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Azita Yazdani, Reza Safdari, Roxana Sharifian, Maryam Zahmatkeshan, Marjan Ghazi Saeedi,
Volume 14, Issue 2 (5-2020)
Volume 14, Issue 2 (5-2020)
Abstract
Background and Aim: When clinical decision support systems are developed, implementing solutions that enable these systems to be -used on a large scale can reduce the production costs associated with the creation, maintenance and by sharing these systems, producing multiple clinical decision support systems will be prevented. In recent years, one of the approaches used for this purpose in combination with clinical decision support systems is the service-oriented architecture approach. The purpose of this study was to investigate the role and importance of service-oriented architecture in delivering scalable architectures of clinical decision support systems focusing on different approaches to this architecture.
Materials and Methods: This article is a simple review article. Bibliographic databases of IEEE Explore, Science Direct, Springer, Web of Science, and Scopus were reviewed. The keywords "Service Oriented Architecture" and "clinical decision support systems" were used as keywords along with related terms for searching these databases.
Results: The clinical decision support systems based on service-oriented architecture brings benefits such as Facilitate knowledge maintenance, reducing costs and improving agility. Point-to-point communication, enterprise service bus, service registry, clinical and engine guiding engine, and service choreography and orchestration are general architectural designs that are evident in the use of web-based clinical decision support systems based on a service-oriented architecture approach.
Conclusion: Service-oriented architecture is a potential solution for delivering scalable platforms for clinical decision systems.
Materials and Methods: This article is a simple review article. Bibliographic databases of IEEE Explore, Science Direct, Springer, Web of Science, and Scopus were reviewed. The keywords "Service Oriented Architecture" and "clinical decision support systems" were used as keywords along with related terms for searching these databases.
Results: The clinical decision support systems based on service-oriented architecture brings benefits such as Facilitate knowledge maintenance, reducing costs and improving agility. Point-to-point communication, enterprise service bus, service registry, clinical and engine guiding engine, and service choreography and orchestration are general architectural designs that are evident in the use of web-based clinical decision support systems based on a service-oriented architecture approach.
Conclusion: Service-oriented architecture is a potential solution for delivering scalable platforms for clinical decision systems.
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.
Mostafa Roshanzadeh, Mina Shirvani, Ali Tajabadi, Mohammad Hossein Khalilzadeh, Somayeh Mohammadi,
Volume 16, Issue 2 (5-2022)
Volume 16, Issue 2 (5-2022)
Abstract
Background and Aim: Clinical learning is an important part of the health field, where the student interacts with the environment and applies the learned concepts in practice. Clinical environments such as operating rooms are challenging for students due to their special complexity and can have a negative impact on their learning process. In order to identify students ‘learning challenges in the operating room environment, the present study was conducted to explain students’ experiences in the field of clinical learning challenges.
Materials and Methods: The present qualitative study was performed by contract content analysis method in 2022 in Shahrekord University of Medical Sciences. Fourteen surgical technology students were purposefully selected and data were collected using in-depth semi-structured individual and group interviews and analyzed using the Granheim and Landman approaches.
Results: The participants were interviewed over a period of 5 months. 9 face-to-face interviews were conducted with 14 participants. There were 6 individual interviews and 3 group interviews. The average duration of the interview was 30 minutes. The interviews continued until data saturation and when no new themes or categories were obtained from the interviews. The findings included a theme of “unfavorable learning environment” and three categories of “confusion in learning educational content, improper professional behavior of staff and insufficient self-confidence”. The main challenge that students faced in the field of clinical learning was the unfavorable learning environment. Conditions such as confusion in learning educational content, improper professional behavior of staff and insufficient self-confidence experienced by the students in the operating room, cause the students to find the learning atmosphere in the operating room unfavorable.
Conclusion: Improving the behavior and performance of staff and physicians in accordance with the standards of professional and ethical behavior and its regular evaluation from the perspective of students and other colleagues can play an effective role in maintaining professional conditions. Also, using experienced instructors who have the role of facilitating communication and learning of students in the operating room environment will play an effective role in reducing fear and controlling inappropriate behaviors of staff towards students. Educational officials are advised to solve the existing problems in order to improve the educational atmosphere of the operating room.
Materials and Methods: The present qualitative study was performed by contract content analysis method in 2022 in Shahrekord University of Medical Sciences. Fourteen surgical technology students were purposefully selected and data were collected using in-depth semi-structured individual and group interviews and analyzed using the Granheim and Landman approaches.
Results: The participants were interviewed over a period of 5 months. 9 face-to-face interviews were conducted with 14 participants. There were 6 individual interviews and 3 group interviews. The average duration of the interview was 30 minutes. The interviews continued until data saturation and when no new themes or categories were obtained from the interviews. The findings included a theme of “unfavorable learning environment” and three categories of “confusion in learning educational content, improper professional behavior of staff and insufficient self-confidence”. The main challenge that students faced in the field of clinical learning was the unfavorable learning environment. Conditions such as confusion in learning educational content, improper professional behavior of staff and insufficient self-confidence experienced by the students in the operating room, cause the students to find the learning atmosphere in the operating room unfavorable.
Conclusion: Improving the behavior and performance of staff and physicians in accordance with the standards of professional and ethical behavior and its regular evaluation from the perspective of students and other colleagues can play an effective role in maintaining professional conditions. Also, using experienced instructors who have the role of facilitating communication and learning of students in the operating room environment will play an effective role in reducing fear and controlling inappropriate behaviors of staff towards students. Educational officials are advised to solve the existing problems in order to improve the educational atmosphere of the operating room.
