A Bibliometric Analysis of Health Cloud Scientific\u27s Productions

Introduction: Cloud computing is an innovative paradigm meeting the user\u27s demand for accessing a shared source comprising adjustable computational sources, such as servers and applied programs. An increase in the costs of information technology, emerging problems with updating software and hardware, and expanded storage volume, make it possible to utilize cloud-based health information cases. Organizations have focused on cloud platform-based services as a new opportunity to develop the software industry for healthcare. The aim of the research is to conduct a bibliometric study of the scientific productions on health cloud . Methodology: The present study, applied in nature, was conducted using a bibliometric and scientometric method. It was conducted in 2018 using PubMed and key portmanteaus over the period 2009-2018. Subjected to the application of input and output standards, 491 research papers were selected for analysis. Findings: The findings revealed that the production of health cloud-focused papers over a decade, excluding those in 2017, had an upward trend. The US, India, and China were the most productive in this respect. Having presented 5 papers on cloud computing, Costa, Lee, Malamateniou, Stoicu-Tivadar, Vassilacopoulos, writers, were most productive. The greatest co-occurrence was that of the words Internet, electronic health records, computer security, information storage and retrieval, algorithms, confidentiality, female, male, delivery of health care, computer communication networks, medical informatics, mobile applications, data mining, and health information exchang. Conclusion: The results of the present study indicate the leading status of the USA in health cloud publications. In view of the recognition received for using cloud computing, the trend of the papers in the base was upward in nature. On analysis of the co-occurrence of words, the largest cluster was that of cloud computing with 6 items focused on: The Internet of Things (IoT), Electronic health record, healthcare, and e-health in one cluster, indicating the continuity of the issues

A Bibliometric Analysis of Health Cloud Scientific\u27s Productions

Introduction: Cloud computing is an innovative paradigm meeting the user\u27s demand for accessing a shared source comprising adjustable computational sources, such as servers and applied programs. An increase in the costs of information technology, emerging problems with updating software and hardware, and expanded storage volume, make it possible to utilize cloud-based health information cases. Organizations have focused on cloud platform-based services as a new opportunity to develop the software industry for healthcare. The aim of the research is to conduct a bibliometric study of the scientific productions on health cloud . Methodology: The present study, applied in nature, was conducted using a bibliometric and scientometric method. It was conducted in 2018 using PubMed and key portmanteaus over the period 2009-2018. Subjected to the application of input and output standards, 491 research papers were selected for analysis. Findings: The findings revealed that the production of health cloud-focused papers over a decade, excluding those in 2017, had an upward trend. The US, India, and China were the most productive in this respect. Having presented 5 papers on cloud computing, Costa, Lee, Malamateniou, Stoicu-Tivadar, Vassilacopoulos, writers, were most productive. The greatest co-occurrence was that of the words Internet, electronic health records, computer security, information storage and retrieval, algorithms, confidentiality, female, male, delivery of health care, computer communication networks, medical informatics, mobile applications, data mining, and health information exchang. Conclusion: The results of the present study indicate the leading status of the USA in health cloud publications. In view of the recognition received for using cloud computing, the trend of the papers in the base was upward in nature. On analysis of the co-occurrence of words, the largest cluster was that of cloud computing with 6 items focused on: The Internet of Things (IoT), Electronic health record, healthcare, and e-health in one cluster, indicating the continuity of the issues

Co-authorship network analysis of Iranian medical science researchers: A social network analysis

Background and aim: Co-authorship networks of scientists exhibit a pattern of developing and complicated networks. The aim of the present study was to analyze the power structure in co-authorship networks of Iranian medical science researchers based on centrality measures. Material and methods: Social network analysis was used as the research method. The research population was all those researchers who have published articles in one of the seven journals of Iranian medical sciences indexed by ISI. Data were collected in two phases, first electronically access to the articles and second using a questioner to gather opinions of authors with centrality roles. Pearson correlation and regression were used to analyze the data. Findings: The research finding showed that a significant correlation existed between centrality scores and productivity at P= 0.001. The findings from variance regression analysis revealed that researchers’ productivity variable was determined by factors such as degree, eigenvector and beta centrality. Most important criteria for selecting research teams from opinions of researcher with high centrality Scores are: the same proficiency, having dominant teams, having a necessary knowledge, political, cultural and scientific acceptance. Conclusion: The results showed that co-authorship networks of Iranian medical journals had a low centrality and few connections existed among authors. In addition, authors with high centrality scores have a quick access to other authors and resources and they regarded as powerful authors

Analysis of Search Engines and Meta Search Engines\\\' Position by University of Isfahan Users Based on Rogers\\\' Diffusion of Innovation Theory

The present study investigated the analysis of search engines and meta search engines adoption process by University of Isfahan users during 2009-2010 based on the Rogers' diffusion of innovation theory. The main aim of the research was to study the rate of adoption and recognizing the potentials and effective tools in search engines and meta search engines adoption among University of Isfahan users. The research method was descriptive survey study. The cases of the study were all of the post graduate students of the University of Isfahan. 351 students were selected as the sample and categorized by a stratified random sampling method. Questionnaire was used for collecting data. The collected data was analyzed using SPSS 16 in both descriptive and analytic statistic. For descriptive statistic frequency, percentage and mean were used, while for analytic statistic t-test and Kruskal-Wallis non parametric test (H-test) were used. The finding of t-test and Kruscal-Wallis indicated that the mean of search engines and meta search engines adoption did not show statistical differences gender, level of education and the faculty. Special search engines adoption process was different in terms of gender but not in terms of the level of education and the faculty. Other results of the research indicated that among general search engines, Google had the most adoption rate. In addition, among the special search engines, Google Scholar and among the meta search engines Mamma had the most adopting rate. Findings also showed that friends played an important role on how students adopted general search engines while professors had important role on how students adopted special search engines and meta search engines. Moreover, results showed that the place where students got the most acquaintance with search engines and meta search engines was in the university. The finding showed that the curve of adoption rate was not normal and it was not also in S-shape. Morover, among simple and advanced pages of Google, a specific bias toward simple search pages could be seen in users. This part of the study confirmed Rogers’ theory. Other results of the study indicated that renouncement of innovation was not only in decision phase. This part of the investigation was in conflict with Rogers’ theory