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Mr Kasra Dolatkhahi, Adel Azar, Tooraj Karimi, Mohammad Hadizadeh,
Volume 15, Issue 4 (10-2021)
Volume 15, Issue 4 (10-2021)
Abstract
Background and Aim: Cancer and in particular Breast cancer are among the diseases that have the highest mortality rate in Iran after heart disease. The accurate prognosis for Breast cancer is important, and the presence of various symptoms and features of this disease makes it difficult for doctors to diagnose. This study aimed to identify the factors affecting Breast cancer, modeling and ultimately diagnosing the risk of Breast cancer.
Materials and Methods: In the present study, first, by content analysis and library studies, the effective factors in Breast cancer were identified, then with the help of a team of experts consisting of physicians and subspecialists in Breast oncology and Breast surgery; With the help of the Delphi method, the factors were adjusted and 26 final factors that were numerically correct and string based on local and climatic conditions were approved. Then, according to the final factors and based on the medical records of 5208 patients in the Cancer Research Center of Shahid Beheshti University of medical sciences, to diagnose cancer, Decision Tree, Random Forest, and Support Vector Machine methods were used as machine learning methods.
Results: In the first step, by content analysis method, 29 effective factors in Breast cancer were identified. Then, taking into account the indigenous and climatic conditions and using the Delphi method and also using the opinions of 18 Experts during three years, 26 factors were finalized. In the final step, using the medical records of the patients and the results obtained from the three methods mentioned, random forest, had the highest accuracy of 94.75% and precision of 97.26% in diagnosing Breast cancer. It has been noted that, compared to other similar studies, indigenous databases have been exploited, the accuracy obtained has been very close to previous studies, and in many cases much better.
Conclusion: Using the random forest method and taking advantage of the factors affecting Breast cancer, the ability to diagnose cancer has been provided with greatest accuracy.
Materials and Methods: In the present study, first, by content analysis and library studies, the effective factors in Breast cancer were identified, then with the help of a team of experts consisting of physicians and subspecialists in Breast oncology and Breast surgery; With the help of the Delphi method, the factors were adjusted and 26 final factors that were numerically correct and string based on local and climatic conditions were approved. Then, according to the final factors and based on the medical records of 5208 patients in the Cancer Research Center of Shahid Beheshti University of medical sciences, to diagnose cancer, Decision Tree, Random Forest, and Support Vector Machine methods were used as machine learning methods.
Results: In the first step, by content analysis method, 29 effective factors in Breast cancer were identified. Then, taking into account the indigenous and climatic conditions and using the Delphi method and also using the opinions of 18 Experts during three years, 26 factors were finalized. In the final step, using the medical records of the patients and the results obtained from the three methods mentioned, random forest, had the highest accuracy of 94.75% and precision of 97.26% in diagnosing Breast cancer. It has been noted that, compared to other similar studies, indigenous databases have been exploited, the accuracy obtained has been very close to previous studies, and in many cases much better.
Conclusion: Using the random forest method and taking advantage of the factors affecting Breast cancer, the ability to diagnose cancer has been provided with greatest accuracy.
Zahra Aghasizadeh, Ali Reza Pouya, Nasser Motahari Farimani, Ali Vafeaa Najjar,
Volume 16, Issue 1 (3-2022)
Volume 16, Issue 1 (3-2022)
Abstract
Background and Aim: Hospitals are the most important component of the health system and accurate evaluation of their performance is important. So far, much research has been done on the evaluation of hospitals using DEA models, but in these studies, organizations are considered as a black box and system processes and relationships between them are ignored. In this study, the efficiency of hospitals was evaluated using network envelopment analysis and its results were compared with simple envelopment analysis.
Materials and Methods: The method of the present research was ptactical and the nature of the survey was descriptive. The research population was all hospitals and educational centers affiliated to Mashhad University of Medical Sciences with a capacity of more than one hundred beds, which included twelve public hospitals and forty-eight sections. To collect information, methods of observing and studying documents, records and statistics of hospital activities have been used. For validation, by calculating Spearman correlation coefficient, it was found that the proposed model has a significant correlation with the Black Box DEA Model and the validity of the model was confirmed. SOLVER DEA and EXCEL software were used to implement the model.
Results: The results show that by considering the internal departments of the organization as well as the relations between the departments, a more accurate analysis of the efficiency of the hospitals would be done and we will have a better separation in the ranking between the organizations. Also, by using the network DEA model, the overall efficiency, the efficiency of each department and the rank of each department in comparison with similar departments in other hospitals are determined.
Conclusion: The framework presented in this study can be an appropriate criterion for measuring the efficiency of hospitals and their internal sections by determining the overall position of each hospital relative to other hospitals and by determining the efficiency of the section. By determining the efficiency of the internal departments of hospitals, a suitable priority is provided for allocating resources and investing in different departments in the direction of organizational improvement.
