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Afshin Mousavi Chalak, Aref Riahi, Amin Zare,
Volume 12, Issue 1 (5-2018)
Volume 12, Issue 1 (5-2018)
Abstract
Background and Aim: Scientific journals are known as one of the basic tools in knowledge development in today's world and have a special place in publication of the newest achievements of human knowledge and science. This study aimed to evaluate Iranian journals of medical sciences in Scopus database and determine their level in the world.
Materials and Methods: This is an analytical-descriptive study with Scientometrics approach. The research population includes all Iranian journals in the field of medicine which are indexed in Scopus database until 2016. We used SPSS and Excel software to analyze data and NodeXL to draw shapes and pictures.
Results: The findings show that the number of Iranian journals increased from 2 in 1999 to 78 in 2015. Also, 15 cities and 29 centers and universities have played a role in publishing those journals. Meanwhile, the findings show that Iranian indexed journals are at a lower level compared with those of the developed and industrial countries.
Conclusion: The most important reasons for Iranian journals' growth were "the policy of Scopus to increase scientific journals", "observance of standards and compliance with international fashion and standards of medical journals”, and the like. We concluded that Iranian journals compared with those of other countries are not at a good quality position and that it is essential to have an appropriate policy by the Ministry of Health and its subordinate Universities.
Materials and Methods: This is an analytical-descriptive study with Scientometrics approach. The research population includes all Iranian journals in the field of medicine which are indexed in Scopus database until 2016. We used SPSS and Excel software to analyze data and NodeXL to draw shapes and pictures.
Results: The findings show that the number of Iranian journals increased from 2 in 1999 to 78 in 2015. Also, 15 cities and 29 centers and universities have played a role in publishing those journals. Meanwhile, the findings show that Iranian indexed journals are at a lower level compared with those of the developed and industrial countries.
Conclusion: The most important reasons for Iranian journals' growth were "the policy of Scopus to increase scientific journals", "observance of standards and compliance with international fashion and standards of medical journals”, and the like. We concluded that Iranian journals compared with those of other countries are not at a good quality position and that it is essential to have an appropriate policy by the Ministry of Health and its subordinate Universities.
Arefeh Kalavani, Maryam Kazerani, Maryam Shekofteh,
Volume 12, Issue 1 (5-2018)
Volume 12, Issue 1 (5-2018)
Abstract
Background and Aim: With the development of the Internet and databases and the increasing need to institutionalize evidence-based medicine, physicians' awareness and use of evidence-based medical databases and concepts are considered to be necessary. Therefore, the aim of this study is to evaluate the knowledge and use of evidence-based medical concepts and databases among residents of Shahid Beheshti University of Medical Sciences (SBMU).
Materials and Methods: The present study is an applied and descriptive research. The population of this study comprised 192 SBMU residents in 2016. A questionnaire was used for data collection and SPSS software was applied for data analysis.
Results: The findings showed that residents obtained 2.99 for knowledge and 2.73 for the use of evidence-based medical databases out of a total average of 5 points, which indicates that their knowledge and practical use of evidence-based medical databases are moderate. Databases about which residents have the highest knowledge and practical use are UpToDate, PubMed Clinical Queries, and
Cochrane, respectively.
Conclusion: The majority of residents at Shahid Beheshti University of Medical Sciences do not have sufficient awareness about databases and concepts of evidence-based medicine; in fact, most of the resources that are used to answer their information needs are non-evidence-based resources. Therefore, planning to accept evidence-based medicine and databases and teach them to residents is essential.
Materials and Methods: The present study is an applied and descriptive research. The population of this study comprised 192 SBMU residents in 2016. A questionnaire was used for data collection and SPSS software was applied for data analysis.
Results: The findings showed that residents obtained 2.99 for knowledge and 2.73 for the use of evidence-based medical databases out of a total average of 5 points, which indicates that their knowledge and practical use of evidence-based medical databases are moderate. Databases about which residents have the highest knowledge and practical use are UpToDate, PubMed Clinical Queries, and
Cochrane, respectively.
Conclusion: The majority of residents at Shahid Beheshti University of Medical Sciences do not have sufficient awareness about databases and concepts of evidence-based medicine; in fact, most of the resources that are used to answer their information needs are non-evidence-based resources. Therefore, planning to accept evidence-based medicine and databases and teach them to residents is essential.
Hojatollah Soleimani, Fatemeh Nooshinfard, Fahimeh Babolhavaeji,
Volume 12, Issue 1 (5-2018)
Volume 12, Issue 1 (5-2018)
Abstract
Background and Aim: To understand veterans’ needs and to make future generations familiar with the culture of self-sacrifice and martyrdom, we need a database to store information. The first step for designing a base is to provide a conceptual framework of the base. This study aims to provide a conceptual model to create the national base of veterans in Iran.
Materials and Methods: This research was conducted in a two-step, mixed approach. The first step was conducted using content analysis method (quantitative) and the second step using Delphi (qualitative) technique. Data collection tool was Excel 2016 software. With the help of Delphi technique, a researcher-made conceptual pattern was sent to the experts in three rounds. Based on their views, the final plan of national base of veterans was formed.
Results: Among the main components, introduction to the war was removed, history of war changed to history of wars, link to links, other materials to other contents, art and war to war and art, and the sub-component of possibilities turned into the main component. Veterans’ personal information turned into veterans’ database that changed into subsidiary components of the martyrs / veterans / prisoners-of-war / warriors database.
