JOURNAL OF HEALTH MANAGEMENT AND INFORMATICS
Year:2021 | Volume:8 | Issue:1|Papers Count:8

Introduction: Economic growth has a direct impact on public health expenditures; also, it indirectly affects public health expenditures through the environment’ s quality. Therefore, this study aimed to investigate the relationship between environmental performance index, economic growth, and public health expenditures in countries with high and very high human development index. Methods: The present descriptive-analytical and applied study was performed on 16 countries with high and very high human development index. The time-series data required for the years 2006-2018 were extracted from the World Bank and United Nations database and environmental performance index extracted from the Yale University website. Im, Pesaran and Shin (IPS), Levin, Lin, and Chu (LLC), Augmented Dickey– Fuller (ADF)– Fisher, and Phillips-Perron (PP)– Fisher tests for stationary and Pedroni and Kao tests for cointegration were used. The study model was estimated by the DOLS cointegration method in Eviews 10 software. Results: The mean environmental performance index for selected countries with very high and high human development index was 79. 04 and 64. 71, respectively; also, the elasticity of public health expenditures to gross national production, environmental performance index, physician supply, and urbanization ratio were 0. 96,-2. 41, 0. 441 and 0. 448, respectively. Conclusion: Increasing economic growth, urbanization ratio, and physician supply had a positive effect, and improving environmental performance index had a negative effect on public health expenditures. Therefore, to reduce public health expenditures, policies are recommended to maintain environmental sustainability and reduce environmental pollutants, and to invest in advanced equipment to purify pollutant gases. Maintaining and increasing economic growth is also essential for adopting policies to increase physicians and invest in health infrastructure.

Introduction: Reclassification risk in the health insurance market happens when premium prices are determined based on the health level. It is necessary for insurance applicants to manage this risk due to uncertainty about the individual’ s health status in later periods. Guaranteed renewable insurance fully covers this risk because the health level is not taken into account in calculating the premiums. This study is an attempt to calculate the welfare benefits resulting from the coverage of this risk by providing guaranteed renewable insurance in this market. Methods: The economic welfare model in the form of computable general equilibrium has been used to measure welfare. The model is calibrated by the data of social accounting matrix and national health accounts in 2011. Social accounting matrix is extracted based on the latest input-output table for the economy of Iran presented in this year. Results: The results show that, in general, the more guaranteed renewable insurance expands in the health insurance market, the greater the welfare effects will be; therefore, the elimination of basic insurance from this market and provision of the same insurance for all people in the form of guaranteed renewable insurance (complete elimination of reclassification risk) can increase economic welfare up to 6%. Conclusion: Reclassification risk management by providing guaranteed renewable insurance in the health insurance market of Iran, due to increasing the welfare of the insured, will lead to the provision of a unit insurance plan and equal access to health services for all.

Introduction: One of the important factors that may affect the destination of development is medical and healthcare tourism. The presence of experts in medical tourism and the geographical location of Iran is one of the most important factors in the sustainable development of Iran. This study aimed to identify and prioritize the components of the medical and healthcare tourism entrepreneurship ecosystem (EE) in Iran with a structuralinterpretive modeling approach. Methods: This research is methodologically a mixed (qualitative-quantitative) exploratory type, and its participants were health tourism experts and entrepreneurs. The method for selecting the participants was Purposeful sampling. 15 participants were interviewed based on theoretical saturation. The data collection tools were interviews and questionnaires. Also, to analyze the data in this research, we used two overlapping processes of open and axial coding and structural-interpretive modeling. Results: The results of data analysis showed that the components of the health tourism EE were included in the seven main dimensions of law, regulations and governance factors, financing and investment, the role of culture, influential institutions (universities and Educational centers), influential regional market factors, the role of human capital, and effective infrastructure. Conclusion: Researchers examining entrepreneurial ecosystems have not studied the role of a regional level of analysis. The results of qualitative analysis and Interpretive Structural Modeling (ISM) showed that to create the entrepreneurial ecosystem of the medical and healthcare tourism, the central factors resulting from qualitative research interacted with each other at three different levels, and the set of factors at these levels caused the medical sciences university to move towards creating an Entrepreneurial ecosystem drive in the university.

Introduction: Trauma patients are potentially at high risk of acquiring infections in hospitals, which is the main cause of in-hospital mortality. The aim of this study was to identify the risk factors contributing to death from hospital-acquired infections in trauma patients by data mining techniques. Methods: This is a cohort study. A total of 549 trauma patients with nosocomial infection who were admitted to Shiraz trauma hospital between 2017 and 2018 were studied. Sex, age, mechanism of injury, body region injured, injury severity score, length of stay, type of intervention, infection day after admission, microorganism cause of infections, and the outcomes were collected. Association rule mining techniques were applied to extract knowledge from the data set. The IBM SPSS Modeler data mining software version 18. 0 was used as a tool for data mining of the trauma patients with hospital queried infections database. Results: The age older than 65, surgical site infection skin, bloodstream infection, mechanism injury of car accident, invasive intervention of tracheal intubation, injury severity score higher than 16, and multiple injuries with higher than 71 percent confidence level were associated with in-hospital mortality. The relationship between those predicators and death among hospital-acquired infection was strong (Lift value >1). Conclusion: Factors such as increasing age, tracheal intubation, mechanical ventilator, surgical site infection skin, upper respiratory infection are associated with death from hospital-acquired infections in trauma patients by data mining.

