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Showing 7 results for Decision Support System
A Systematic Review Of Decision Support Systems: Effects On Health Care
Reza Safdari, Mahtab Karami, Mahboobeh Mirzaee, Azin Rahimi ,
Volume 7, Issue 1 (5-2013)
Abstract

Background and Aim: Decision support systems(DSSs) refer to one of the types of information technology applications that can help clinicians to make right and timely decisions about patients. The aim of this study is to learn more about DSSs and their applications and effects on health care.

Materials and Methods: In this systematic review, articles which were published between 2000 and 2012, which were available as full texts through databases and search engines -- such as PubMED, EBSCO host research, Google scholar and Yahoo -- and which were also of clinical-trial type were examined besides, certain books in this area were used as primary sources.

Conclusion : The findings show that DSSs were applied in five areas in health care, which had a significant effect on improving the process of care and the performance of providers. These areas are as follows: disease progress management(15.15%), care and treatment(27.27%), medication(27.27%), evaluation(27.27%), and preventation(12.12%). In general, improvement can be seen in three areas: quality of care and patient safety, cost effectiveness, and provider’s level of knowledge.


Utilization Of Evidence-Based Medicine Establishment By Hospital Information Systems In Iran
Fatemeh Rangrazjeddi , Alireza Moraveji , Fatemeh Abazari ,
Volume 7, Issue 6 (3-2014)
Abstract

 Background and Aim: Evidence Based Medicine (EBM) is the explicit use of current best evidence in making decisions about the care of individual patients. Hospital information system (HIS) can act as a bridge between medical data and medical knowledge through merging of patient's data, individual clinical knowledge and external evidences .The aim of this research was to determine the Capability to establish EBM by HIS.

 Materials and Methods: This descriptive cross-sectional study was carried out on HISs of 30 hospitals from March to October 2011. Data were collected using a researcher- constructed checklist including applicant’s background information as well as information based on research objectives. Validity of the checklist were assessed by the qualified specialists and then the data were analyzed using descriptive statistics and SPSS software.

 Results: HISs lacked the essential components for providing access to CDSS, Reference databases and internet-based health information in 19, 16 and 20 hospitals were 63.3%, 53.3% and 66.7%, respectively. Twenty-two hospitals (70%) had more than two-thirds of the essential components to access clinical and administrative data repositories 23 hospitals (76.7%) had at least one essential component to access contextual and case specific information.

 Conclusion: The Capability of HIS is better in order to place EBM in having access to the clinical and administrator data repositories while it needs more attention in other areas.

 


Service-Oriented Architecture as an Integration and Scalability Strategy of Clinical Decision Support Systems
Azita Yazdani, Reza Safdari, Roxana Sharifian, Maryam Zahmatkeshan, Marjan Ghazi Saeedi,
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.

Evaluation of Outpatient Electronic Prescription System Capabilities from the Perspective of Physicians in Specialized Polyclinics of Urmia Social Security Organization
Mohamad Jebraeily, Ali Rashidi, Taher Mohitmafi, Rooghayeh Muossazadeh,
Volume 14, Issue 6 (1-2021)
Abstract
Background and Aim: Electronic prescription systems can improve patient safety and the quality of health care services. These systems must provide the capabilities required to reduce medical errors and enhance the performance of health care providers. The purpose of this study is to evaluate the capabilities of the e-prescription system from the perspective of physicians in the polyclinics of the Social Security Organization (SSO) of Urmia.
Materials and Methods: The present study is a descriptive cross-sectional study that was conducted in 2020. The study population consisted of 82 physicians working in 3 polyclinics of the SSO in Urmia, which was determined by census. The instrument used in this study is a self-designed questionnaire that the validity of it was determined based on the opinions of experts and its reliability was evaluated by Cronbach's alpha coefficient. Data analysis was performed using SPSS software.
Results: The results showed that in the section of documentation and access to information, the highest score was related to the possibility of drug prescribe (4.58), request for examination and radiology (4.44). In terms of decision support capabilities, the highest score for providing alerts related to drug interactions (4.18) and controlling the amount of medication prescribed for chronic patients (3.83) and also in the field of technical capabilities, the highest score related to easy to use (3.87) and fit of user interface (3.66).
Conclusion: The e-prescription system under survey has gained fewer score in some capabilities, such as access to pharmaceutical information based on reliable sources, advice to treatment options based on original diagnosis and the customized system. Therefore the system developer should be improved capabilities of it through communicating properly with users and understanding their real needs.

Data Elements for Screening Major Depression in Ages 10 To 25 Using a Clinical Decision Support System
Leila Shahmoradi, Niloofar Kheradbin, Ahmad Reza Farzanehnejad, Niloofar Mohammadzadeh, Atefeh Ghanbari Jolfaei,
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.

Design of Clinical Decision Support System to Diagnose Breast Cancer: An Approach Using Data Mining
Mostafa Shanbehzadeh, Hadi Kazemi-Arpanahi, Raoof Nopour,
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.

The Effects of Implementing Clinical Decision Support Systems on the Diagnosis, Treatment and Management of Cancers: A Systematic Review
Saman Mohammadpour, Reza Rabiei, Elham Shabahrami, Kamyar Fathisalari, Maryam Khakzad, Mostafa Langarizadeh,
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.

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