Conclusion: The main components of the conceptual pattern of national base of veterans of Iran include: home page, introduction, conflicts and operations, equipment, war zones, facilities, news, cemeteries of martyrs, veterans’ rules, questions and answers, history of wars, war and art, veterans’ database, archives, links, guide, contact with us, FAQs, other content, resources, about the base, search, map.
Materials and Methods: This research was conducted in a two-step, mixed approach. The first step was conducted using content analysis method (quantitative) and the second step using Delphi (qualitative) technique. Data collection tool was Excel 2016 software. With the help of Delphi technique, a researcher-made conceptual pattern was sent to the experts in three rounds. Based on their views, the final plan of national base of veterans was formed.
Results: Among the main components, introduction to the war was removed, history of war changed to history of wars, link to links, other materials to other contents, art and war to war and art, and the sub-component of possibilities turned into the main component. Veterans’ personal information turned into veterans’ database that changed into subsidiary components of the martyrs / veterans / prisoners-of-war / warriors database.
Conclusion: The main components of the conceptual pattern of national base of veterans of Iran include: home page, introduction, conflicts and operations, equipment, war zones, facilities, news, cemeteries of martyrs, veterans’ rules, questions and answers, history of wars, war and art, veterans’ database, archives, links, guide, contact with us, FAQs, other content, resources, about the base, search, map.
Minoo Shahbazi, Reza Safdari, Mohammad Zarei,
Volume 12, Issue 2 (7-2018)
Volume 12, Issue 2 (7-2018)
Abstract
Background and Aim: The quality of Electronic Health Records (EHRs) depends on the quality of its content and proper documentation. Determining the Minimum Data Set (MDS) to enhance the quality of electronic health records’ content and helping to improve the quality of health care provision to uveitis patients are essential matters. The aim of this study is to determine the essential MDS for uveitis patients’ electronic health records.
Materials and Methods: In this descriptive-analytical study, data collection tools for collecting the Minimum Data Set were library resources and internet-based database. The MDS was obtained through Likert scale questionnaire and was surveyed by 22 ophthalmologists and retina subspecialists.
Results: Among the elements of the survey, all cases with over 90% approval were considered as main elements. Regarding the importance of presented data elements, no significant difference was found between the responses of ophthalmologists who participated in this study.
Conclusion: The Minimum Data Set of uveitis patients’ electronic health records can be represented by five groups of demographic information: patients’ clinical records, laboratory information, type of uveitis, treatment guidelines, and the information of ophthalmic pictures. A suggested model for manual systems and electronic medical records is available.
Materials and Methods: In this descriptive-analytical study, data collection tools for collecting the Minimum Data Set were library resources and internet-based database. The MDS was obtained through Likert scale questionnaire and was surveyed by 22 ophthalmologists and retina subspecialists.
Results: Among the elements of the survey, all cases with over 90% approval were considered as main elements. Regarding the importance of presented data elements, no significant difference was found between the responses of ophthalmologists who participated in this study.
Conclusion: The Minimum Data Set of uveitis patients’ electronic health records can be represented by five groups of demographic information: patients’ clinical records, laboratory information, type of uveitis, treatment guidelines, and the information of ophthalmic pictures. A suggested model for manual systems and electronic medical records is available.
Reza Safdari, Somaye Mahdavi, Leila Shahmoradi, Khdijeh Adabi, Shahram Tahmasebian, Mahnaz Nazari,
Volume 12, Issue 5 (1-2019)
Volume 12, Issue 5 (1-2019)
Abstract
Background and Aim: To provide effective care, health care providers need timely and appropriate information. Electronic records provide quick access and easy management of data. The aim of this study was to develop electronic health records for patients with hydatidiform mole and evaluation of completeness of medical records
Materials and Methods: This applied study was conducted in 2017. After verifying the minimum data set required for the system, data were extracted from patient records using a checklist and entered into SQL server. SQL server 2012 and Visual Studio 2013 to design electronic records and SPSS 20 for data analysis was used. Extent of data completion in patient records was also assesed.
Results: Data on the completion of paper records indicated that in 100% of cases, “address” item was filled in. The less completed data was related to carotene deficiency (%1.1). Our findings also showed that the eight most important items like age of first menstruation, first gestational age, interval between pregnancies, number of sexual partners, menstruation between pregnancies, contraceptive methods, social habits and radiotherapy, were not completed in all records.
Conclusion: Many of the important minimum data set for hydatidiform mole disease were either not completed or completed in limited numbers in paper records. By developing such health records, we can ensure better prevention and treatment, and regular follow-up for the patients and help them to save their time and costs.
Materials and Methods: This applied study was conducted in 2017. After verifying the minimum data set required for the system, data were extracted from patient records using a checklist and entered into SQL server. SQL server 2012 and Visual Studio 2013 to design electronic records and SPSS 20 for data analysis was used. Extent of data completion in patient records was also assesed.
Results: Data on the completion of paper records indicated that in 100% of cases, “address” item was filled in. The less completed data was related to carotene deficiency (%1.1). Our findings also showed that the eight most important items like age of first menstruation, first gestational age, interval between pregnancies, number of sexual partners, menstruation between pregnancies, contraceptive methods, social habits and radiotherapy, were not completed in all records.
Conclusion: Many of the important minimum data set for hydatidiform mole disease were either not completed or completed in limited numbers in paper records. By developing such health records, we can ensure better prevention and treatment, and regular follow-up for the patients and help them to save their time and costs.
