation” and “compounding” to include “patient adherence instructions”, “assessment”, “medical treatment”, and “information provision”. Accordingly, we concluded that the content of “dispensing”, which is a pharmacist’s duty, has expanded from the duties of “dispensing drugs” to include “responding to patients” within the definition of “dispensing”, and we were able to present this finding as objective data by using the mechanical method known as text mining.

2.
Article in Japanese | WPRIM | ID: wpr-738279

ABSTRACT

Drug dispensing is a statutory and designated duty of a pharmacist. We aimed to examine the changes in the nature of drug dispensing using a text mining method. Our corpus consisted of text documents from “Chozai Shishin”, the most standard manual for dispensing drugs in Japan, Editions 1 to 13 (Japan Pharmaceutical Association), and we used the KH Coder software for text mining. We constructed networks showing the association between frequent word co-occurrence and edition number, and co-occurrence relations for frequent words in each edition. We found that “patient” superseded “dispensing” as a frequent term over time. “Dispensing” was another frequent term with a highly centralized node in each edition. Accordingly, we targeted the term “dispensing” for network analysis to depict its co-occurrence relations. We found that the range of related words for “dispensing” broadened from “preparation” and “compounding” to include “patient adherence instructions”, “assessment”, “medical treatment”, and “information provision”. Accordingly, we concluded that the content of “dispensing”, which is a pharmacist’s duty, has expanded from the duties of “dispensing drugs” to include “responding to patients” within the definition of “dispensing”, and we were able to present this finding as objective data by using the mechanical method known as text mining.

3.
Article in English | WPRIM | ID: wpr-374954

ABSTRACT

<b>Objective: </b>In this study, we evaluated distinctive types of physical predisposition in patients with common side effects.<br><b>Method: </b>We selected 500 and 1,200 individuals with and without a previous diagnosis of side effects, respectively, through web-based research.  Then, we conducted a decision tree analysis for investigating the status of 100 types of physical predisposition in these individuals.<br><b>Results and Conclusion: </b>The individuals who had suffered from hepatic disorder and answered “relevant” for “predisposition to swelling” (likelihood ratio of a positive result [LR+] 2.17; <i>p</i>=0.004) and “very relevant” for “predisposition to skin dryness” (LR+ 3.52; <i>p</i><0.001) enhanced the probability of extracting individuals who developed side effects.  The individuals who had suffered from skin disorder and answered “relevant” for “predisposition to eczema and inflammation” and “not relevant” for “predisposition to higher temperature” had an LR+ of 2.22 (<i>p</i><0.001).  The individuals with “predisposition to worsening of physical condition on a rainy or high-humidity day” are more likely to develop side effects with the use of antibiotics and NSAIDs, compared to those without this predisposition (antibiotics: LR+ 2.33; NSAIDs: LR+ 2.51).  The results of this study indicate that we can identify patients with a high risk of side effects through an interview on predisposition.

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