Materials and Methods: The method of the present research was ptactical and the nature of the survey was descriptive. The research population was all hospitals and educational centers affiliated to Mashhad University of Medical Sciences with a capacity of more than one hundred beds, which included twelve public hospitals and forty-eight sections. To collect information, methods of observing and studying documents, records and statistics of hospital activities have been used. For validation, by calculating Spearman correlation coefficient, it was found that the proposed model has a significant correlation with the Black Box DEA Model and the validity of the model was confirmed. SOLVER DEA and EXCEL software were used to implement the model.
Results: The results show that by considering the internal departments of the organization as well as the relations between the departments, a more accurate analysis of the efficiency of the hospitals would be done and we will have a better separation in the ranking between the organizations. Also, by using the network DEA model, the overall efficiency, the efficiency of each department and the rank of each department in comparison with similar departments in other hospitals are determined.
Conclusion: The framework presented in this study can be an appropriate criterion for measuring the efficiency of hospitals and their internal sections by determining the overall position of each hospital relative to other hospitals and by determining the efficiency of the section. By determining the efficiency of the internal departments of hospitals, a suitable priority is provided for allocating resources and investing in different departments in the direction of organizational improvement.
Leila Shahmoradi, Niloofar Kheradbin, Ahmad Reza Farzanehnejad, Niloofar Mohammadzadeh, Atefeh Ghanbari Jolfaei,
Volume 16, Issue 2 (5-2022)
Volume 16, Issue 2 (5-2022)
Abstract
Background and Aim: Identifying risk factors is recommended as the first step for depression management in children and adolescents. This study aims to determine the data elements required for developing a clinical decision support system for screening major depression in young people.
Materials and Methods: This research was a descriptive-analytical study. The research population included a variety of mental health specialists that were both psychologists and students in psychiatry and guidance & counseling majors as well as electronic databases including Scopus, Pubmed, Embase, PsychInfo, WOS and Clinical key. The data collection tool was a questionnaire designed in three main sections which was answered by a convenient sample of 8 people who were specialists in the field. To analyze the extracted data Content Validity Ratio (CVR) and Mean measures were calculated for each item in questionnaire. Content Validity Index (CVI) and Cronbach’s Alpha (using SPSS software) were calculated which were equal to 0.74 and 0.824 respectively which confirmed validity and reliability of the research tool.
Results: According to Lawshe’s table, data elements with CVR between 0 and 0.75 and Mean less than 1.5, like “Ethnicity and race” (CVR=-0.25, Mean=1.125), were rejected. Items such as “Gender” (CVR=0.5) with a CVR equal to or less than 0.75, as well as items with a CVR between 0 and 0.75 and a Mean equal to or more than 1.5, like “Marital status” (CVR=0.5, Mean=1.625) were retained and considered to be included as the minimum data set for screening major depression in ages 10 to 25 years. Data elements were categorized in three categories: Demographic, Clinical and Psychosocial
Conclusion: Clinical decision support systems can facilitate providing healthcare at different levels such as screening major depression. These systems can be used for screening major depression risk factors to improve accessibility to mental health practitioners, assure the implementation of guidelines and provide a common language between different levels of healthcare. Determining the minimum data set for screening major depression in ages 10 to 25 years, is the first step toward developing a clinical decision support system for screening individuals for major depression.
Materials and Methods: This research was a descriptive-analytical study. The research population included a variety of mental health specialists that were both psychologists and students in psychiatry and guidance & counseling majors as well as electronic databases including Scopus, Pubmed, Embase, PsychInfo, WOS and Clinical key. The data collection tool was a questionnaire designed in three main sections which was answered by a convenient sample of 8 people who were specialists in the field. To analyze the extracted data Content Validity Ratio (CVR) and Mean measures were calculated for each item in questionnaire. Content Validity Index (CVI) and Cronbach’s Alpha (using SPSS software) were calculated which were equal to 0.74 and 0.824 respectively which confirmed validity and reliability of the research tool.
Results: According to Lawshe’s table, data elements with CVR between 0 and 0.75 and Mean less than 1.5, like “Ethnicity and race” (CVR=-0.25, Mean=1.125), were rejected. Items such as “Gender” (CVR=0.5) with a CVR equal to or less than 0.75, as well as items with a CVR between 0 and 0.75 and a Mean equal to or more than 1.5, like “Marital status” (CVR=0.5, Mean=1.625) were retained and considered to be included as the minimum data set for screening major depression in ages 10 to 25 years. Data elements were categorized in three categories: Demographic, Clinical and Psychosocial
Conclusion: Clinical decision support systems can facilitate providing healthcare at different levels such as screening major depression. These systems can be used for screening major depression risk factors to improve accessibility to mental health practitioners, assure the implementation of guidelines and provide a common language between different levels of healthcare. Determining the minimum data set for screening major depression in ages 10 to 25 years, is the first step toward developing a clinical decision support system for screening individuals for major depression.