Reza Safdari, Mozhgan Rahmanian, Shahrbanoo Pahlevany Nejad ,
Volume 12, Issue 6 (3-2019)
Volume 12, Issue 6 (3-2019)
Abstract
Background and Aim: Preeclampsia is one of the most prominent cases of pregnancy related diseases that threatens health at global level, especially in developing countries. In Iran, with 14% of outbreak, it is the second most common cause of maternal mortality. The main goal of this study was to identify the information requirements of the Android-based preeclampsia self-Management application.
Materials & Methods: This was a descriptive study that was done in 2018 in Amir_Almomenin Hospital affiliated to Semnan University of Medical Sciences in two stages of reviewing the sources and the need for data elements. In the review phase, after studying the articles and study, the data requirements and factors which affecting the prevalence of preeclampsia were identified and a survey of qualified physicians was done by designing a researcher-made questionnaire.
Results: This research results indicate that 63.9% of the respondents assigned to the elements mentioned in the demographic findings. 75.9% of them identified health information elements as very important. Also, 77.85% of the research community considered the elements in the lifestyle sector to be of the highest importance. All participants recognized that reminder in the program was necessary. Approximately 33.33% of them reconsidered sport education to be at the lowest level, while 45.24% rated it as being of the highest importance.
Conclusion: The information requirements of this program were determined in 6 groups of health history, educational tips, lifestyle, alarms, referral, and reporting. These programs can help pregnant mothers with preeclampsia to control their disease to minimize complications by observing proper nutrition and principles of treatment.
Materials & Methods: This was a descriptive study that was done in 2018 in Amir_Almomenin Hospital affiliated to Semnan University of Medical Sciences in two stages of reviewing the sources and the need for data elements. In the review phase, after studying the articles and study, the data requirements and factors which affecting the prevalence of preeclampsia were identified and a survey of qualified physicians was done by designing a researcher-made questionnaire.
Results: This research results indicate that 63.9% of the respondents assigned to the elements mentioned in the demographic findings. 75.9% of them identified health information elements as very important. Also, 77.85% of the research community considered the elements in the lifestyle sector to be of the highest importance. All participants recognized that reminder in the program was necessary. Approximately 33.33% of them reconsidered sport education to be at the lowest level, while 45.24% rated it as being of the highest importance.
Conclusion: The information requirements of this program were determined in 6 groups of health history, educational tips, lifestyle, alarms, referral, and reporting. These programs can help pregnant mothers with preeclampsia to control their disease to minimize complications by observing proper nutrition and principles of treatment.
Mohammad Reza Shahraki , Mahboubeh Mesgar,
Volume 13, Issue 1 (5-2019)
Volume 13, Issue 1 (5-2019)
Abstract
Background and Aim: The liver, as one of the largest internal organs in the body, is responsible for many vital functions including purifying and purifying blood, regulating the body's hormones, preserving glucose, and the body. Therefore, disruptions in the functioning of these problems will sometimes be irreparable. Early prediction of these diseases will help their early and effective treatment. Regarding the importance of liver diseases and increasing number of patients, the present study, using data mining algorithms, aimed to predict liver disease.
Materials and Methods: This descriptive study was performed using 721 data from liver patient in zahedan. In this study, after preprocessing data, data mining techniques such as SVM: Support Vector Machine, CHAID, Exhaustive CHAID and boosting C5.0, data were analyzed using IBM SPSS Modeler 18 data mining software.
Result: The validity obtained for boosting C5.0 94/09, for Exhaustive CHAID algorithm 88/71, for SVM 87/09, for CHAID algorithm 85/47 prediction of liver disease. the boosting C5.0 algorithm showed a better performance of this algorithm among other algorithms.
Conclusion: According to the rules created by boosting C5.0 algorithm, for a new sample, one can predict the likelihood of a person for developing liver disease with high precision.
Materials and Methods: This descriptive study was performed using 721 data from liver patient in zahedan. In this study, after preprocessing data, data mining techniques such as SVM: Support Vector Machine, CHAID, Exhaustive CHAID and boosting C5.0, data were analyzed using IBM SPSS Modeler 18 data mining software.
Result: The validity obtained for boosting C5.0 94/09, for Exhaustive CHAID algorithm 88/71, for SVM 87/09, for CHAID algorithm 85/47 prediction of liver disease. the boosting C5.0 algorithm showed a better performance of this algorithm among other algorithms.
Conclusion: According to the rules created by boosting C5.0 algorithm, for a new sample, one can predict the likelihood of a person for developing liver disease with high precision.
Farideh Akbarzadeh, Zahed Bigdeli,
Volume 13, Issue 5 (1-2020)
Volume 13, Issue 5 (1-2020)
Abstract
Background and aim: A Library is a safe place to research and study for some students, but it creates anxiety for others. The main purpose of this research is to investigate the library anxiety among Kermanshah University of Medical Sciences(KUMS) residents in using information sources and electronic services based on five factors of Bostick scale.
Materials and Methods: The study was a cross-sectional survey. The sample size was 197 persons who were selected using simple random sampling. Data collection tool was a researcher-made questionnaire whose validity was confirmed by experts and its reliability was confirmed by Cronbach's alpha coefficient of 0.809. The questionnaire consisted of 41 questions on a five-point Likert scale. The library anxiety questions were designed and localized based on the five factors of the Bostick scale. Data were analyzed using descriptive statistics, mean, standard deviation and analytical statistics by Kolmogorov-Smirnov test and Pearson correlation coefficient using SPSS 23 software.
