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Showing 3 results for Rabiei
People Awareness on Diabetes Disease and Complications in Bushehr, Iran using Linears Models
Qr Babaee , Ar Soltanian , Hr Khalkhaly , M Rabieian , F Bahreini , M Afkhami Ardekani ,
Volume 1, Issue 1 (9 2007)
Abstract

Backgound: Approximately half of the diabetics population type 2 are not aware of their disease .Lack of awareness can lead to development of diabetes and increase cost of treatment. The aim of this survey was to determine the level of population awareness in Bushehr port in south of Iran in Bushehr prov­ince.

Methods: In this cross-sectional study, 719 subjects (417 male and 302 female) aged over 18 years old, without diabetes and inhabitant in Bushehr port in 2005 were assessed. Multi-stage random simple sampling was used. A 39 question questionnaire was used with validity checked by researchers in Yazd Diabetes Research Center and reliability alpha-cronbach=75%.The data were analyzed with independ­ent t-test, pearson correlation coefficient , ANOVA and Multiple Logistic Regression models by SPSS package ver. 10.05.

Results: Mean and SD of scores of subjects knowledge levels were 16.96 and 6.29, respectively. The levels of males' awareness rate was more than females' (P=0.001). There was indirect relation between subjects awareness and their age (r=-0.203, P=0.001) and direct relation between awareness and the level of education (P=0.01, r=0.07).The mean of awareness scores was not similar between singles and married (P=0.042). Awareness regarding fundamental diabetes disease, primary symptoms, early com­pli­ca­tions, delay complications, diet awareness was low and concerning controlling methods of diabetes was high.

Conclusion: Awareness in relation to fundamental and complications of diabetes disease was low, so the people need more education about diabetes.


The Effects of Implementing Clinical Decision Support Systems on the Diagnosis, Treatment and Management of Cancers: A Systematic Review
Saman Mohammadpour, Reza Rabiei, Elham Shabahrami, Kamyar Fathisalari, Maryam Khakzad, Mostafa Langarizadeh,
Volume 16, Issue 2 (Jun 2022)
Abstract
Background and Aim: Cancer is the second leading cause of death in the world, which leads to the death of more than 10 million people in the world every year. Its early diagnosis, management and proper treatment play an important role in reducing complications and mortality. One of the support tools in early diagnosis, treatment and management of this disease are Clinical Decision Support System (CDSS), which are divided into two groups, rule-based and non-rule-based. Rule-based decision support systems are created based on clinical guidelines, while non-rule-based decision support systems use machine learning. In this research, the effects of decision support systems, rule-based and non-rule-based, on cancer diagnosis, treatment and management were measured.
Materials and Methods: The present study was conducted using a systematic review method, which was conducted by searching the Web of Science, Scopus, IEEE and PubMED databases until 12/31/2021. After removing duplicates and evaluating the characteristics of the inclusion and exclusion criteria, studies related to the goal were selected. The selection of articles was based on the title, abstract and full text The data collection tool was the data extraction form, which included year of study, type of study, system of body, organ of body, the service provided by the decision support system, type of decision support system, effect, effect index and the score of effect index. Narrative synthesis were used for data analysis.
Results: Out of 768 articles, 16 articles related to the objectives of the study were identified. Studies were presented in two categories of clinical decision-support systems: Rule-based and non-Rule based. The effects evaluated in the clinical decision support systems were Rule-based, dose adjustment, symptoms, adherence to treatment guidelines, care time, smoking, need for chemotherapy and pain management, all of which except pain management were significant and positive. The effects evaluated were in the category of non-Rule based clinical decision support systems, diagnostic and therapeutic decisions, controlling neutropenia, all of which were significant and positive except controlling neutropenia.
Conclusion: The results obtained for the effectiveness of both Rule-based and non-Rule-based decision support systems indicated different benefits of these two categories. Therefore, using their combination in the field of cancer can bring very useful results.

Factors Affecting the Acceptance and Use of Hospital Intelligent Management (HIM) System based on the Unified Theory of Acceptance and Use of Technology (UTAUT)
Pezhman Sadeghi, Nader Jahanmehr, Reza Rabiei,
Volume 19, Issue 5 (12-2025)
Abstract
Background and Aim: Information systems serve different purposes in organizational and management hierarchies. The hospital intelligent management system is an analytical and decision-support management information system that provides information and important performance indicators for managers in hospitals. Considering the role of this system in increasing the efficiency and effectiveness and the lack of academic hospitals having the desired level of productivity, this research was conducted to investigate the effective factors in improving the acceptance of the intelligent hospital management system in the hospitals of Shahid Beheshti University of Medical Sciences (SBMU). 
Materials and Methods: This descriptive and correlational research was conducted in 19 hospitals (12 teaching hospitals and 7 non-teaching hospitals) of Shahid Beheshti University of Medical Sciences in 2022. In this study, 126 senior and middle managers and experts of the productivity committee participated. The data of this study were collected Using the Unified Theory of Acceptance and Use of Technology(UTAUT)  Questionnaire and for statistical analysis, SPSS software (statistical table and linear and multiple regression tests, sequencing, and chi-square) was used. The validity of the questionnaire was determined using the opinion of research experts and its reliability was also determined using Cronbach’s alpha coefficient (0.824).
Results: Most of the participants in the study were from teaching hospitals (63.2%) and were middle managers (50.8%). Behavioral intentions were identified as the most important factors in the use of system by senior and middle managers and experts of productivity committee (P<0.001). The effort expectancy had the greatest impact on the intention to use the system as compared to the expected components of Performance expectancy and social influence. Also, training and having educational programs on how to use the HIM and its applications can increase the intention and use of the HIM by employees (P<0.001).
Conclusion: Based on the results, the effect of the moderating variables in this study was insignificant. If senior managers and influential people encourage working with the system, and employees also make more effort to learn the system, and working with the system meets their expectations, employees will be willing to use the system. In other words, employees use the system when they believe that this system is user-friendly, valuable, and useful for them.

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