Mostafa Shanbehzadeh, Hadi Kazemi-Arpanahi, Raoof Nopour,
Volume 16, Issue 2 (5-2022)
Volume 16, Issue 2 (5-2022)
Abstract
Background and Aim: Breast cancer is one of the most common and aggressive malignancies in women. Timely diagnosis of breast cancer plays an important role in preventing the progression of this disease, timely treatment measures, and aftermath reducing the mortality rate of these patients. Machine learning has the potential ability to diagnose diseases quickly and cost-effectively. This study aims to design a CDSS based on the rules extracted from the decision tree algorithm with the best performance to diagnose breast cancer in a timely and effective manner.
Materials and Methods: The data of 597 suspected people with breast cancer (255 patients and 342 healthy people) were retrospectively extracted from the electronic database of Ayatollah Taleghani Hospital in Abadan city with 24 characteristics, mainly pertained to lifestyle and medical histories. After selecting the most important variables by using the Chi-square Pearson and one-way analysis of variance (P<0.05), the performance of selected data mining algorithms including RF, J-48, DS, RT and XG -Boost was evaluated for breast cancer diagnosis in Weka 3.4 software. Finally, the breast cancer diagnostic system was designed based on the best model and through C# programming language and Dot Net Framework V3.5.4.
Results: Fourteen variables including personal history of breast cancer, breast sampling, and chest X-ray, high blood pressure, increased LDL blood cholesterol, presence of mass in upper inner quadrant of the breast, hormone therapy with estrogen, hormone therapy with Estrogen-progesterone, family history of breast cancer, age, history of other cancers, waist-to-hip ratio and fruit and vegetable consumption showed a significant relationship with the output class at the P<0.05. Based on the results of the performance evaluation of selected algorithms, the RF model with sensitivity, specificity, accuracy, and F- measure equal to 0.97, 0.99, 0.98, 0.974, respectively, AUC=0.936 had higher performance than other selected algorithms and was suggested as the best model for breast cancer diagnosis.
Conclusion: It seems that using modifiable variables such as lifestyle and reproductive-hormonal characteristics as input to the RF algorithm to design the CDSS, can detect breast cancer cases with optimal accuracy. In addition, the proposed system can be effectively adapted in real clinical environments for quick and effective disease diagnosis.
Materials and Methods: The data of 597 suspected people with breast cancer (255 patients and 342 healthy people) were retrospectively extracted from the electronic database of Ayatollah Taleghani Hospital in Abadan city with 24 characteristics, mainly pertained to lifestyle and medical histories. After selecting the most important variables by using the Chi-square Pearson and one-way analysis of variance (P<0.05), the performance of selected data mining algorithms including RF, J-48, DS, RT and XG -Boost was evaluated for breast cancer diagnosis in Weka 3.4 software. Finally, the breast cancer diagnostic system was designed based on the best model and through C# programming language and Dot Net Framework V3.5.4.
Results: Fourteen variables including personal history of breast cancer, breast sampling, and chest X-ray, high blood pressure, increased LDL blood cholesterol, presence of mass in upper inner quadrant of the breast, hormone therapy with estrogen, hormone therapy with Estrogen-progesterone, family history of breast cancer, age, history of other cancers, waist-to-hip ratio and fruit and vegetable consumption showed a significant relationship with the output class at the P<0.05. Based on the results of the performance evaluation of selected algorithms, the RF model with sensitivity, specificity, accuracy, and F- measure equal to 0.97, 0.99, 0.98, 0.974, respectively, AUC=0.936 had higher performance than other selected algorithms and was suggested as the best model for breast cancer diagnosis.
Conclusion: It seems that using modifiable variables such as lifestyle and reproductive-hormonal characteristics as input to the RF algorithm to design the CDSS, can detect breast cancer cases with optimal accuracy. In addition, the proposed system can be effectively adapted in real clinical environments for quick and effective disease diagnosis.
Saman Mohammadpour, Reza Rabiei, Elham Shabahrami, Kamyar Fathisalari, Maryam Khakzad, Mostafa Langarizadeh,
Volume 16, Issue 2 (5-2022)
Volume 16, Issue 2 (5-2022)
Abstract
Background and Aim: Cancer is the second leading cause of death in the world, which leads to the death of more than 10 million people in the world every year. Its early diagnosis, management and proper treatment play an important role in reducing complications and mortality. One of the support tools in early diagnosis, treatment and management of this disease are Clinical Decision Support System (CDSS), which are divided into two groups, rule-based and non-rule-based. Rule-based decision support systems are created based on clinical guidelines, while non-rule-based decision support systems use machine learning. In this research, the effects of decision support systems, rule-based and non-rule-based, on cancer diagnosis, treatment and management were measured.
Materials and Methods: The present study was conducted using a systematic review method, which was conducted by searching the Web of Science, Scopus, IEEE and PubMED databases until 12/31/2021. After removing duplicates and evaluating the characteristics of the inclusion and exclusion criteria, studies related to the goal were selected. The selection of articles was based on the title, abstract and full text The data collection tool was the data extraction form, which included year of study, type of study, system of body, organ of body, the service provided by the decision support system, type of decision support system, effect, effect index and the score of effect index. Narrative synthesis were used for data analysis.