Results: The mean score of library anxiety was 78.32, the mean score of familiarity and usage was 32.08 and 29.54. Mechanical and emotional factors had the highest mean of library anxiety factors. Mean library anxiety was not significantly different between male and female residents(p>0.05). There was a significant relationship between residents' library anxiety and their skills in using information resources and e-services.
Conclusion: The results indicate a level of library anxiety among the assistants. Accepting this fact can be a positive step in solving the problems associated with the use of information and electronic resources.
Materials and Methods: The study was a cross-sectional survey. The sample size was 197 persons who were selected using simple random sampling. Data collection tool was a researcher-made questionnaire whose validity was confirmed by experts and its reliability was confirmed by Cronbach's alpha coefficient of 0.809. The questionnaire consisted of 41 questions on a five-point Likert scale. The library anxiety questions were designed and localized based on the five factors of the Bostick scale. Data were analyzed using descriptive statistics, mean, standard deviation and analytical statistics by Kolmogorov-Smirnov test and Pearson correlation coefficient using SPSS 23 software.
Results: The mean score of library anxiety was 78.32, the mean score of familiarity and usage was 32.08 and 29.54. Mechanical and emotional factors had the highest mean of library anxiety factors. Mean library anxiety was not significantly different between male and female residents(p>0.05). There was a significant relationship between residents' library anxiety and their skills in using information resources and e-services.
Conclusion: The results indicate a level of library anxiety among the assistants. Accepting this fact can be a positive step in solving the problems associated with the use of information and electronic resources.
Raoof Nopour, Mohammad Shirkhoda, Sharareh Rostam Niakan Kalhori,
Volume 14, Issue 2 (5-2020)
Volume 14, Issue 2 (5-2020)
Abstract
Background and Aim: Colorectal cancer is one of the most common gastrointestinal cancers among human beings and the most important cause of death in the world. Based on the risk of colorectal cancer for individuals, using an appropriate screening program can help to prevent the disease. Therefore, the purpose of this study was to design a model for screening colorectal cancer based on risk factors to increase the survival rate of the disease on the one hand and to reduce the mortality rate on the other.
Materials and Methods: By reviewing articles and patients' records, 38 risk factors were detected. To determine the most important risk factors clinically, CVR(content validity ratio) was used; and considering the collected data, Spearman correlation coefficient and logistic regression analysis were applied for statistical analyses. Then, four algorithms -- J-48, J-RIP, PART and REP-Tree -- were used for data mining and rule generation. Finally, the most common model was obtained based on comparing the performance of the algorithms.
Results: After comparing the performance of algorithms, the J-48 algorithm with an F-Measure of 0.889 was found to be better than the others.
Conclusion: The results of evaluating J-48 data mining algorithm performance showed that this algorithm could be considered as the most appropriate model for colorectal cancer risk prediction.
Materials and Methods: By reviewing articles and patients' records, 38 risk factors were detected. To determine the most important risk factors clinically, CVR(content validity ratio) was used; and considering the collected data, Spearman correlation coefficient and logistic regression analysis were applied for statistical analyses. Then, four algorithms -- J-48, J-RIP, PART and REP-Tree -- were used for data mining and rule generation. Finally, the most common model was obtained based on comparing the performance of the algorithms.
Results: After comparing the performance of algorithms, the J-48 algorithm with an F-Measure of 0.889 was found to be better than the others.
Conclusion: The results of evaluating J-48 data mining algorithm performance showed that this algorithm could be considered as the most appropriate model for colorectal cancer risk prediction.
Marjan Ghazi Saeedi, Gholam Reza Esmaeili Javid, Niloufar Mohammadzadeh, Hamide Asadallah Khan Vali,
Volume 14, Issue 5 (1-2021)
Volume 14, Issue 5 (1-2021)
Abstract
Background and Aim: Diabetes is one of the most common metabolic diseases in the world, of which one of the most common and painful complications is diabetic foot ulcer. The accuracy and comprehensiveness of the contents of electronic medical record is effective in improving the quality of treatment and the care of diabetic foot ulcer patients. The aim of this study is to determine the minimum data set (MDS) essential for diabetic foot patients' electronic medical records.
Materials and Methods: In this descriptive-analytical study, authoritative internet and library resources were studied to collect diabetic foot ulcer information elements. Fourteen physicians and nurses working and collaborating with the Wound Healing Center affiliated to Academic Center for Education, Culture and Research (ACECR) were selected for clinical survey, and 5 health information technology specialists of Tehran University of Medical Sciences (TUMS) were chosen for demographic information survey. The study tools were a researcher-made questionnaire, CVR content validity method and test-retest method for reliability.
Results: Out of 23 information elements surveyed in demographic section, cases above 99% of the agreement were selected. Also, out of 86 information elements of the clinical section, more than 51% of the cases were selected. Clinical experts included 6 wound specialists, 4 general practitioners and 6 nurses. In the demographic information section, the lowest agreement was related to the element of identity and Education level with 20% agreement. In clinical information, the lowest agreement was related to surgery, leech therapy and MRI of the foot with 0% and PRP, G-CSF, Sono-Doppler liver with 14%.
Conclusion: The minimum information elements of diabetic foot ulcer electronic medical record were divided into history, wound information, lower limb information, paraclinical results, wound management, and follow-up in clinical section; and in demographic information section, they were divided into identity, admission, finance, reporting, and system capability. The proposed model for manual and electronic medical records is available.
