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Showing 25 results for Abbasi
Reza Dehkhodaei, Mazyar Karamali, Mohammad Mohammadian, Mohammadkarim Bahadori, Mohsen Abbasifarajzadeh,
Volume 17, Issue 6 (2-2024)
Volume 17, Issue 6 (2-2024)
Abstract
Background and Aim: Considering the importance of knowledge management in the current era and the emphasis on the implementation of knowledge management in the health system in the knowledge management system of the Ministry of Health, Treatment and Medical Education, and since it is one of the first steps in the implementation of knowledge management, Drawing the knowledge tree of the organization, the purpose of the current research is to review the process of publishing the knowledge tree and examine it in the field of health.
Materials and Methods: The current research is a type of applied and descriptive review study that was carried out with the method of scientometrics and co-occurrence analysis of keywords. For this purpose, the term “Knowledge tree” OR “knowledge trees” was searched in Scopus reference database. For data analysis, the analyzes provided by the database itself were used, and VOSviewer software was used to visualize the data.
Results: The growth of scientific productions related to the field of the tree of knowledge in general in the mentioned base has started since 1979 and has had a growing trend until 2023. In the field of knowledge tree, among the authors Yang, Y, among the organizations, machine intelligence institute, iona college, and among the countries, China, America, and England have been at the top of the most productive in this field. Among the subjects, the most related articles are primarily related to the field of computer science (32.2 percent) and then to the field of engineering (22.1 percent) and mathematics (10.1 percent), which is significant. that medicine is in the sixth place and this is a sign of the weakness of producing resources in this field and the clustering resulting from the co-occurrence of keywords led to the identification of five clusters respectively with the titles of data mining and information processing, artificial intelligence and expert systems. , knowledge structure and decision support systems, semantics and knowledge representation, and finally learning and teaching systems.
Conclusion: The study of the thematic structure of scientific productions in the field of Knowledge Tree showed that the field of health has a weakness in the production of resources in this field. Therefore, it is necessary for future researches to pay special attention to the development and explanation of this concept and modeling its drawing, especially in the health system, in order to identify and prevent diseases.
Ali Mohammad Mosadeghrad, Mahdiyeh Heydari, Mahya Abbasi, Mahdi Abbasi,
Volume 18, Issue 2 (5-2024)
Volume 18, Issue 2 (5-2024)
Abstract
Background and Aim: Health insurance organizations play an important role in increasing people’s access to health services and protecting them financially against catastrophic health costs. Iran Health Insurance Organization (IHIO) is one of the largest public health insurance organizations in Iran, which faces many challenges. The purpose of this research is the strategic analysis of health financing performance of IHIO.
Materials and Methods: This qualitative research was conducted using interpretive phenomenology method through using semi-structured interviews with 25 managers and employees of IHIO. In addition, relevant documents and archival data of IHIO were collected and analyzed. Thematic analysis method was used to analyze the data.
Results: Overall, 19 strengths, 24 weaknesses, 14 opportunities, 37 threats and 43 solutions were identified for the health financing system of IHIO. Increasing the coverage of health services, correcting the information databases of the insured and electronic prescribing were the most important strengths, and inappropriate pooling of financial resources, incomplete risk pooling, high administrative costs, and inefficient control were the most important weaknesses of IHIO. The most important opportunities for IHIO include the government’s support for universal health coverage and emphasis on primary health care, legal support for consolidating health insurance funds and improving the health technology assessment system in the country. The main threats to IHIO include political and economic unstability, low health insurance premiums, decisions without scientific support and insufficient enforcement of laws. Finally, solutions such as modernizing the tax system, increasing the health literacy of the community, reducing bureaucracy, increasing transparency and accountability, and reforming the monitoring and evaluation system were identified to strengthen the performance of the financing system of IHIO.
Conclusion: Iran’s health insurance organization is facing numerous structural, contexual and process challenges that have reduced its productivity. Decrease in revenues, increase in costs and decrease in efficiency have caused problems in the financing performance of this organization. Recognizing the weaknesses and challenges of financing performance and applying corrective interventions is the first step in strengthening the sustainability of health financing of IHIO.