Results: Out of 768 articles, 16 articles related to the objectives of the study were identified. Studies were presented in two categories of clinical decision-support systems: Rule-based and non-Rule based. The effects evaluated in the clinical decision support systems were Rule-based, dose adjustment, symptoms, adherence to treatment guidelines, care time, smoking, need for chemotherapy and pain management, all of which except pain management were significant and positive. The effects evaluated were in the category of non-Rule based clinical decision support systems, diagnostic and therapeutic decisions, controlling neutropenia, all of which were significant and positive except controlling neutropenia.
Conclusion: The results obtained for the effectiveness of both Rule-based and non-Rule-based decision support systems indicated different benefits of these two categories. Therefore, using their combination in the field of cancer can bring very useful results.
Materials and Methods: The present study was conducted using a systematic review method, which was conducted by searching the Web of Science, Scopus, IEEE and PubMED databases until 12/31/2021. After removing duplicates and evaluating the characteristics of the inclusion and exclusion criteria, studies related to the goal were selected. The selection of articles was based on the title, abstract and full text The data collection tool was the data extraction form, which included year of study, type of study, system of body, organ of body, the service provided by the decision support system, type of decision support system, effect, effect index and the score of effect index. Narrative synthesis were used for data analysis.
Results: Out of 768 articles, 16 articles related to the objectives of the study were identified. Studies were presented in two categories of clinical decision-support systems: Rule-based and non-Rule based. The effects evaluated in the clinical decision support systems were Rule-based, dose adjustment, symptoms, adherence to treatment guidelines, care time, smoking, need for chemotherapy and pain management, all of which except pain management were significant and positive. The effects evaluated were in the category of non-Rule based clinical decision support systems, diagnostic and therapeutic decisions, controlling neutropenia, all of which were significant and positive except controlling neutropenia.
Conclusion: The results obtained for the effectiveness of both Rule-based and non-Rule-based decision support systems indicated different benefits of these two categories. Therefore, using their combination in the field of cancer can bring very useful results.
Ali Mawla Gawwam Al Meyyah, Hamid Jaddoa Abbas, Reza Afrisham, Nahid Einollahi,
Volume 17, Issue 4 (10-2023)
Volume 17, Issue 4 (10-2023)
Abstract
Background and Aim: Diabetes is a metabolic disorder characterized by an elevated blood glucose level, resulting from impairments in insulin action, insulin secretion, or both; which causes abnormalities in the metabolism of carbohydrates, protein, and lipids. Chronic hyperglycemia is associated with long-term damage, dysfunction, and failure of various organs. Adropin and irisin are newly described proteins that can be an essential component in the pathophysiological pathways of diabetes mellitus. The current study was designed to evaluate Irisin and Adropin biochemical markers in patients with type 2 diabetes mellitus and their correlation with risk factors.
Materials and Methods: A case control study, that included 90 patients of type 2 diabetes mellitus and 90 healthy individuals, who matched for both age and sex with patients. Fasting blood sugar (FBS), HbA1c, serum insulin, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG), irisin and adropin were measured at the chemistry laboratory of AL-Faihaa teaching Hospital by standard methods.
Results: Serum irisin (8.154±1.642 vs. 14.06±3.916 ng/ml) and adropin (25.39±8.897 vs. 59.43±8.768 pg/ml) levels were significantly lower in the patient group than in the control cases, respectively (P.value<0.0001). Serum adropin levels were significantly and positively correlated with age (r=0.236, P=0.025) and negatively with BMI (r=-0.209, P=0.048). While, serum irisin levels were significantly and negatively correlated with TG (r=-0.248, P=0.018). Based on ROC analysis, the AUC for irisin was 0.937 (95% CI: 0.906-0.969), which showed a sensitivity of 91.1% and a specificity of 80.0% at the cut-off of 9.715 (P<0.0001). In addition, the AUC for adropin was 0.991 (95% CI: 0.980-1.00), which showed a sensitivity of 100.0% and a specificity of 91.1% for this biomarker at a cut-off of 37.945 (P<0.0001).
Conclusion: Our findings showed that the serum levels of irisin and adropin were lower in the patient group than in the control group. Probably, the reduction of adropin and irisin may be used as a biomarker to predict the risk of T2DM, which requires more studies in this regard.
Fatemeh Soofiabadi, Alireza Shahraki, Mohebali Rahdar,
Volume 18, Issue 3 (7-2024)
Volume 18, Issue 3 (7-2024)
Abstract
Background and Aim: Given the high sensitivity of the medical field, a mistake can cause irreparable damage to human society. For this reason, finding the symptoms of the disease and the relationships between them to facilitate the improvement of diseases is inevitable. Therefore, the aim of the present study was to first identify the symptoms of neurofibromatosis type 1 by specialists, then determine the relationship between the symptoms and the degree of their impact on each other in order to determine the most important criterion in improving the disease.