Materials and Methods: In this descriptive-analytical study, authoritative internet and library resources were studied to collect diabetic foot ulcer information elements. Fourteen physicians and nurses working and collaborating with the Wound Healing Center affiliated to Academic Center for Education, Culture and Research (ACECR) were selected for clinical survey, and 5 health information technology specialists of Tehran University of Medical Sciences (TUMS) were chosen for demographic information survey. The study tools were a researcher-made questionnaire, CVR content validity method and test-retest method for reliability.
Results: Out of 23 information elements surveyed in demographic section, cases above 99% of the agreement were selected. Also, out of 86 information elements of the clinical section, more than 51% of the cases were selected. Clinical experts included 6 wound specialists, 4 general practitioners and 6 nurses. In the demographic information section, the lowest agreement was related to the element of identity and Education level with 20% agreement. In clinical information, the lowest agreement was related to surgery, leech therapy and MRI of the foot with 0% and PRP, G-CSF, Sono-Doppler liver with 14%.
Conclusion: The minimum information elements of diabetic foot ulcer electronic medical record were divided into history, wound information, lower limb information, paraclinical results, wound management, and follow-up in clinical section; and in demographic information section, they were divided into identity, admission, finance, reporting, and system capability. The proposed model for manual and electronic medical records is available.
Marjan Ghazi-Saeedi, Roya Riahi, Rasool Nouri,
Volume 14, Issue 6 (1-2021)
Volume 14, Issue 6 (1-2021)
Abstract
Background and Aim: In this study, in order to increase the visibility of articles in Scopus journals of Tehran University of Medical Sciences (TUMS), selective dissemination of information (SDI) service was presented and its impact on some citation indices was investigated.
Materials and Methods: This is a semi-experimental study of two groups (pretest-posttest design with a control group). In this study, TUMS Scopus indexed journals (20 titles) were randomly divided into test and control groups and their citation indices were assessed. Then, the SDI services for test group journals were designed based on PubMed's Alert system and presented to the university's top researchers for one year. Finally, the citation indices of the journals of test and control groups were reassessed and compared. For data analysis, independent t-test, paired t-test and, covariance analysis were used.
Results: Comparison of mean citations as well as SJR, SNIP and CiteScore indices before and after the intervention showed no significant difference between the test and control groups. But the average CiteScore in both groups after the intervention was significantly higher than the average before the intervention.
Conclusion: The results showed that the provision of the aformentioned services in the time period defined in this study had no significant effect on the citation indices. However, the valuable experiences gained in this study will undoubtedly be applicable to future research as well as services to researchers, librarians, and journal managers.
Materials and Methods: This is a semi-experimental study of two groups (pretest-posttest design with a control group). In this study, TUMS Scopus indexed journals (20 titles) were randomly divided into test and control groups and their citation indices were assessed. Then, the SDI services for test group journals were designed based on PubMed's Alert system and presented to the university's top researchers for one year. Finally, the citation indices of the journals of test and control groups were reassessed and compared. For data analysis, independent t-test, paired t-test and, covariance analysis were used.
Results: Comparison of mean citations as well as SJR, SNIP and CiteScore indices before and after the intervention showed no significant difference between the test and control groups. But the average CiteScore in both groups after the intervention was significantly higher than the average before the intervention.
Conclusion: The results showed that the provision of the aformentioned services in the time period defined in this study had no significant effect on the citation indices. However, the valuable experiences gained in this study will undoubtedly be applicable to future research as well as services to researchers, librarians, and journal managers.
Reza Safdari, Seyyed Farshad Allameh, Ms Fariba Shabani,
Volume 15, Issue 6 (3-2022)
Volume 15, Issue 6 (3-2022)
Abstract
Background and Aim: Many risk factors can cause biliary system diseases. Hence, this category of diseases is amongst the most common ones. Active patient cooperation is very important in disease management, self-care, and clinical outcomes improvement. A mobile phone application has a high potential in supporting the patients’ self-management. Therefore, this study was conducted to recognize and define data elements to develop a self-care application for biliary patients.
Materials and Methods: The current descriptive study was conducted in 2 stages, resource investigation, and data elements’ need assessment. In the first stage, scientific articles available in databases were used for defining required data elements to develop the application for biliary patients, and a checklist of data elements was prepared. In the second stage, a questionnaire was made based on the checklist. Content and face validity were accepted by the research team and the reliability was calculated 87.2%, using the Cronbach’s alpha test. The mentioned questionnaire was given to Gastroenterologists at Imam Khomeini Hospital complex, and the elected data elements were recognized.
Results: In this application, data elements were categorized into seven sections, including demographic and clinical information, data related to the biliary system diseases, postoperative lifestyle information of the biliary patients, reminders, disease management, and informing. Sixty point five percent of the responders gave the highest importance to data elements in the demographic and clinical data section. Data elements related to patients’ education were considered highly important by 54.2% of the responders. Forty three point eight percent gave the highest importance to data elements in interventional applications’ sections, and only 4.2% gave the least importance to this section.
Conclusion: Based on the identified data elements, a self-care application was designed and developed and can be used as a supplement to specialized care for biliary patients.
Materials and Methods: The current descriptive study was conducted in 2 stages, resource investigation, and data elements’ need assessment. In the first stage, scientific articles available in databases were used for defining required data elements to develop the application for biliary patients, and a checklist of data elements was prepared. In the second stage, a questionnaire was made based on the checklist. Content and face validity were accepted by the research team and the reliability was calculated 87.2%, using the Cronbach’s alpha test. The mentioned questionnaire was given to Gastroenterologists at Imam Khomeini Hospital complex, and the elected data elements were recognized.
