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Effective Factors on the Acceptance of Information Technology by Academic Librarians of Isfahan Based on TAM
Zahra Jalali, Hasan Ashrafi-Rizi, Mohammad Reza Soleymani, Mina Afshar,
Volume 11, Issue 4 (12-2017)
Abstract
Background and Aim: Functions and services of academic libraries have been affected very much by the entrance and the development of information technology (IT) in university libraries. Since the main mission of academic libraries is advance of educational and research programs of university, the authorities should deploy expertise with technical skills to be able to fulfill their most important job. The aim of this study was to identify factors influencing the adoption of information technology by librarians of governmental academic libraries based on the Technology Acceptance Model (TAM).
Materials and Methods: This was a survey research and the tool was a questionnaire based on TAM. The study population consisted of 151 librarians and census method was used. The validity was confirmed by experts in library and information sciences and also IT. Reliability obtained 0.89 using Cronbach's alpha. Statistical method was descriptive, inferential and data analysis was done via software SPSS20.
Results: Determination coefficient 0.282 shows that TAM is applicable in research population. This means that the applicability of the TAM was relatively appropriate for study about librarians of university libraries. Priorities effects of TAMs variables on the actual use of IT shows the most effective variable are intent to use (0.39), perceived ease of use (0.21), perceived usefulness (0.15) and attitude to use of IT (0.12).
Conclusion: Provision of required IT infrastructure and training for effective use should be considered for librarians.  In addition to that, courses of introduction to library information technology should be included in the library and information science curriculum.

The Impact of COVID-19 on Outcome Indicators of Hospitals of Shahid Beheshti University of Medical Sciences: An Interrupted Time Series Analysis
Mohammad Jalali, Ehsan Zarei, Ali Maher, Soheila Khodakarim,
Volume 16, Issue 5 (Dec 2022)
Abstract
Background and Aim:  With the outbreak of the COVID-19 pandemic, the performance of hospitals were affected, and changes were made in the utilization of hospital services. Analyzing hospital performance data during the COVID-19 pandemic can provide insights into service utilization patterns and care outcomes for managers and policymakers. This study was conducted to investigate the impact of COVID-19 on selected outcome indicators in the hospitals of Shahid Beheshti University of Medical Sciences, Tehran.
Materials and Methods: This research was descriptive-analytical and of the time series analysis type. Six outcome indicators were considered: hospitalization rate, bed occupancy rate, the average length of stay, emergency visits, laboratory tests, and imaging requests. Related data from 12 affiliated hospitals from 2017-2019 (pre-COVID) and 2020 (post-COVID) were obtained from the hospital's intelligent management system. The data were analyzed using R software's interrupted time series analysis method.
Results: The hospitalization rate (P=0.015), bed occupancy rate (P=0.04), and the number of laboratory tests (P=0.003) significantly increased immediately after the outbreak of the pandemic. In contrast, emergency visits (P=0.034) have significantly decreased. The bed occupancy rate and the number of imaging requests showed no significant change. The decrease in emergency room visits within one year after the pandemic was significant, but the changes in other outcome indicators were non-significant (P>0.05).
Conclusion: Understanding the changes and impact of a major event on hospital outcome indicators is necessary for decision-makers to effectively plan for resource allocation and effective pandemic response. The outbreak of COVID-19 has caused a change in performance and hospital outcomes by affecting the supply and demand of services. In a year after the pandemic's beginning, except for emergency visits, the other indicators have not experienced significant changes. Preservation of essential services such as emergency room visits is recommended in the strategy of rapid response to an epidemic outbreak and public campaigns to encourage people to seek medical care if needed in future waves of the pandemic.

Investigating the Role of Artificial Intelligence in Management of Diabetes in Iran: A Systematic Review
Fatemeh Bahador, Azam Sabahi, Samaneh Jalali, Fatemeh Ameri,
Volume 16, Issue 6 (Feb 2023)
Abstract
Background and Aim: Diabetes is one of the most common metabolic diseases in Iran and the fifth leading cause of death all over the world. Its spread around the world has created new methods in biomedical research, including artificial intelligence. The present study was carried out to review the studies conducted in the area of artificial intelligence and diabetes in Iran. 
Materials and Methods: This study was carried out using a systematic review method. Valid domestic databases, including Irandoc, Magiran, Sid and Google Scholar search engine, were reviewed using the keywords of artificial intelligence and diabetes in Persian both individually and in a combined manner without time limitation until June 20, 2021. A total number of 7495 articles were retrieved, which were screened in different stages (exclusion of duplicates (1824), title and summary of the articles (5884) and full text (30) and finally 20 articles that met the criteria desired by the researchers were carefully reviewed. 
Results: Among the retrieved articles, 20 articles met the inclusion criteria, of which 16 articles dealt with methods based on artificial intelligence and 4 articles dealt with the design of new systems based on artificial intelligence. Also, 10 articles examined the role of artificial intelligence in prediction, 8 articles in diagnosis, and 2 articles dealt with the control and management of diabetes. Most of the articles were related to the use of data mining methods such as artificial neural network, decision tree, etc. (16 articles). Some studies also evaluated and compared artificial intelligence methods on application, accuracy and the sensitivity of artificial intelligence in diagnosing and predicting diabetes (10 studies). 
Conclusion: A systematic review of articles revealed that the use of data mining methods for diabetes management in Iran has been associated with good progress, but there is a need to design artificial intelligence systems and algorithms and more measures should be taken in the area of diabetes control and management.

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