Materials and Methods: The present study is of a developmental-applied type in terms of its purpose and of a descriptive-survey type in terms of its data collection method. The case study of the present study is spinal disorders, of which neurofibromatosis type 1 has been diagnosed as one of them based on the opinion of experts. Neurofibromatosis type 1 is a genetic disorder that causes tumors in the nervous tissue. Accordingly, in the present study, the criteria, which are the symptoms of the disease, were first determined using the opinion of a group of experts and the implementation of the fuzzy Delphi method. In the next step, a model for the causal relationships between the symptoms of the disease is presented. For this purpose, a fuzzy cognitive map is drawn using MATLAB, FCMapper and Pajek software, then backward and forward scenarios are presented for neurofibromatosis type 1 and the disease improvement scenario is determined.
Results: The results showed that hormonal changes, flat brown spots on the skin, freckles in the armpit and groin area, soft bumps on the face or under the skin, high blood pressure, respiratory problems, bumps on the iris of the eye (Lish nodules), tumor in the optic nerve-ocular glioma, short stature, bone deformity, learning disabilities-attention deficit hyperactivity disorder (ADHD) and larger than average head size are ranked first to twelfth, respectively. The causal relationships between the symptoms showed that the criterion of hormonal changes has the greatest impact on the criterion of freckles in the armpit or groin area; Therefore, if the hormonal changes criterion improves, neurofibromatosis type 1 will also improve.
Conclusion: The findings of this study have helped the medical community to have a better understanding of the symptoms of the disease so that doctors can improve their prevention and care recommendations based on the severity of the symptoms of the diseases.
Materials and Methods: The present study is of a developmental-applied type in terms of its purpose and of a descriptive-survey type in terms of its data collection method. The case study of the present study is spinal disorders, of which neurofibromatosis type 1 has been diagnosed as one of them based on the opinion of experts. Neurofibromatosis type 1 is a genetic disorder that causes tumors in the nervous tissue. Accordingly, in the present study, the criteria, which are the symptoms of the disease, were first determined using the opinion of a group of experts and the implementation of the fuzzy Delphi method. In the next step, a model for the causal relationships between the symptoms of the disease is presented. For this purpose, a fuzzy cognitive map is drawn using MATLAB, FCMapper and Pajek software, then backward and forward scenarios are presented for neurofibromatosis type 1 and the disease improvement scenario is determined.
Results: The results showed that hormonal changes, flat brown spots on the skin, freckles in the armpit and groin area, soft bumps on the face or under the skin, high blood pressure, respiratory problems, bumps on the iris of the eye (Lish nodules), tumor in the optic nerve-ocular glioma, short stature, bone deformity, learning disabilities-attention deficit hyperactivity disorder (ADHD) and larger than average head size are ranked first to twelfth, respectively. The causal relationships between the symptoms showed that the criterion of hormonal changes has the greatest impact on the criterion of freckles in the armpit or groin area; Therefore, if the hormonal changes criterion improves, neurofibromatosis type 1 will also improve.
Conclusion: The findings of this study have helped the medical community to have a better understanding of the symptoms of the disease so that doctors can improve their prevention and care recommendations based on the severity of the symptoms of the diseases.
Leila Keikha, Fatemeh Sheikhshoaei, Abdolahad Nabiolahi, Mahnaz Khosravi,
Volume 18, Issue 4 (10-2024)
Volume 18, Issue 4 (10-2024)
Abstract
Background and Aim: Health librarians can play an important role in meeting the information needs of the clinical team and improving the quality of medical cares. Increasing clinical health literacy and use of Evidence-based medicine among ophthalmology residents is of great importance due to the importance of patients’ health in this field and appropriate decision-making about the individual’s health status. The present study aimed to evaluate the effect of an educational intervention by clinical librarians on the skills of ophthalmology residents in using of evidence-based information at Zahedan University of Medical Sciences.
Materials and Methods: This was a semi-experimental applied study. The research population was ophthalmology residents of Al-Zahra Eye Hospital, Zahedan University of Medical Sciences during the years 2020-2023, who were selected through a census. During a three-month period, 17 combined training sessions (face-to-face and virtual using the Navid system) were held for 18 ophthalmology residents regarding correct search methods from different databases and appropriate use of evidence-based information. To collect data before and after training, a clinical information literacy questionnaire derived from previous studies was used, and data analysis was performed using SPSS software and ANOVA and ANCOVA statistical tests to compare scores before and after training in the intervention group.
Results: The majority of participating residents (55.6%) were female. Before the intervention, 33.3% of the study population had moderate to high levels of knowledge about evidence-based medicine. There was a statistically significant relationship between the total level of knowledge of residents after training and gender (P-value<0.05). Clinical librarian training was effective on the level of basic knowledge of evidence-based medicine, designing clinical questions, searching for clinical evidence, critical evaluation of clinical evidence, and dissemination of evidence-based medical information of residents (P-value<0.05).
