IntroductionHospital managers and personnel need to Hospital Information System (HIS) to increase the efficiency and effectiveness in their organization. Accurate, appropriate, precise, timely, valid information, and Suitable Information system for their tasks is required and the basis for decision making in various levels of the hospital management, since, this study was conducted to Assess of Selected HIS in Isfahan University of Medical Science Hospitals According to ISO 9241-10.MethodsThis paper obtained from an applied, descriptive cross sectional study, in which the medical records module of IUMS selected HIS in Isfahan University of Medical Science affiliated seven hospitals were assessed with ISO 9241-10 questionnaire contained 7 principles and 74 items. The obtained data were analyzed with SPSS software and descriptive statistics were used to examine measures of central tendencies.ResultsThe analysis of data revealed the following about the software: Suitability for user tasks, self descriptiveness, controllability by user, Conformity with user expectations, error tolerance, suitability for individualization, and suitability for user learning, respectively, was 68, 67, 70, 74, 69, 53, and 68 percent. Total compliance with ISO 9241-10 was 67 percent.ConclusionInformation is the basis for policy and decision making in various levels of the hospital management. Consequently, it seems that HIS developers should decrease HIS errors and increase its suitability for tasks, self descriptiveness, controllability, conformity with user expectations, error tolerance, suitability for individualization, suitability for user learning.

Background: Coronary artery disease (CAD) is known as the most common cardiovascular disease. The development of CAD is influenced by several risk factors. Diagnostic and therapeutic methods of this disease have many and costly side effects. Therefore, researchers are looking for cost-effective and accurate methods to diagnose this disease. Machine learning algorithms can help specialists diagnose the disease early. The aim of this study is to detect CAD using machine learning algorithms. Materials and Methods: In this study, three data mining algorithms support vector machine (SVM), artificial neural network (ANN), and random forest were used to predict CAD using the Isfahan Cohort Study dataset of Isfahan Cardiovascular Research Center. 19 features with 11495 records from this dataset were used for this research. Results: All three algorithms achieved relatively close results. However, the SVM had the highest accuracy compared to the other techniques. The accuracy was calculated as 89.73% for SVM. The ANN algorithm also obtained the high area under the curve, sensitivity and accuracy and provided acceptable performance. Age, sex, Sleep satisfaction, history of stroke, history of palpitations, and history of heart disease were most correlated with target class. Eleven rules were also extracted from this dataset with high confidence and support. Conclusion: In this study, it was shown that machine learning algorithms can be used with high accuracy to detect CAD. Thus, it allows physicians to perform timely preventive treatment in patients with CAD.

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