Results: In this application, data elements were categorized into seven sections, including demographic and clinical information, data related to the biliary system diseases, postoperative lifestyle information of the biliary patients, reminders, disease management, and informing. Sixty point five percent of the responders gave the highest importance to data elements in the demographic and clinical data section. Data elements related to patients’ education were considered highly important by 54.2% of the responders. Forty three point eight percent gave the highest importance to data elements in interventional applications’ sections, and only 4.2% gave the least importance to this section.
Conclusion: Based on the identified data elements, a self-care application was designed and developed and can be used as a supplement to specialized care for biliary patients.
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.
Zohreh Ehteshami, Azam Shahbodaghi, Mohammad Javad Mansourzadeh,
Volume 18, Issue 5 (11-2024)
Volume 18, Issue 5 (11-2024)
Abstract
Background and Aim: An efficient data librarian equipped with the necessary competencies and capabilities is one of the most crucial elements in managing research data. The aim of this study is to identify the expected competencies and capabilities for data librarians in research data management according Harvard Biomedical Data Life Cycle.
Materials and Methods: This study is a scoping review, utilizing the Harvard Biomedical Data Lifecycle model to systematically present the findings. To retrieve relevant literature, a search strategy was employed using related keywords in databases such as Scopus, PubMed, Web of Science, Google scholar and other reputable domestic databases, over the past five years. The research population comprised original research articles published in Persian and English that addressed the expected skills and capabilities for data librarians in managing research data.
Results: Out of 5064 documents found, 196 were selected for full-text review. After reviewing the full texts, 17 studies were included in the research. In total, 92 competencies and capabilities were identified across 23 processes within the 7 stages of the Harvard Biomedical Data Lifecycle: 16 in the first stage, 16 in the second stage, 7 in the third stage, 15 in the fourth and fifth stages, 12 in the sixth stage, 8 in the seventh stage, and 18 general competencies and capabilities. According to the findings, the most studies focused on the competencies and capabilities required for the second stage, “Collection and Creation,” while the fewest studies addressed the seventh stage, “Publish and Reuse.” No studies mentioned competencies and capabilities for the processes “Image Management” in the third stage and “Preprints and Publishing” in the seventh stage.
Conclusion: The results of this study indicate that among the various stages of the data lifecycle, the “Collection and Creation” stage received the most attention. Additionally, data librarians should possess not only specialized and professional skills but also general competencies and capabilities. It is recommended that the findings of this research be considered for designing short-term and long-term educational programs to train data librarians for research data managenet.
Materials and Methods: This study is a scoping review, utilizing the Harvard Biomedical Data Lifecycle model to systematically present the findings. To retrieve relevant literature, a search strategy was employed using related keywords in databases such as Scopus, PubMed, Web of Science, Google scholar and other reputable domestic databases, over the past five years. The research population comprised original research articles published in Persian and English that addressed the expected skills and capabilities for data librarians in managing research data.
Results: Out of 5064 documents found, 196 were selected for full-text review. After reviewing the full texts, 17 studies were included in the research. In total, 92 competencies and capabilities were identified across 23 processes within the 7 stages of the Harvard Biomedical Data Lifecycle: 16 in the first stage, 16 in the second stage, 7 in the third stage, 15 in the fourth and fifth stages, 12 in the sixth stage, 8 in the seventh stage, and 18 general competencies and capabilities. According to the findings, the most studies focused on the competencies and capabilities required for the second stage, “Collection and Creation,” while the fewest studies addressed the seventh stage, “Publish and Reuse.” No studies mentioned competencies and capabilities for the processes “Image Management” in the third stage and “Preprints and Publishing” in the seventh stage.
Conclusion: The results of this study indicate that among the various stages of the data lifecycle, the “Collection and Creation” stage received the most attention. Additionally, data librarians should possess not only specialized and professional skills but also general competencies and capabilities. It is recommended that the findings of this research be considered for designing short-term and long-term educational programs to train data librarians for research data managenet.
Farzin Halabchi, Reza Safdari, Shahrbanoo Pahlevanynejad, Sahba Kazemipour,
Volume 19, Issue 2 (7-2025)
Volume 19, Issue 2 (7-2025)
Abstract
Background and Aim: The World Health Organization defines physical inactivity as engaging in less than 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity physical activity per week for adults, which is recognized as a serious global health challenge with dangerous consequences for public health. Global statistics indicate that this issue is more prominent among women; in Iran, 61.9% of women do not engage in sufficient physical activity. The adoption and expansion of health-related technologies indicate their high potential in supporting self-care. This study aims to identify the necessary data elements for designing a personalized self-care fitness mobile application for women.
Materials and Methods: This descriptive study was conducted in two phases: literature review and data element needs assessment. In the first phase, relevant data elements for creating a personalized self-care fitness application for women were identified through scientific articles in databases and library resources, and a data elements checklist was prepared. In the second phase, based on the checklist, a questionnaire was designed by the researcher. Its validity was confirmed by the research team, and its reliability was calculated with a Cronbach’s alpha coefficient of 91.3%.
Results: The aforementioned questionnaire was provided to 20 physicians from the sports medicine department at Mahdi Clinic, Imam Khomeini Hospital Complex, Tehran, to thoroughly evaluate the proposed data elements in terms of their importance, measurability, and relevance. In total, 49 data elements were identified across seven sections: demographic information, health information, disease information, inappropriate behavioral habits, anthropometric data, reports, and lifestyle. Of these, 4 elements were removed due to incompatibility with the study objectives and low importance scores. Additionally, to facilitate future analyses, the remaining elements were re-categorized into 6 groups.
