RESUMEN
Introduction: Mobile health (mHealth) technology-based applications provide strong medical health-care support. Applications have an important impact as tools to improve the knowledge and support the health-care team practice. In this study, an over-the-counter (OTC) therapy application was developed based on Clinical Decision Support Systems (CDSS). CDSS is a key to improve health-related decisions and healthcare delivery. Furthermore, the quality and effectiveness of this application were evaluated among community pharmacists. Methods: The application was designed and developed for 10 topics of OTC therapy. After the approval of the expert panel, 40 pharmacists affiliated with Tehran University of Medical Science (TUMS) participated in this before and after quasi-experimental study. The related scenarios and checklists were designed for the ten topics. The participants had to manage the scenarios first by their knowledge and then with the application. The knowledge and pharmaceutical skills in OTC therapy were evaluated based on the obtained scores and the time recorded. The quality of the application was evaluated by pharmacists using user version of mobile application rating scale (uMARS) questionnaire. To compare before/after measurements of parametric and non-parametric data, we used the paired t-test and Wilcoxon matched-pairs test, respectively. Besides, the variables was compared using Mann-Whitney test. The statistical significance was considered at P<0.05. The analyses were performed using the statistical software Stata (ver. 13). Results: All scores after using the application increased, and the P-value was not significant. Also, the recorded time was increased after the use of the application, and the P-value was not significant. The minimum mean scores of the six uMARS questionnaire sections were 3. It means that acceptable scores were obtained in all sections of the questionnaire. The "App quality score" section of the application was reported 3.45±0.94. No relationship was found between gender and the median score of each section of the uMARS questionnaire. Conclusion: The OTC therapy application developed in this study will help Persian-speaking pharmacists to increase their knowledge and pharmaceutical skills.
RESUMEN
Many methods have been studied to analyze and interpret patterns and relationships that are embedded in the database to discover new knowledge in educational systems. Association rule mining is a type of data mining that identifies relationships among elements of the dataset. However, because these methods often generate various rules including non-significant ones, it is important to identify the most useful rules. Therefore, evaluating and ranking rules has become a topic of interest in the decision-making process in order to represent the level of usefulness of rules. We incorporated Apriori and Eclat algorithms on an educational dataset of a national medical exam in Iran. The aim of this study is to identify the usefulness of the extracted rules. This method can reliably discover new knowledge by interpreting the prioritized rules. The results show that those who have Scored in the highest category, i.e. [407,493], are accepted and who have scored in the lowest category, i.e. [150,236), are not accepted in the exam regardless of others features. Although, the rules that implication Accept=0 occurs, find out with high confidence, due to a large number of samples in this case. The ranking rules show this method is effective in the identification of insignificant rules that have no effect on decision making.