Materials and Methods: This qualitative research was conducted using interpretive phenomenology method through using semi-structured interviews with 25 managers and employees of IHIO. In addition, relevant documents and archival data of IHIO were collected and analyzed. Thematic analysis method was used to analyze the data.
Results: Overall, 19 strengths, 24 weaknesses, 14 opportunities, 37 threats and 43 solutions were identified for the health financing system of IHIO. Increasing the coverage of health services, correcting the information databases of the insured and electronic prescribing were the most important strengths, and inappropriate pooling of financial resources, incomplete risk pooling, high administrative costs, and inefficient control were the most important weaknesses of IHIO. The most important opportunities for IHIO include the government’s support for universal health coverage and emphasis on primary health care, legal support for consolidating health insurance funds and improving the health technology assessment system in the country. The main threats to IHIO include political and economic unstability, low health insurance premiums, decisions without scientific support and insufficient enforcement of laws. Finally, solutions such as modernizing the tax system, increasing the health literacy of the community, reducing bureaucracy, increasing transparency and accountability, and reforming the monitoring and evaluation system were identified to strengthen the performance of the financing system of IHIO.
Conclusion: Iran’s health insurance organization is facing numerous structural, contexual and process challenges that have reduced its productivity. Decrease in revenues, increase in costs and decrease in efficiency have caused problems in the financing performance of this organization. Recognizing the weaknesses and challenges of financing performance and applying corrective interventions is the first step in strengthening the sustainability of health financing of IHIO.
Kourosh Abbasiyan, Mohammad Alimoradnori, Mohammad Bagher Karami,
Volume 18, Issue 2 (5-2024)
Volume 18, Issue 2 (5-2024)
Abstract
Background and Aim: Managers, as the main decision-makers in facing various internal and external organizational problems, play a significant and determining role in the success or even failure of an organization. If competent and experienced managers are positioned at the top of organizations, the success of these organizations in achieving their goals will be guaranteed and an organization can achieve maximum efficiency with minimal resources. The aim of this study was to design a model of managerial competencies for hospital managers.
Materials and Methods: This qualitative research was conducted from year 2020 to 2022. After reviewing studies related to the topic, the extracted competencies were given to 19 experts consisted of relevant academic faculty members and managers with experience in the healthcare system and hospitals. Eventually, a managerial competency model was formulated through the use of the Delphi method and expert panel discussions. Collected data were analyzed in Excel software.
Results: The developed model in this research for the concept of hospital managers’ competencies includes 33 managerial competencies of hospital managers in four main management functions (planning, organizing, leadership and control) and managerial roles, which starts from literature review and performing two Delphi steps and implementing two expert panel plans. In the competency of hospital managers model, the planning dimension consists 4 components, organizing consists 4 components, leadership consists 12 components, control consists 4 components, and managerial roles consists 9 components. Strategic thinking, which is a subset of planning, has the highest weight (0.495) and highest rank among other components, and continuous improvement, which is a subset of managerial roles, has the lowest weight (0.033) and lowest rank among other components.
Conclusion: This study proposes an exclusive and comprehensive model, utilizing practical techniques as a suitable solution for evaluating the managerial competencies of hospital managers. The proposed framework in this study can serve as a standard performance assessment tool for evaluating managers.
Materials and Methods: This qualitative research was conducted from year 2020 to 2022. After reviewing studies related to the topic, the extracted competencies were given to 19 experts consisted of relevant academic faculty members and managers with experience in the healthcare system and hospitals. Eventually, a managerial competency model was formulated through the use of the Delphi method and expert panel discussions. Collected data were analyzed in Excel software.
Results: The developed model in this research for the concept of hospital managers’ competencies includes 33 managerial competencies of hospital managers in four main management functions (planning, organizing, leadership and control) and managerial roles, which starts from literature review and performing two Delphi steps and implementing two expert panel plans. In the competency of hospital managers model, the planning dimension consists 4 components, organizing consists 4 components, leadership consists 12 components, control consists 4 components, and managerial roles consists 9 components. Strategic thinking, which is a subset of planning, has the highest weight (0.495) and highest rank among other components, and continuous improvement, which is a subset of managerial roles, has the lowest weight (0.033) and lowest rank among other components.