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.
Sedighe Hannani, Nazanin Sarraf Shahri, Asma Feizy Dehkharghani, Najme Samii, Azar Arab Khazaie, Azin Arab Khazaie, Kiarash Kamboozia,
Volume 17, Issue 3 (8-2023)
Volume 17, Issue 3 (8-2023)
Abstract
Background and Aim: Virtual networks have become one of the most influential tools in people’s lives, affecting various aspects of life. In medical sciences, with numerous advancements, the use of virtual networks is increasing. However, virtual networks can lead to wastage of students’ time and a reduction in study hours, which negatively impacts their knowledge and practical skills. Therefore, this study aimed to investigate the impact of using virtual networks on the knowledge and practical skills of surgical technologist students.
Materials and Methods: This descriptive-analytical study was conducted on 60 students in the 6th and 8th semester of operating room technology at Iran University of Medical Sciences in 2020-2021. At the end of the semester, the students underwent a comprehensive 40-question theoretical exam to assess their theoretical knowledge. To evaluate the level of virtual network usage, the students filled out a researcher-designed questionnaire. The practical skills of the students were measured based on their internship grades. Normality of the data was assessed using the Kolmogorov-Smirnov, and Pearson correlation coefficient test. Data analysis was performed using SPSS software.
Results: According to the findings of this study, there was a significant negative relationship between the level of virtual network usage and theoretical knowledge (P<0.05). This means that with an increase in virtual network usage, the level of students’ knowledge decreased. On the other hand, there was a significant positive relationship between theoretical knowledge and practical skills. This means that as the scores of the comprehensive exam increased, the scores of practical skills (internship) also increased. However, no significant relationship was found between the level of virtual network usage and students’ practical skills (P>0.21).
Conclusion: Based on the findings of this study, virtual networks lead to a weakening of students’ theoretical knowledge, as evidenced by the decrease in scores on the researcher-designed questionnaire (level of virtual network usage) and the comprehensive exam scores. Another result of the study was the significant positive relationship between theoretical knowledge and practical skills, indicating that as the comprehensive exam scores increased, the scores obtained in internships also increased.
Materials and Methods: This descriptive-analytical study was conducted on 60 students in the 6th and 8th semester of operating room technology at Iran University of Medical Sciences in 2020-2021. At the end of the semester, the students underwent a comprehensive 40-question theoretical exam to assess their theoretical knowledge. To evaluate the level of virtual network usage, the students filled out a researcher-designed questionnaire. The practical skills of the students were measured based on their internship grades. Normality of the data was assessed using the Kolmogorov-Smirnov, and Pearson correlation coefficient test. Data analysis was performed using SPSS software.
Results: According to the findings of this study, there was a significant negative relationship between the level of virtual network usage and theoretical knowledge (P<0.05). This means that with an increase in virtual network usage, the level of students’ knowledge decreased. On the other hand, there was a significant positive relationship between theoretical knowledge and practical skills. This means that as the scores of the comprehensive exam increased, the scores of practical skills (internship) also increased. However, no significant relationship was found between the level of virtual network usage and students’ practical skills (P>0.21).
Conclusion: Based on the findings of this study, virtual networks lead to a weakening of students’ theoretical knowledge, as evidenced by the decrease in scores on the researcher-designed questionnaire (level of virtual network usage) and the comprehensive exam scores. Another result of the study was the significant positive relationship between theoretical knowledge and practical skills, indicating that as the comprehensive exam scores increased, the scores obtained in internships also increased.
Mohamad Jebraeily, Shima Touraj, Farid Khorrami,
Volume 17, Issue 3 (8-2023)
Volume 17, Issue 3 (8-2023)
Abstract
Background and Aim: In the health system, reimbursement methods are an important criterion for the allocation of resources and the performance of service providers. The use of diagnosis-related groups (DRG) system reduces the length of stay and additional costs of the patient, prevents unnecessary treatment, increases resource efficiency and transparency of health care services. The development of the DRG system focuses on the accurate documentation of medical records and the correct coding of diagnoses and procedures. The purpose of this research is to evaluate the documentation and coding requirements of medical records in the implementation of a payment system based on diagnosis-related groups in Iran.
Materials and Methods: This research was descriptive-cross-sectional and was conducted in 2022. The data collection tool was a researcher-made checklist, the validity of which was confirmed based on the opinion of experts (health information management health economics) and its reliability was obtained by calculating Cronbach’s alpha (0.83). The research population consisted of 418 medical records in five medical training centers affiliated to Urmia University of Medical Sciences, which were selected through stratified-proportional sampling. Data were analyzed using SPSS software.
Results: The results of the evaluation of the documentation and coding requirements of medical records for the implementation of the DRG system showed that the demographic/administrative variables including age, sex, type of admission, length of stay, health insurance, and doctor’s expertise were completely recorded. Evaluation of clinical variables also showed that the main diagnosis, main procedure, secondary diagnosis and other procedures were documented in medical records in 98%, 97%, 88% and 75% respectively. Regarding the coding of the main diagnosis and the main procedure, 100%, secondary diagnosis 68% and other procedures 80% have been done.
Conclusion: Considering that some essential clinical variables for the implementation of DRG, especially co-morbidities, complications and other procedures are not recorded separately and completely, therefore it is necessary to define separate information elements in medical records and HIS for accurate recording of these variables and proper interaction between coders and doctors is established to increase the possibility of correct coding. It is also suggested that the DRG system be implemented in our country in a phased and gradual approach so that necessary changes are made in the documentation process and hospital information systems.
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