Introduction: Public higher education is competing for limited public funds. Activity-based costing (ABC) provides detailed evidence that higher education administrators and policy-makers can be employed to allocate scare resources more effectively and better understand what education centers do. Conducting context-specific studies on ABC and budgeting for educational systems is the crux of the matter for cost containment and making decisions. The present study was undertaken with the aim of determining the costs of training undergraduate and postgraduate students. Methods: This is a descriptive-analytic and applied study. The costs incurred by 7 different disciplines and degrees including bachelor (n=2), master (n=4), and PhD (n=1) in the School of Management and Medical Information Sciences of the Shiraz University of Medical Science in the academic year 2015-16 were examined and costs of training undergraduate and postgraduate students were totaled by ABC method. The total number of students in included disciplines was 269; of them, 71% were studying in the bachelor, 26% in the master, and 3% in PhD programs. Since the primary purpose of our study was to calculate the total sum of cost per student, no sampling was done. After identifying the activity centers and incurred costs per activity center, the proportion of the schools’ costs to the university headquarter was traced. In the school level, the costs of non-faculty staff by the deputies of education, research, support, and cultural-student affairs were estimated. Moreover, other costs, namely energy costs, rentals, consumables, depreciation, and missions were determined and assigned based on the number of students. Data management and analysis were performed using Excel 2007. Results: The cost of training undergraduate students in the disciplines of health services management and health information technology was $24413± 2891 and $24286± 2926, respectively. The maximum cost of schooling a student in the master degree belonged to the discipline of medical informatics. The total cost of training a PhD student in the academic year 2014-2015 was $95303± 16106. Conclusion: In an era of resource scarcity, the ability to recognize the gaps between resources and academic goals and redirect the resources into programs which maximize the value added is crucial for all higher education institutes.

ntroduction: Job crafting is the process of making proactive changes in the boundaries composing a job, which are known as mental fences that individuals adopt to define their job’ s physical, emotional, or cognitive limitations. Job crafting considers the change in the nature of jobs, whether realistically in the form of task crafting and relational crafting, or as cognitive perceptions. In this study, the role of self-efficacy as the antecedent and work engagement as the consequence of job crafting was studied. Methods: The jobs were academic and the sample was selected from faculty members of Shiraz University of Medical Sciences. The research questionnaires were distributed among faculty members of Shiraz University of Medical Sciences. A PLS model is analyzed and interpreted in two stages: the assessment of the reliability and validity of the measurement model, and the assessment of the structural model. Results: The results showed that self-efficacy was positively related to all dimensions of job crafting. Moreover, the triple dimensions of job crafting had a significant positive effect on work engagement. The moderating role of gender and academic level in the relationship between self-efficacy and work engagement was confirmed. However, the results showed that gender did not moderate the relationship between self-efficacy and task crafting. Conclusion: The main novelty of this research is the study of job crafting, self-efficacy and engagement variables considering the moderating role of gender and academic level.

Introduction: The purpose of this research is to provide an appropriate framework for implementing IT management services in the field of pre-hospital emergencies with an integrated approach of COBIT maturity model and ITIL framework. Methods: In a qualitative part, experts familiar with the field of pre-hospital emergency and information technology were purposefully selected. In the quantitative phase of the statistical community, we included experts in the field of information technology management who are also experts in the field of emergency, as well as university professors who worked in the field of emergency and senior and middle managers in the field of pre-hospital emergency entered the community. Considering the limitations of the community and the purposefulness of the selection of individuals to enter the community, 915 individuals were selected as a sample. To select a sample in the quantitative section, Morgan table was used. They were selected by simple random method using software. To collect information, we first reviewed the texts and articles in the field of ITIL and COBIT and then the extracted codes in this category were reviewed and an overview of the research was obtained; then, in the qualitative part the interview method and in the quantitative part the researchermade questionnaire were used. To analyze the data in the qualitative section, we used MAX QDA software to review and categorize the information. Then, in the quantitative section, the researcher-made questionnaire was collected and finally the model was fitted using confirmatory factor analysis. Results: In the end, it was concluded that the main components such as management, organization, processes, eyes, size, goals of the organization, staff, monitoring and evaluation, support, organization, information architecture and service delivery and their subcomponents were the main factors that should be paid special attention in the field of prehospital emergency management. Conclusion: To be more successful in implementing the organization’ s framework, it must identify the most important problems and then create a controllable domain to implement service support processes in the organization. The selected processes should be strongly and clearly supported by the general management of the organization. A codified and specific plan for implementation should be developed. A coordinated and planned approach for design, implementation should be specified and after the implementation of the mentioned processes. After expressing the output measurement indicators of the processes, the outputs should be measured and based on the changes that exist, these changes should be considered and returned to the planning stage to re-formulate the steps.

Distinguishing P300 signals from other components of the EEG is one of the most challenging issues in Brain Computer Interface (BCI) applications, and machine learning methods have vastly been utilized as effective tools to perform such separation. Although in recent years deep neural networks have significantly improved the quality of the above detection, the significant similarity between P300 and other components of EEG in parallel with their unrepeatable nature have led to P300 detection, which are still an open problem in BCI domain. In this study, a novel architecture is proposed in order to detect P300 signal among EEG, in which the temporal learning concept is engaged as a new substructure inside the main Convolutional Neural Network (CNN). The above Temporal Convolutional Network (TCN) may better address the problem of P300 detection, thanks to its potential in involving time sequence properties in modelling of these signals. The performance of the proposed method is evaluated on the EPFL BCI dataset, and the obtained results are compared in two inter-subject and intra-subject scenarios with the results of classical CNN in which temporal properties of input are not considered. Increased True Positive Rate of the proposed method (an average of 4 percent) and its accuracy (an average of 2. 9 percent) in parallel with the decrease in its False Positive Rate (averagely 3. 1 percent) shows the effectiveness of the TCN structure in promoting the detection procedure of P300 signals in BCI applications.