Conclusion: This study proposes an exclusive and comprehensive model, utilizing practical techniques as a suitable solution for evaluating the managerial competencies of hospital managers. The proposed framework in this study can serve as a standard performance assessment tool for evaluating managers.
Fatemeh Abbasi Ghaletaki, Maryam Kazerani, Azam Shahbodaghi,
Volume 19, Issue 1 (4-2025)
Volume 19, Issue 1 (4-2025)
Abstract
Background and Aim: Hospital library services are among the basic infrastructures for promoting e-health readiness. This study evaluated the components of e-health readiness in hospital libraries in Isfahan.
Materials and Methods: This is an applied-descriptive survey. The statistical population is 10 government hospital libraries in Isfahan city. The questionnaire completers are the managers of the aforementioned libraries. The research tool is a researcher-made questionnaire that was prepared by a deep and comprehensive review of related literature. The 61-question questionnaire is based on the Likert scale and has four sections: learning readiness (R1), core readiness (R2), social readiness (R3), and technology readiness (R4). To determine the content validity of the questionnaire, the opinions of professors, specialists, and experts were used and its validity was confirmed. Its reliability was confirmed using Cronbach’s alpha of 0.83. Descriptive statistics were used to examine the data.
Results: Isfahan government hospital libraries are in a good state in terms of learning readiness with a score of 3.77. They were in a moderate state in terms of core readiness with a score of 3.49. Social readiness with a score of 2.47 and technology readiness with a score of 2.48 were reported as poor state. “Literacy level of technology and services related to health care” component with a score of 2.9 from the core readiness, “reimbursement policies” component with a score of 1 from the social readiness, “resources training” component with a score of 1.8 from the learning readiness, and “organization access to ICT education” component with a score of 1.35 from the technology readiness were identified as weak components. In general, all government hospital libraries in Isfahan are in a moderate state in terms of e-health readiness.
Conclusion: Hospital libraries face various challenges in joining the e-health category, including a lack of readiness in the technology sector. The lack of appropriate policies for implementing e-health in libraries, lack of users’ skills in using information and communication technology, users’ ignorance of the e-health services needed in the library, lack of professional human resources, and lack of e-health-related training for users are some of the weaknesses of hospital libraries in the e-health readiness sector.
Materials and Methods: This is an applied-descriptive survey. The statistical population is 10 government hospital libraries in Isfahan city. The questionnaire completers are the managers of the aforementioned libraries. The research tool is a researcher-made questionnaire that was prepared by a deep and comprehensive review of related literature. The 61-question questionnaire is based on the Likert scale and has four sections: learning readiness (R1), core readiness (R2), social readiness (R3), and technology readiness (R4). To determine the content validity of the questionnaire, the opinions of professors, specialists, and experts were used and its validity was confirmed. Its reliability was confirmed using Cronbach’s alpha of 0.83. Descriptive statistics were used to examine the data.
Results: Isfahan government hospital libraries are in a good state in terms of learning readiness with a score of 3.77. They were in a moderate state in terms of core readiness with a score of 3.49. Social readiness with a score of 2.47 and technology readiness with a score of 2.48 were reported as poor state. “Literacy level of technology and services related to health care” component with a score of 2.9 from the core readiness, “reimbursement policies” component with a score of 1 from the social readiness, “resources training” component with a score of 1.8 from the learning readiness, and “organization access to ICT education” component with a score of 1.35 from the technology readiness were identified as weak components. In general, all government hospital libraries in Isfahan are in a moderate state in terms of e-health readiness.
Conclusion: Hospital libraries face various challenges in joining the e-health category, including a lack of readiness in the technology sector. The lack of appropriate policies for implementing e-health in libraries, lack of users’ skills in using information and communication technology, users’ ignorance of the e-health services needed in the library, lack of professional human resources, and lack of e-health-related training for users are some of the weaknesses of hospital libraries in the e-health readiness sector.
Atefeh Abbasi, Somayeh Nasiri, Sayyed Mostafa Mostafavi, Abbas Habibolahi,
Volume 19, Issue 4 (11-2025)
Volume 19, Issue 4 (11-2025)
Abstract
Background and Aim: Neonatal hypoxic-ischemic encephalopathy (HIE) is a clinical syndrome characterized by impaired brain function resulting from oxygen deprivation and reduced cerebral blood flow. Developing predictive models can serve as valuable tools for physicians in forecasting disease outcomes and facilitating early interventions. The present study was conducted with the aim of constructing a predictive model for neonatal hypoxic-ischemic encephalopathy using data mining algorithms.
