Journal of

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Background: As in many countries, Medical Education (ME) is offered in three levels including Undergraduate ME, Graduate ME, and Continuing ME. Information theology development has provided a suitable chance for ME. E-learning in ME is growing more and more. The present study seeks to determine the key success factors (KSF) in E-learning in medical fields.
Material and Methods: KSF has been scrutinized in the literature following of which, and due to similarity, a clas­sification with seven groupings was established including institutional factor, technology, interested parties, information knowledge, methods and approaches educational resources, and environmental factors. Through a questionnaire, the data were gathered from the information technology (IT) direc­tors in all medical universities throughout the country. The data collected were subjected to facto­rial analysis. Data from heads of educational groups were obtained through focus group discussion. Cronbach reliability coefficient was calculated for questionnaire used. Factorial analysis was used to identify meaningful KSF. T-Test, and one-way variance analysis as well as Pearson's correlation were used. The analysis was conducted with SPSS software.
Results: The preparedness factors were analyzed through group discussions with the heads of the academic departments under the study. By factorial analyses, five factors were found. Fisher Exeact Test was used to compare the obtained ratios in 5% curve whose results showed that among the three factors including legal and technical environ­ment, specialized hardware and software, and high speed internet, performance interest and potentials showed a significant difference (p=0.002). A p=0.011 was found for the authorities' interest and finan­cial and non-financial rewards. No other significant differences were found anywhere else.
Conclusion: Appropriate strategies to coordinate and aligned with the conditions that must be taken, including some of them can be cited : Document Perspective drawn by the Ministry of Health, Content production (medical, etc.) to the appropriate shape, Develop technical and communications infrastructure, First e-learning development in the field of basic science And then as a complementary training in Clinical Science, Develop and build information literacy skills among teachers and students And encourage them in this area, Platforms and create the appropriate structures and interactions necessary, Despite the virtual library, Drawing rules for the protection of creators and owners of content rights education, Culture correct and appropriate, Private sector participation in developing e-learning and ..so on

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Background: Increaseing efficiency and effectiveness is the ultimate goal of staff training. Determining and being aware of staff training`s results efficiency is the necessity of learning transfer process and environmental identification which can complete the training cycle and leads to more effective plans and training activities. This study is aimed to predict the relationship between the aspects of learning environment and transmission of learning in Milad Hospital from clinical staff`s viewpoints. Materials & Methods: In this study, 306 people from different hospital's wards were selected by random sampling. The data were collected using Bartram et al learning questionnaire and researcher-made questionnaire of training transmission.The data were analyzed using Pearson correlation test and regression analysis. Results: There is a significant relationship between learning environment and learning transmission with correlation coefficient of 0.604 and coefficient of determination of. 0.604 with positive direction. About 0.60% of training transfer can be clarified by learning environment. Conclusion: Identifying the elements of learning environment is essential to enhance learning transfer to workplace. Learning transfer can lead to the development of organizational learning and sharing skills in order to optimize quality of services provided for clients and patients.

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Background: The organizational learning has been influenced in different theories and model based on theoretical and practical dimensions in organizations development and provides a favorable context for changing and development. Organizational learning capacity can play a main role in clinical governance implemention.
Materials and Methods: This study was a descriptive- analitical and cross-sectional one which performed during the first six months of 2014. Study population included staff of Ardabil Social Security hospital. One hundred and seventy participants selected using simple random sampling. A four dimensional standard questionnaire of Gumejeet et al  and a seven dimensional self administrated questionnaire were conducted to examine organizational learning capacity and clinical governance assessment, respectively. Data analysis was carried out using Pierson Correlation Coefficient and Mulivariate regression analysis. Data was analyzed by SPSS₁₈ software.

Results: Study results revealed that there was a positive and significant relation between organizational learning capacity and clinical governance implementation (R= 0.507). This correlation coefficient was 0.644 in management commitment, 0.498 in systematic approach, 0.446 in open climate and 0.261 in knowledge transfer.

Conclusion: According to the main role of organizational learning on implementing clinical governance, providing an essential background to enforce organizational learning capacity in four components especially management commitment and systematic approach to implement efficient clinical governance is recommended.

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Background: Concerning to the importance of team learning and process change, the clinical laboratories employees should be familiar with organizational learning. This study aimed at assessing and determining organizational learning capability among clinical laboratories employees of Tehran University of Medical Sciences hospitals.

Materials and Methods: This study was a descriptive-analytical and cross-sectional one which conducted among 85 employees of clinical laboratories using Cochran formula at five general teaching hospitals of Tehran University of Medical Sciences. The research instrument was Gomez et al. questionnaire consisted of four dimensions such management commitment, systematic approach, open climate and knowledge transfer in 17 questions. Five point Likert scale was used to rank questions. SPSS software 19 version was utilized to data analysis using t- test, Pearson Correlation Coefficient and Welch method.

