Maryam Eslami Jahromi, Abbas Sheikhtaheri, Foziye Tahmasbi, Maryam Ahmadi, Fatemeh Rangraz Jeddi,
Volume 8, Issue 4 (Winter 2023)
Abstract
Objective: Identifying the strengths and weaknesses of curriculums and updating them in accordance with the future needs of students and the qualifications corresponding to their job positions is of particular importance. This study aims to identify the shortcomings of the current curriculum of Health Information Management for PhD students in Iran.
Methods: This descriptive study was conducted in 2021. The study population included all professors in the department of health information technology & management and the department of medical informatics and all PhD students and graduates in health information management from medical universities in Iran who were selected for the study by a census sampling method. Data were collected by a researcher-made questionnaire, by which the relevance and applicability of the courses and amount of required revision in the courses were evaluated. Data were analyzed in SPSS software, version 19. The answers to the open-ended questions were analyzed using the qualitative content analysis approach.
Results: Out of 67 participants, 61.1% were faculty members. In terms of relevance, the courses “Clinical Governance Information Systems” and “Health Information Economy”, and in terms of applicability, the courses “Clinical Governance Information Systems”, “Health Information Economy”, and “Methods for Improving and Managing Information Quality” were identified as the courses required modifications. Regarding the amount of required revision, most of the courses, except for “Advanced Statistical Methods”, “Thesis”, “Research Seminar” and “Design and Development of Health Information Systems No. 1”, needed serious revision.
Conclusion: It seems necessary to modify the curriculum of health information management for PhD students in Iran to improve the quality of education in this filed and respond to the needs of graduates based on the changes in the health field toward digital health and health information technologies.
Dr Hamidreza Tahmasbi, Dr Reza Besharati,
Volume 10, Issue 2 (Summer 2024)
Abstract
Objective Early diagnosis of hypertension in children is necessary to minimize the risks and consequences of this complication. This study aims to design and create a mobile application for diagnosing and predicting hypertension in children using machine learning methods so that the parents can continuously control and monitor their children’s blood pressure.
Methods In this applied-developmental study that was conducted in 2022, the values related to 19 factors affecting hypertension were collected from 1287 primary school children aged 7-13 years were first collected using questionnaires and measurements. Then, by combining the outputs of three machine learning methods (multi-layer perceptron, support vector machine (SVM) and random forest [RF]), a model was presented for a more accurate diagnosis of hypertension using Weka software, version 3.7.8 and Python programming language. To measure the effectiveness of the model, the 10-fold cross-validation method was used, and the paired t-test was used to compare its performance with other methods. P<0.05 was considered statistically significant. Finally, the proposed model was implemented in an Android mobile application.
Results The proposed model’s precision, sensitivity and specificity were 91.74%, 83.5% and 94.49%, respectively, and had better performance in predicting hypertension in children than other methods. These values were significantly different from those of compared methods (P<0.05).
Conclusion The proposed model performs better in diagnosing and predicting hypertension in children. The developed mobile application is thus useful for early diagnosis of hypertension in children. Parents can use this application to be informed about their children’s blood pressure status and consult a doctor immediately if there is a danger.
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