A Framework for Neonatal Prematurity Information System Development Based on a Systematic Review on Current Registries: An Original Research.

Background: Registries are regarded as a just valuable fount of data on determining neonates suffering prematurity or low birth weight (LBW), ameliorating provided care, and developing studies. Objective: This study aimed to probe the studies, including premature infants' registries, adapt the needed minimum data set, and provide an offered framework for premature infants' registries. Material and Methods: For this descriptive study, electronic databases including PubMed, Scopus, Web of Science, ProQuest, and Embase/Medline were searched. In addition, a review of gray literature was undertaken to identify relevant studies in English on current registries and databases. Screening of titles, abstracts, and full texts was conducted independently based on PRISMA guidelines. The basic registry information, scope, registry type, data source, the purpose of the registry, and important variables were extracted and analyzed. Results: Fifty-six papers were qualified and contained in the process that presented 51 systems and databases linked in prematurity at the popular and government levels in 34 countries from 1963 to 2017. As a central model of the information management system and knowledge management, a prematurity registry framework was offered based on data, information, and knowledge structure. Conclusion: To the best of our knowledge, this is a comprehensive study that has systematically reviewed prematurity-related registries. Since there are international standards to develop new registries, the proposed framework in this article can be beneficial too. This framework is essential not only to facilitate the prematurity registry design but also to help the collection of high-value clinical data necessary for the acquisition of better clinical knowledge.

Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis.

The most frequent reason for individuals experiencing abdominal discomfort to be referred to emergency departments of hospitals is acute appendicitis, and the most frequent emergency surgery performed is an appendectomy. The purpose of this study was to design and develop an intelligent clinical decision support system for the timely and accurate diagnosis of acute appendicitis. The number of participants which is equal to 181 was chosen as the sample size for developing and evaluating neural networks. The information was gathered from the medical files of patients who underwent appendicectomies at Shahid Modarres Hospital as well as from the findings of their appendix samples' pathological tests. The diagnostic outcomes were then ascertained by the development and comparison of a Multilayer Perceptron network (MLP) and a Support Vector Machine (SVM) system in the MATLAB environment. The SVM algorithm functioned as the central processing unit in the Clinical Decision Support System (CDSS) that was built. The intelligent appendicitis diagnostic system was subsequently developed utilizing the Java programming language. Technical evaluation and system usability testing were both done as part of the software evaluation process. Comparing the output of the optimized artificial neural network of the SVM with the pathology result showed that the network's sensitivity, specificity, and accuracy were 91.7%, 96.2%, and 95%, respectively, in diagnosing acute appendicitis. Based on the existing standards and the opinions of general surgeons, and also comparing the results with the diagnostic accuracy of general surgeons, findings indicated the proper functioning of the network for the diagnosis of acute appendicitis. The use of this system in medical centers is useful for purposes such as timely diagnosis and prevention of negative appendectomy, reducing patient hospital stays and treatment costs, and improving the patient referral system.

Designing and evaluating the children's developmental motor disorders system: an experience from a developing country.

BACKGROUND: Developmental disorders are a prevalent problem in the health sector of low- and middle-income countries (LMICs), and children in these countries are at greater risk. A registry system is helpful and vital to monitoring and managing this disease. OBJECTIVE: The present study aims to develop an electronic registry system for children's developmental motor disorders. METHODS: The study was conducted between 2019 and 2020 in three phases. First, the requirements of the system were identified. Second, UML diagrams were first drawn using Microsoft Visio software. Then, the system was designed using the ASP.NET framework in Visual Studio 2018, and the C# programming language was used in the NET 4.5 technology platform. In the third phase, system usability was evaluated from the users' viewpoint. RESULTS: The findings of this research included system requirements, a conceptual model, and a web-based system. The client and system server connection was established through the IP/TCP communication protocol in a university physical network. End users approved the system with an agreement rate of 87.14%. CONCLUSION: The study's results can be used as a model for designing and developing systems related to children's developmental movement disorders in other countries. It is also suggested as a valuable platform for research and improving the management of this disease.

The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease.

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.