Conclusion: Considering the positive impact of clinical librarians’ intervention in improving the level of clinical decision-making knowledge of ophthalmology residents, it is suggested that evidence-based medicine training workshops or courses be held for residents of different disciplines using a variety of educational methods. In addition, it is suggested that evidence-based units be included in the residents’ curriculum and that training be conducted as a team consisting of medical librarians and specialists and ophthalmologist.
Materials and Methods: This was a semi-experimental applied study. The research population was ophthalmology residents of Al-Zahra Eye Hospital, Zahedan University of Medical Sciences during the years 2020-2023, who were selected through a census. During a three-month period, 17 combined training sessions (face-to-face and virtual using the Navid system) were held for 18 ophthalmology residents regarding correct search methods from different databases and appropriate use of evidence-based information. To collect data before and after training, a clinical information literacy questionnaire derived from previous studies was used, and data analysis was performed using SPSS software and ANOVA and ANCOVA statistical tests to compare scores before and after training in the intervention group.
Results: The majority of participating residents (55.6%) were female. Before the intervention, 33.3% of the study population had moderate to high levels of knowledge about evidence-based medicine. There was a statistically significant relationship between the total level of knowledge of residents after training and gender (P-value<0.05). Clinical librarian training was effective on the level of basic knowledge of evidence-based medicine, designing clinical questions, searching for clinical evidence, critical evaluation of clinical evidence, and dissemination of evidence-based medical information of residents (P-value<0.05).
Conclusion: Considering the positive impact of clinical librarians’ intervention in improving the level of clinical decision-making knowledge of ophthalmology residents, it is suggested that evidence-based medicine training workshops or courses be held for residents of different disciplines using a variety of educational methods. In addition, it is suggested that evidence-based units be included in the residents’ curriculum and that training be conducted as a team consisting of medical librarians and specialists and ophthalmologist.
Elham Maserat, Zeinab Mohammadzadeh, Zahra Mahmoudvand, Hasan Siamian, Pourya Taghizadeh, Azadeh Yazdanian,
Volume 19, Issue 5 (12-2025)
Volume 19, Issue 5 (12-2025)
Abstract
Background and Aim: As a pandemic, the COVID-19 epidemic has had widespread impacts on society and has highlighted the need for effective management through timely case detection, early isolation, and treatment. Web portals have emerged as an effective information technology intervention and a solution for crisis management. This study aims to review various web portals implemented in the context of COVID-19.
Materials and Methods: In 2025, a systematic review was conducted to identify articles related to the use of web portals in the COVID-19 context. Keywords such as information technology, portal, COVID-19, and university were used to search multiple databases and search engines including Scopus, PubMed, Science Direct, Web of Knowledge, Ovid Medline, and Google Scholar. Published texts from 2019 to 2025 were included in the search.
Results: Initially, 1,058 articles were retrieved, and after careful evaluation, 40 articles directly relevant to the research topic were selected for inclusion. The analysis identified several notable web portals deployed during the COVID-19 pandemic, including platforms such as COVIDome, Over COVID, interactive visualization portals, country-specific information portals, prediction-based systems, electronic portals for specific medical conditions, data platforms, drug repurposing portals, patient triage and scheduling tools, health mapping portals, telemetry capabilities, and epidemiology applications. The results showed that the highest number of related articles were published in 2020, primarily concentrated in the United States, Saudi Arabia, and Canada. In-depth reviews indicated that WPs such as COVIDome and MyChart significantly facilitated patient access to medical information and healthcare services. These portals not only provided timely information regarding vaccination and outbreaks but also played a crucial role in facilitating effective communication between patients and Healthcare Providers. Furthermore, the overall use of portals increased 10-fold during the pandemic, a trend that persisted afterward. Findings also highlight existing digital divides, as individuals with higher education and income levels benefited more from these portals.
Conclusion: Successful implementation of web portals requires proper management and planning, increased awareness among stakeholders including policymakers, healthcare professionals, and the general public, user training, comprehensive data integration, adherence to standards, and periodic evaluations. These measures are essential to optimize the effectiveness and utility of the portals.
Materials and Methods: In 2025, a systematic review was conducted to identify articles related to the use of web portals in the COVID-19 context. Keywords such as information technology, portal, COVID-19, and university were used to search multiple databases and search engines including Scopus, PubMed, Science Direct, Web of Knowledge, Ovid Medline, and Google Scholar. Published texts from 2019 to 2025 were included in the search.
Results: Initially, 1,058 articles were retrieved, and after careful evaluation, 40 articles directly relevant to the research topic were selected for inclusion. The analysis identified several notable web portals deployed during the COVID-19 pandemic, including platforms such as COVIDome, Over COVID, interactive visualization portals, country-specific information portals, prediction-based systems, electronic portals for specific medical conditions, data platforms, drug repurposing portals, patient triage and scheduling tools, health mapping portals, telemetry capabilities, and epidemiology applications. The results showed that the highest number of related articles were published in 2020, primarily concentrated in the United States, Saudi Arabia, and Canada. In-depth reviews indicated that WPs such as COVIDome and MyChart significantly facilitated patient access to medical information and healthcare services. These portals not only provided timely information regarding vaccination and outbreaks but also played a crucial role in facilitating effective communication between patients and Healthcare Providers. Furthermore, the overall use of portals increased 10-fold during the pandemic, a trend that persisted afterward. Findings also highlight existing digital divides, as individuals with higher education and income levels benefited more from these portals.