Materials and Methods: This applied study was conducted using a descriptive approach. In the first stage, the factors influencing the prediction of neonatal hypoxic-ischemic encephalopathy were identified through expert surveys. In the second stage, data pertaining to 4,000 neonates were collected from the Iman system, available in the database of the Ministry of Health and Medical Education, during the years 2020–2021. Following preprocessing, a dataset comprising 3,962 records with 13 features was extracted. Subsequently, predictive models were developed using algorithms including artificial neural networks, decision tree variants, random forest, support vector machines, logistic regression, and Bayesian networks. Model construction was performed using the Python programming language within the Anaconda environment. Finally, performance evaluation and comparison were carried out using metrics such as accuracy, precision, specificity, F1-score, and the Area Under the Curve (AUC).
Results: The findings of the study revealed that the Area Under the Receiver Operating Characteristic Curve (AUROC) for models developed using logistic regression, artificial neural networks, random forest, Bayesian networks, support vector machines, and decision trees were 86%, 86%, 84%, 82%, 76%, and 74%, respectively. The highest performance was achieved by the logistic regression algorithm, with an accuracy of 81%, sensitivity of 85%, and specificity of 96%. The greatest sensitivity was observed in logistic regression, artificial neural networks, and support vector machines, whereas the naïve Bayesian algorithm demonstrated the lowest performance metrics. In the predictive model for hypoxic-ischemic encephalopathy, the most influential feature was the first-minute Apgar score, while the least influential factor was delivery outside the hospital.
Conclusion: The findings of the present study indicated that the predictive model for neonatal hypoxic-ischemic encephalopathy based on the logistic regression algorithm demonstrated superior performance. It is anticipated that the application of practical data-driven algorithms for neonates with hypoxic-ischemic encephalopathy will play a crucial role in the rapid identification of the condition and the provision of appropriate treatment. Such approaches can enable healthcare professionals to act within the critical window of opportunity, thereby improving the quality of care, preventing disease progression, and reducing the severity of adverse outcomes.
Materials and Methods: This applied study was conducted using a descriptive approach. In the first stage, the factors influencing the prediction of neonatal hypoxic-ischemic encephalopathy were identified through expert surveys. In the second stage, data pertaining to 4,000 neonates were collected from the Iman system, available in the database of the Ministry of Health and Medical Education, during the years 2020–2021. Following preprocessing, a dataset comprising 3,962 records with 13 features was extracted. Subsequently, predictive models were developed using algorithms including artificial neural networks, decision tree variants, random forest, support vector machines, logistic regression, and Bayesian networks. Model construction was performed using the Python programming language within the Anaconda environment. Finally, performance evaluation and comparison were carried out using metrics such as accuracy, precision, specificity, F1-score, and the Area Under the Curve (AUC).
Results: The findings of the study revealed that the Area Under the Receiver Operating Characteristic Curve (AUROC) for models developed using logistic regression, artificial neural networks, random forest, Bayesian networks, support vector machines, and decision trees were 86%, 86%, 84%, 82%, 76%, and 74%, respectively. The highest performance was achieved by the logistic regression algorithm, with an accuracy of 81%, sensitivity of 85%, and specificity of 96%. The greatest sensitivity was observed in logistic regression, artificial neural networks, and support vector machines, whereas the naïve Bayesian algorithm demonstrated the lowest performance metrics. In the predictive model for hypoxic-ischemic encephalopathy, the most influential feature was the first-minute Apgar score, while the least influential factor was delivery outside the hospital.
Conclusion: The findings of the present study indicated that the predictive model for neonatal hypoxic-ischemic encephalopathy based on the logistic regression algorithm demonstrated superior performance. It is anticipated that the application of practical data-driven algorithms for neonates with hypoxic-ischemic encephalopathy will play a crucial role in the rapid identification of the condition and the provision of appropriate treatment. Such approaches can enable healthcare professionals to act within the critical window of opportunity, thereby improving the quality of care, preventing disease progression, and reducing the severity of adverse outcomes.
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