Feasibility study and determination of prerequisites of telecare programme to enhance patient management in lung transplantation: a qualitative study from the perspective of Iranian healthcare providers.

BACKGROUND: Non-adherence to treatment plans, follow-up visits and healthcare advice is a common obstacle in the management of lung transplant patients. This study aims to investigate experts' views on the needs and main aspects of telecare programmes for lung transplantation. DESIGN: A qualitative study incorporating an inductive thematic analysis. SETTING: Lung transplant clinic and thoracic research centre. PARTICIPANTS: Clinicians: four pulmonologists, two cardiothoracic surgeons, two general physicians, two pharmacotherapists, one cardiologist, one nurse and one medical informatician. METHOD: This study adopted a focus group discussion technique to gather experts' opinions on the prerequisites and features of a telecare programme in lung transplantation. All interviews were coded and combined into main categories and themes. Thematic analysis was performed to extract the key concepts using ATLAS.Ti. Ultimately, all extracted themes were integrated to devise a conceptual model. RESULTS: Ten focus groups with 13 participants were conducted. Forty-six themes and subthemes were extracted through the thematic analysis. The main features of the final programme were extracted from expert opinions through thematic analysis, such as continuous monitoring of symptoms, drug management, providing a specific care plan for each patient, educating patients module, creating an electronic medical record to collect patient information, equipping the system with decision support tools, smart electronic prescription and the ability to send messages to the care team. The prerequisites of the system were summarised in self-care activities, clinician's tasks and required technologies. In addition, the barriers and benefits of using a telecare system to enhance the quality of care were determined. CONCLUSION: Our investigation recognised the main factors that must be considered to design a telecare programme to provide ideal continuous care for lung transplant patients. Users should further explore the proposed model to support the development of telecare interventions at the point of care.

Evaluation of the PSO Metaheuristic Algorithm in Different Types of Sleep Apnea Diagnosis Using RR Intervals.

Background: Sleep apnea is one of the most common sleep disorders that facilitating and accelerating its diagnosis will have positive results on its future trend. Objective: This study aimed to diagnosis the sleep apnea types using the optimized neural network. Material and Methods: This descriptive-analytical study was done on 50 cases of patients referred to the sleep clinic of Imam Khomeini Hospital in Tehran, including 11 normal, 13 mild, 17 moderate and 9 severe cases. At the first, the data were pre-processed in three stages, then The Electrocardiogram (ECG) signal was decomposed to 8 levels using wavelet transform convert and 6 nonlinear features for the coefficients of this level and 10 features were calculated for RR Intervals. For apnea categorizing classes, the multilayer perceptron neural network was used with the backpropagation algorithm. For optimizing Multi-layered Perceptron (MLP) weights, the Particle Swarm Optimization (PSO) evolutionary optimization algorithm was used. Results: The simulation results show that the accuracy criterion in the MLP network is allied with the Backpropagation (BP) training algorithm for different types of apnea. By optimizing the weights in the MLP network structure, the accuracy criterion for modes normal, obstructive, central, mixed was obtained %96.86, %97.48, %96.23, and %96.44, respectively. These values indicate the strength of the evolutionary algorithm in improving the evaluation criteria and network accuracy. Conclusion: Due to the growth of knowledge and the complexity of medical decisions in the diagnosis of the disease, the use of artificial neural network algorithms can be useful to support this decision.

An evaluation of the quality of COVID-19 websites in terms of HON principles and using DISCERN tool.

BACKGROUND: As many people relied on information from the Internet for official scientific or academically affiliated information during the COVID-19 pandemic, the quality of information on those websites should be good. OBJECTIVE: The main purpose of this study was to evaluate a selection of COVID-19-related websites for the quality of health information provided. METHOD: Using Google and Yahoo, 36 English language websites were selected, in accordance with the inclusion criteria. The two tools were selected for evaluation were the Health on the Net (HON) Code and the 16-item DISCERN tool. RESULTS: Most websites (39%) were related to information for the public, and a small number of them (3%) concerned screening websites in which people could be informed of their possible condition by entering their symptoms. The result of the evaluation by the HON tool showed that most websites were reliable (53%), and 44% of them were very reliable. Based on the assessment results of the Likert-based 16-item DISCERN tool, the maximum and minimum values for the average scores of each website were calculated as 2.44 and 4.25, respectively. CONCLUSION: Evaluation using two widely accepted tools shows that most websites related to COVID-19 are reliable and useful for physicians, researchers and the public.