Conclusion: Successful implementation of web portals requires proper management and planning, increased awareness among stakeholders including policymakers, healthcare professionals, and the general public, user training, comprehensive data integration, adherence to standards, and periodic evaluations. These measures are essential to optimize the effectiveness and utility of the portals.
Samira Sadat Pourhosseini, Vahid Yazdi-Feyzabadi, Mohammad Hossein Mehrolhassani,
Volume 19, Issue 6 (3-2026)
Volume 19, Issue 6 (3-2026)
Abstract
Background and Aim: Identifying and transferring lessons learned from past disasters can significantly improve future disaster management performance. Although the general principles of disaster management are similar across events such as earthquakes, context-specific factors, including geographical location, scale and severity of the event, timing, and local characteristics, can shape distinct challenges and, consequently, different management approaches. Therefore, conducting case-based studies that account for the unique conditions of each disaster is essential for effective learning. This study focuses on the Kuhbanan earthquake and aims to identify management challenges arising from the specific characteristics of this region.
Materials and Methods: This study employed a qualitative design using a directed content analysis approach. The study population consisted of experts, decision-makers, and frontline practitioners directly involved in managing the Kuhbanan earthquake. Using purposive sampling with maximum variation, 15 participants were selected from key organizations, including the University of Medical Sciences, the Iranian Red Crescent Society, the Provincial Crisis Management Center, and non-governmental organizations active in rescue and relief operations. Data were collected through in-depth semi-structured interviews, with questions developed based on the dimensions of the STEEPV framework (Social, Technological, Economic, Environmental, Political, and Values). Interviews continued until data saturation was achieved. Data analysis was conducted using MAXQDA software.
Results: Data analysis led to the identification of 42 initial codes, 14 subcategories, and six categories aligned with the STEEPV framework. In the social dimension, key challenges included deficiencies in public and professional training systems (3 codes), weak intersectoral communication and coordination (6 codes), and inadequate responsiveness to community health needs (4 codes). In the technological domain, major limitations were observed in information management (2 codes) and the capacity of technological infrastructure (2 codes). From an economic perspective, financial constraints (5 codes) and welfare-related barriers (3 codes) were identified as influential factors. In the environmental dimension, specific geographical and climatic conditions including mountain topography, fault proximity, and mining operations (4 codes), along with unsuitable physical spaces for disaster management (3 codes), posed major challenges. In the political sphere, a noticeable gap between the government and the public (2 codes) and weak performance of some executive institutions (2 codes) were evident. Cultural and values-related challenges included specific local beliefs (1 code), inappropriate behaviors among communities and relief teams (2 codes), and insufficient consideration of regional culture in rescue and relief operations (3 codes).
Conclusion: This study demonstrated that despite the relatively small scale of the Kuhbanan earthquake, many structural and managerial challenges previously observed in larger disasters were repeated. This finding highlights a chronic weakness in institutional learning and process improvement within the disaster management system. The application of comprehensive analytical frameworks such as STEEPV can assist managers and planners in understanding the complexity and interconnections of different crisis dimensions, moving beyond fragmented and reactive approaches toward more informed decision-making, enhanced stakeholder coordination, and ultimately greater community resilience. It is recommended that the findings of this study be used as a roadmap for revising national disaster management policies and for designing an integrated disaster lesson-learning system.
Materials and Methods: This study employed a qualitative design using a directed content analysis approach. The study population consisted of experts, decision-makers, and frontline practitioners directly involved in managing the Kuhbanan earthquake. Using purposive sampling with maximum variation, 15 participants were selected from key organizations, including the University of Medical Sciences, the Iranian Red Crescent Society, the Provincial Crisis Management Center, and non-governmental organizations active in rescue and relief operations. Data were collected through in-depth semi-structured interviews, with questions developed based on the dimensions of the STEEPV framework (Social, Technological, Economic, Environmental, Political, and Values). Interviews continued until data saturation was achieved. Data analysis was conducted using MAXQDA software.
Results: Data analysis led to the identification of 42 initial codes, 14 subcategories, and six categories aligned with the STEEPV framework. In the social dimension, key challenges included deficiencies in public and professional training systems (3 codes), weak intersectoral communication and coordination (6 codes), and inadequate responsiveness to community health needs (4 codes). In the technological domain, major limitations were observed in information management (2 codes) and the capacity of technological infrastructure (2 codes). From an economic perspective, financial constraints (5 codes) and welfare-related barriers (3 codes) were identified as influential factors. In the environmental dimension, specific geographical and climatic conditions including mountain topography, fault proximity, and mining operations (4 codes), along with unsuitable physical spaces for disaster management (3 codes), posed major challenges. In the political sphere, a noticeable gap between the government and the public (2 codes) and weak performance of some executive institutions (2 codes) were evident. Cultural and values-related challenges included specific local beliefs (1 code), inappropriate behaviors among communities and relief teams (2 codes), and insufficient consideration of regional culture in rescue and relief operations (3 codes).