Results: The average score of organizational learning among employees of studied clinical laboratories was 3.11 which showed relative desirable situation. Also, management commitment as an element of organizational learning had significant difference among the clinical laboratories (p=0.002). There was a significant relationship between employees education level with knowledge transfer and integration capability (p=0.04).

Conclusion: The process of organizational learning capability of the studied clinical laboratories was not desirable. Therefore, in order to complete establishment of organizational learning in clinical laboratories, should pay attention to some elements such establishment of patient safety system, initiation of error registration system and encouraging employees to report the errors

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Background: This study aimed at investigating the structural relationship between intellectual capital, psychological empowerment, and organizational learning with staff performance of Tehran University of Medical Sciences in the form of a causal model.
Materials and Methods: The research method was correlational type.  Data gathering was performed using four major scales including Benitez intellectual capital, Spreitzer Psychological empowerment, Neefe organizational learning, and Hersey & Goldsmith performance. A sample 450 of persons was selected using stratified method to test hypothesis and fitness of the proposed model. Data analysis performed using SPSS version 24 and Smart PLS software.
Results: Data analysis indicated that measurement model, structural model and total model were fit. Intellectual capital, psychological empowerment, organizational learning had impact directly on performance 0.22, 0.30 and 0.45, respectively and the intellectual capital and psychological empowerment have indirectly impact on performance 0.27 and 0.26 respectively.
Conclusion: Intellectual capital, psychological empowerment, and organizational learning have impact on performance directly. Also, Intellectual capital and psychological empowerment have impact on performance indirectly. Regarding to study finding, tailored programs and processes to promote Intellectual capital, psychological empowerment, and organizational learning status recommended.
 

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Introduction: In the present era, the implementation of open innovation process is necessary for any organization, and hospitals as the main medical centers are no exception. In fact, hospitals, as health operational units, are always directly exposed to changes in the field of health services. Therefore, the present study has investigated the factors affecting open innovation in public hospitals in Guilan province.
Methods: From the point of view of purpose, this research is an applied study and in terms of data collection, it is in the category of descriptive research, which was conducted cross-sectionally in 1400. The statistical population of the study was 1600 senior managers and staff of public hospitals in Guilan province. For sampling, a non-randomized judgmental sampling method was used to access community members (senior managers and employees with at least a bachelor's degree). 250 questionnaires were used to perform the test. Data collection tools are standard questionnaires. The reliability of the questionnaire was confirmed through Cronbach's alpha, and the validity of the questionnaires was confirmed through the face and content validity, convergent and divergent validity. The structural equation modeling method has been used to test the research hypotheses. The software used in this research is SPSS 26 and Smart PLS3.
Results: The results of the structural equation modeling test showed that among the external factors, cooperation with partners and the user, among the internal organizational factors, organizational structure, exploratory learning, and organizational culture, and finally among the individual internal factors, organizational motivation affects the open innovation of public hospitals in Guilan province. The impact of trust between partners, technology, personality traits, and knowledge on hospital open innovation has been rejected.
Conclusion: In collaboration with other health care services, universities, and users, hospitals should develop appropriate policies to transition from a closed innovation system to open innovation, and support effective measures in this regard.

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Background and Objectives
In recent years, medicine supply chain management has become more significant, especially after the Covid-19 pandemic. The most important issue is supply chain cost control. If the drug inventory is not properly managed, it will lead to issues such as the lack of inventory of certain drugs, provision of excess inventory, increased costs, and, finally, patient dissatisfaction.
Materials and Methods
In this study, an attempt has been made to predict and manage the pharmaceutical needs of hospitals using an efficient deep-learning algorithm. The drug consumption data for ten years of Besat General Hospital in Hamedan are extracted from the HIS database. As a case study, the accuracy of the predictive model is evaluated, especially for cefazolin. We use a deep model to analyze the medical time-series data efficiently. This model consists of a Long Short-Term Memory network, which can sufficiently recognize the change history in time-series prediction applications. The proposed model with many adjustable parameters in the deep architecture will bring good performance to overcome the complexities of the learning problem.
Results
Using the deep learning method can increase robustness by reducing the effects of complexity and uncertainty in medical data. The average forecasting error for the proposed method is 0.043, and the measured values for RMSE, MAE, and R2  are 0.335, 0.260, and 0.851, respectively.
Conclusion
A comprehensive comparison between some other predictive methods and the implemented model shows the outperformance of the proposed approach. Additionally, the evaluation results indicate the efficiency of the proposed approach.