Designing and Evaluating a Nutrition Recommender System for Improving Food Security in a Developing Country.

BACKGROUND: Due to the increased price of foods in recent years and the diminished food security in Iran, nutrition recommender systems can suggest the most suitable and affordable foods and diets to users based on their health status and food preferences. Objective: The present study aimed to design and evaluate a recommender system to suggest healthy and affordable meals and provide a tele-nutrition consulting service. METHODS: This applied three-phase study was conducted in 2020. In the first stage, the food items' daily prices were extracted from credible sources, and accordingly, meals were placed in three price categories. After conducting a systematic review of similar systems, the requirements and data elements were specified and confirmed by 10 nutritionists and 10 health information management and medical informatics experts. In the second phase, the software was designed and developed based on the findings. In the third phase, system usability was evaluated by four experts based on Nielsen's heuristic evaluation. RESULTS: Initially, 72 meals complying with nutritional principles were placed in three price categories. Following a literature review and expert survey, 31 data elements were specified for the system, and the experts confirmed system requirements. Based on the information collected in the previous stage, the Web-based software TanSa in the Persian language was designed, developed, and presented on a unique domain. During the evaluation, the mean severity of the problems associated with Nielsen's 10 principles was 1.2, which is regarded as minor. CONCLUSION: To promote food security, the designed system recommends healthy, nutritional, and affordable meals to individuals and households based on user characteristics.

Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review.

BACKGROUND: Machine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high volume of collected data in the lung transplant process makes it possible to extract hidden patterns by applying machine learning methods. Our study aims to investigate the application of machine learning methods in lung transplantation. METHOD: A systematic search was conducted in five electronic databases from January 2000 to June 2022. Then, the title, abstracts, and full text of extracted articles were screened based on the PRISMA checklist. Then, eligible articles were selected according to inclusion criteria. The information regarding developed models was extracted from reviewed articles using a data extraction sheet. RESULTS: Searches yielded 414 citations. Of them, 136 studies were excluded after the title and abstract screening. Finally, 16 articles were determined as eligible studies that met our inclusion criteria. The objectives of eligible articles are classified into eight main categories. The applied machine learning methods include the Support vector machine (SVM) (n = 5, 31.25%) technique, logistic regression (n = 4, 25%), Random Forests (RF) (n = 4, 25%), Bayesian network (BN) (n = 3, 18.75%), linear regression (LR) (n = 3, 18.75%), Decision Tree (DT) (n = 3, 18.75%), neural networks (n = 3, 18.75%), Markov Model (n = 1, 6.25%), KNN (n = 1, 6.25%), K-means (n = 1, 6.25%), Gradient Boosting trees (XGBoost) (n = 1, 6.25%), and Convolutional Neural Network (CNN) (n = 1, 6.25%). Most studies (n = 11) employed more than one machine learning technique or combination of different techniques to make their models. The data obtained from pulmonary function tests were the most used as input variables in predictive model development. Most studies (n = 10) used only post-transplant patient information to develop their models. Also, UNOS was recognized as the most desirable data source in the reviewed articles. In most cases, clinicians succeeded to predict acute diseases incidence after lung transplantation (n = 4) or estimate survival rate (n = 4) by developing machine learning models. CONCLUSION: The outcomes of these developed prediction models could aid clinicians to make better and more reliable decisions by extracting new knowledge from the huge volume of lung transplantation data.

Design, implementation, and evaluation of an innovative intelligence information management system for premature infants.