Conclusion: In this study, the key data elements required for designing and providing exercise programs specifically for women were identified and determined. This process aimed to enhance the level of physical activity and address the specific needs of women, thereby establishing a scientific and precise foundation for developing programs tailored to the physical and psychological characteristics of this group.
Materials and Methods: This descriptive study was conducted in two phases: literature review and data element needs assessment. In the first phase, relevant data elements for creating a personalized self-care fitness application for women were identified through scientific articles in databases and library resources, and a data elements checklist was prepared. In the second phase, based on the checklist, a questionnaire was designed by the researcher. Its validity was confirmed by the research team, and its reliability was calculated with a Cronbach’s alpha coefficient of 91.3%.
Results: The aforementioned questionnaire was provided to 20 physicians from the sports medicine department at Mahdi Clinic, Imam Khomeini Hospital Complex, Tehran, to thoroughly evaluate the proposed data elements in terms of their importance, measurability, and relevance. In total, 49 data elements were identified across seven sections: demographic information, health information, disease information, inappropriate behavioral habits, anthropometric data, reports, and lifestyle. Of these, 4 elements were removed due to incompatibility with the study objectives and low importance scores. Additionally, to facilitate future analyses, the remaining elements were re-categorized into 6 groups.
Conclusion: In this study, the key data elements required for designing and providing exercise programs specifically for women were identified and determined. This process aimed to enhance the level of physical activity and address the specific needs of women, thereby establishing a scientific and precise foundation for developing programs tailored to the physical and psychological characteristics of this group.
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.
Abbas Sheikhtaheri, Elaheh Jamshidi, Ali Mohammadi, Vahid Feyzollahi,
Volume 19, Issue 5 (12-2025)
Volume 19, Issue 5 (12-2025)
Abstract
Background and Aim: Knee ligament rupture is a common knee injury, especially among athletes. Considering the importance of treatment quality in the affected population, there is a crucial need for the collection of high-quality, standardized national-level data. This can be achieved by establishing a Minimum Data Set (MDS). The present study aimed to design a Minimum Data Set for the Knee Ligament Rupture Reconstruction Registry System in Athletes.
Materials and Methods: This applied research was conducted in 2024 using a quantitative method (descriptive-comparative and Delphi technique) across three phases. In the first phase, using a descriptive-comparative approach, the required data elements from the national registry systems of selected countries (Norway, Sweden, Denmark, UK) were extracted and analyzed in comparative tables. In the second phase, the data elements currently recorded for patients undergoing knee ligament rupture reconstruction surgery in Iran were identified using a descriptive data collection form. In the third phase, based on the findings from the first two phases, a preliminary MDS was designed as a questionnaire. Its validity was then assessed over two rounds using the Delphi method by a panel of experts (24 in the first round, 18 in the second). Finally, items that achieved a consensus of 75% or higher were included in the final MDS.
Results: In the review conducted on the registry systems of selected countries, including Norway, Sweden, Denmark, and England, the data elements recorded in these systems were first extracted. Subsequently, in the first phase of the study, the extracted data elements were categorized into two main categories: Administrative and clinical. The findings of this phase were obtained through their comparison in comparative tables. The findings of the second phase of the study consisted of data elements extracted from the medical records of patients who had undergone knee ligament rupture reconstruction surgery in Iran. In the third phase of the study, the final minimum data set for patients undergoing knee ligament rupture reconstruction surgery was developed based on the findings of the first and second phases of the study as well as expert opinions. This data set comprised 78 data elements organized into two sections: administrative (9 data elements) and clinical (69 data elements). In the administrative section, data classes were categorized into demographic, socioeconomic, and visit-related groups. In the clinical section, data classes were categorized into diagnostic, anthropometric, surgical, follow-up, and outcome groups.
Conclusion: The Minimum Data Set for knee ligament rupture reconstruction surgery can play a significant role in collecting high-quality data, evaluating and managing treatment quality and outcomes, and informing planning and policymaking in this field by ensuring the collection of integrated and high-quality data.
Materials and Methods: This applied research was conducted in 2024 using a quantitative method (descriptive-comparative and Delphi technique) across three phases. In the first phase, using a descriptive-comparative approach, the required data elements from the national registry systems of selected countries (Norway, Sweden, Denmark, UK) were extracted and analyzed in comparative tables. In the second phase, the data elements currently recorded for patients undergoing knee ligament rupture reconstruction surgery in Iran were identified using a descriptive data collection form. In the third phase, based on the findings from the first two phases, a preliminary MDS was designed as a questionnaire. Its validity was then assessed over two rounds using the Delphi method by a panel of experts (24 in the first round, 18 in the second). Finally, items that achieved a consensus of 75% or higher were included in the final MDS.
Results: In the review conducted on the registry systems of selected countries, including Norway, Sweden, Denmark, and England, the data elements recorded in these systems were first extracted. Subsequently, in the first phase of the study, the extracted data elements were categorized into two main categories: Administrative and clinical. The findings of this phase were obtained through their comparison in comparative tables. The findings of the second phase of the study consisted of data elements extracted from the medical records of patients who had undergone knee ligament rupture reconstruction surgery in Iran. In the third phase of the study, the final minimum data set for patients undergoing knee ligament rupture reconstruction surgery was developed based on the findings of the first and second phases of the study as well as expert opinions. This data set comprised 78 data elements organized into two sections: administrative (9 data elements) and clinical (69 data elements). In the administrative section, data classes were categorized into demographic, socioeconomic, and visit-related groups. In the clinical section, data classes were categorized into diagnostic, anthropometric, surgical, follow-up, and outcome groups.