Conclusion: This study demonstrated that despite the relatively small scale of the Kuhbanan earthquake, many structural and managerial challenges previously observed in larger disasters were repeated. This finding highlights a chronic weakness in institutional learning and process improvement within the disaster management system. The application of comprehensive analytical frameworks such as STEEPV can assist managers and planners in understanding the complexity and interconnections of different crisis dimensions, moving beyond fragmented and reactive approaches toward more informed decision-making, enhanced stakeholder coordination, and ultimately greater community resilience. It is recommended that the findings of this study be used as a roadmap for revising national disaster management policies and for designing an integrated disaster lesson-learning system.
Vahideh Zarea Gavgani, Abdolrasoul Khosravi, Ali Hossein Ghasemi, Firoozeh Zare-Farashbandi, Hossein Vakilimofrad, Fatemeh Sheikhshoaei, Azra Daei,
Volume 20, Issue 1 (4-2026)
Volume 20, Issue 1 (4-2026)
Abstract
Background and Aim: Continuous revision and updating of educational programs are essential to fulfill the primary mission of higher education. Therefore, the present study aims to examine the perspectives of stakeholders regarding the necessary changes in the BSc curriculum of Medical Library and Information Science.
Materials and Methods: This study was conducted in two phases using the Fuzzy Delphi technique and a survey method. Participants included professors, students, graduates, and relevant administrators in the field of Medical Library and Information Science. The number of participants were 41 in the first phase and 122 in the second. The data collection tool was a researcher-made checklist based on the last edition of BSc curriculum of Medical Library and Information Science in the first phase, which was developed lesson by lesson. In the second phase, a researcher-made questionnaire based on the first phase data was its data collection tool. Collected data were analyzed using fuzzy numbers, defuzzification, and descriptive statistics.
Results: The findings showed that the Basic courses comprised 19 credits (9 courses), of which only 11% were deemed necessary to retain, while 78% were identified as requiring deletion or major revision. The Core Courses (mandatory) comprised 65 credits (30 courses), with 20% considered essential to retain, 43% requiring review, and 37% requiring deletion or major revision. The Non-Core/ elective Courses comprised 12 credits (6 courses), and none of the elective courses achieved the required score for retention; 50 percent required revision and the remaining 50 percent required major revision or elimination. The highest necessity for course retention from stakeholders’ viewpoints was related to Sociology of Information in basic courses, Data Structures and Programming in core courses, and Introduction to Archiving in none core courses. In the second phase, updated teaching methods, inclusion of courses on evidence-based performance, critical thinking, artificial intelligence, and practical orientation of the Research Methods course were among the key findings.
Conclusion: The results highlight the necessity of curriculum revision in BSc curriculum of Medical Library and Information Science. The curriculum of Medical Library and Information Science is expected not only to keep pace with developments in digital health and emerging technologies but also to adopt an interdisciplinary and skill-based approach. This requires changes in the design, implementation, and evaluation of the curriculum.
Materials and Methods: This study was conducted in two phases using the Fuzzy Delphi technique and a survey method. Participants included professors, students, graduates, and relevant administrators in the field of Medical Library and Information Science. The number of participants were 41 in the first phase and 122 in the second. The data collection tool was a researcher-made checklist based on the last edition of BSc curriculum of Medical Library and Information Science in the first phase, which was developed lesson by lesson. In the second phase, a researcher-made questionnaire based on the first phase data was its data collection tool. Collected data were analyzed using fuzzy numbers, defuzzification, and descriptive statistics.
Results: The findings showed that the Basic courses comprised 19 credits (9 courses), of which only 11% were deemed necessary to retain, while 78% were identified as requiring deletion or major revision. The Core Courses (mandatory) comprised 65 credits (30 courses), with 20% considered essential to retain, 43% requiring review, and 37% requiring deletion or major revision. The Non-Core/ elective Courses comprised 12 credits (6 courses), and none of the elective courses achieved the required score for retention; 50 percent required revision and the remaining 50 percent required major revision or elimination. The highest necessity for course retention from stakeholders’ viewpoints was related to Sociology of Information in basic courses, Data Structures and Programming in core courses, and Introduction to Archiving in none core courses. In the second phase, updated teaching methods, inclusion of courses on evidence-based performance, critical thinking, artificial intelligence, and practical orientation of the Research Methods course were among the key findings.
Conclusion: The results highlight the necessity of curriculum revision in BSc curriculum of Medical Library and Information Science. The curriculum of Medical Library and Information Science is expected not only to keep pace with developments in digital health and emerging technologies but also to adopt an interdisciplinary and skill-based approach. This requires changes in the design, implementation, and evaluation of the curriculum.
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