Conclusion: The Minimum Data Set for knee ligament rupture reconstruction surgery can play a significant role in collecting high-quality data, evaluating and managing treatment quality and outcomes, and informing planning and policymaking in this field by ensuring the collection of integrated and high-quality data.
Ahmad Negahban, Azam Salehzadeh, Razieh Farrahi, Alireza Nourozi, Sina Tavakoli,
Volume 19, Issue 6 (3-2026)
Volume 19, Issue 6 (3-2026)
Abstract
Background and Aim: With the digitalization of healthcare, hospital information systems handle vast amounts of sensitive data, making their protection crucial. This study aimed to assess the compliance of these systems in hospitals affiliated with Birjand University of Medical Sciences with the physical and technical safeguard standards of Health Insurance Portability and Accountability Act (HIPAA) in 2024.
Materials and Methods: This cross-sectional descriptive study was conducted in 15 hospitals affiliated with Birjand University of Medical Sciences. The study population consisted of Information Technology (IT) unit managers, who were selected using a census method (15 individuals). The research instrument was a researcher-developed checklist consisting of 56 items based on the physical and technical standards of HIPAA. The face validity of the checklist was confirmed by five experts in Health Information Management, Medical Informatics, and Health Policy, and its reliability was verified with a Cronbach’s alpha coefficient of 0.84. Data were analyzed using SPSS software and descriptive statistics, including frequency, percentage, mean, and standard deviation.
Results: A total of 15 information technology managers (14 men and 1 woman) from 15 hospitals, including 8 teaching and 7 non-teaching hospitals, participated in the study. The findings showed that the hospital information systems of Birjand University of Medical Sciences complied with the HIPAA physical and technical safeguard standards at rates of 81.7% and 86.7%, respectively. In the domain of physical safeguards, the workstation security standard demonstrated the highest level of compliance, with a mean score of 89.3%. Full compliance (100%) was observed for certain indicators, including emergency access procedures for facilities and physical access control procedures. In contrast, the lowest compliance in this domain was related to the device and media controls standard, with a mean score of 74.9%, particularly in the identification and tracking of hardware and electronic media. In the domain of technical safeguards, the overall mean compliance rate was 86.7%. Among these standards, person or entity authentication achieved the highest level of compliance, with all hospitals demonstrating full compliance (100%). In addition, access control (93.3%), audit controls (86.7%), and transmission security (85.3%) were all at desirable levels. However, the lowest compliance was observed for the integrity standard (50%), highlighting the need to strengthen technical infrastructure and implement more advanced electronic mechanisms to ensure data accuracy and integrity.
Conclusion: Although the overall level of compliance in the hospitals under study is satisfactory, significant gaps remain, particularly in device and media control and data integrity. These deficiencies may lead to breaches of patient privacy and undermine public trust in the healthcare system. It is recommended that senior hospital managers and health policymakers address these deficiencies by developing and implementing clear internal guidelines, investing in appropriate supportive technologies, and conducting continuous, targeted training programs for all personnel. In addition, periodic compliance monitoring is essential to ensure continuous improvement.
Materials and Methods: This cross-sectional descriptive study was conducted in 15 hospitals affiliated with Birjand University of Medical Sciences. The study population consisted of Information Technology (IT) unit managers, who were selected using a census method (15 individuals). The research instrument was a researcher-developed checklist consisting of 56 items based on the physical and technical standards of HIPAA. The face validity of the checklist was confirmed by five experts in Health Information Management, Medical Informatics, and Health Policy, and its reliability was verified with a Cronbach’s alpha coefficient of 0.84. Data were analyzed using SPSS software and descriptive statistics, including frequency, percentage, mean, and standard deviation.
Results: A total of 15 information technology managers (14 men and 1 woman) from 15 hospitals, including 8 teaching and 7 non-teaching hospitals, participated in the study. The findings showed that the hospital information systems of Birjand University of Medical Sciences complied with the HIPAA physical and technical safeguard standards at rates of 81.7% and 86.7%, respectively. In the domain of physical safeguards, the workstation security standard demonstrated the highest level of compliance, with a mean score of 89.3%. Full compliance (100%) was observed for certain indicators, including emergency access procedures for facilities and physical access control procedures. In contrast, the lowest compliance in this domain was related to the device and media controls standard, with a mean score of 74.9%, particularly in the identification and tracking of hardware and electronic media. In the domain of technical safeguards, the overall mean compliance rate was 86.7%. Among these standards, person or entity authentication achieved the highest level of compliance, with all hospitals demonstrating full compliance (100%). In addition, access control (93.3%), audit controls (86.7%), and transmission security (85.3%) were all at desirable levels. However, the lowest compliance was observed for the integrity standard (50%), highlighting the need to strengthen technical infrastructure and implement more advanced electronic mechanisms to ensure data accuracy and integrity.
Conclusion: Although the overall level of compliance in the hospitals under study is satisfactory, significant gaps remain, particularly in device and media control and data integrity. These deficiencies may lead to breaches of patient privacy and undermine public trust in the healthcare system. It is recommended that senior hospital managers and health policymakers address these deficiencies by developing and implementing clear internal guidelines, investing in appropriate supportive technologies, and conducting continuous, targeted training programs for all personnel. In addition, periodic compliance monitoring is essential to ensure continuous improvement.
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