Introduction: Low birth weight is the most important condition of neonatal community health and the main cause of neonates' mortality. Identifying the indexes associated with this condition, and factors to prevent, and managing related data can help reduce the birth of premature infants to reduce the mortality rate due to this condition. The goal of present study was to design, implement and evaluate an innovative intelligence information management system for premature infants. Material and method: The present study was a multidisciplinary research that was done in 2019 to 2021 in four integrated phases in Iran. The first phase aimed to compare the current status of registration systems of premature infants through a systematic review and semi-structured interviews by using the Delphi model Then the minimum data set was determined and was designed a proposed model based on it. In the second phase, the structure and how the user interacts with the system were determined, and, using Microsoft Visio software, Unified Modeling Language diagrams were drawn to define the logical relationship of data. In the third phase, the system was developed, and finally in the last phase, in three methods, users' views on the usability of the system were evaluated. Results: The findings of this study included 233 essential data elements that were placed in two main groups of essential data, and the system was approved by end users for 87.73% consent and 67.19% satisfaction for SUMI (Software Usability Measurement Inventory) and 7.97 of 9 in QUIS questionnaire. Conclusion: This research's results can be beneficial and functional such as a complete sample for design and development of other systems concerned to health systems.

Telemedicine in lung transplant to improve patient-centered care: A systematic review.

OBJECTIVES: Long-term care combined with complex follow-up processes is among the essential needs of lung transplantation. Therefore, Telemedicine-based strategies can provide an effective approach for both patients and clinicians by applying remote patient monitoring. Hence, the main objective of this study was to investigate Telemedicine and telehealth usage in lung transplantation. METHOD: A systematic review was conducted in four databases using keywords. Eligible studies were all English papers that developed Telemedicine-based programs to enhance patient care in lung organ transplantation. The interventions were analyzed analysis to determine the main descriptive areas. The quality of the included articles was evaluated using Mixed Methods Appraisal Tool (MMAT) tool by two authors. RESULTS: Of the 261 retrieved articles, 27 met our inclusion criteria. Of these, 22 studies were devoted to the post-transplantation phase. All articles were published from 2002 to 2021 and the trend of publications has increased in recent years. Most of the studies were conducted in the United States and Canada. All eligible studies can be categorized into five types of Telemedicine interventions, 15 (55.56%) articles devoted to Telemonitoring, four (14.81%) for Teleconsultation, four (14.81%) articles for Telerehabilitation, three (11.11%) articles for Telespirometery, and one (3.70%) article were done regarding Tele-education. CONCLUSION: This integrated review provides researchers with a new understanding of Telemedicine-based care solutions. Findings show that remote patient care in lung transplantation includes various aspects, especially self-care improvement.

The silent crisis of child abuse in the COVID-19 pandemic: A scoping review.

Background and Aims: The global outbreak of COVID-19 has become an international concern. The lives of children are severely affected by COVID-19 pandemic. There is evidence of a pandemic impact on violence against children. This scoping review study aimed to investigate the effects of the COVID-19 pandemic on child abuse. Methods: We searched PubMed, Scopus, and Web of Science databases to retrieve related studies. Regarding the recent incident of COVID-19, the articles were reviewed from 2019 to June 1, 2021. The terms Child abuse and COVID-19 were used in the precise search technique of each database. The search techniques were created to work with any scientific database that used the keywords given. Results: In the initial search of scientific databases, 568 articles were retrieved. After applying the inclusion and exclusion criteria during the screening process, 16 papers were included in the scoping review. Twelve articles have mentioned the increase of physical, psychological, and neglect types of abuse. However, sexual violence has not been reported in any of the articles. Four articles reported a reduction in the incidence of child abuse. Conclusion: During the COVID-19 pandemic, a crisis occurred in the form of an upsurge in violence toward children, since limits made to diminish the virus, in general, increased the danger to children. Numerous factors such as stress, poverty, financial situation, history of violence, school closures, and lack of contact with support organizations contribute to this phenomenon. Social action and support needed is the right of every child in need in this critical situation.

Pragmatic solutions to enhance self-management skills in solid organ transplant patients: systematic review and thematic analysis.

BACKGROUND: In organ transplantation, all patients must follow a complex treatment regimen for the rest of their lives. Hence, patients play an active role in the continuity of the care process in the form of self-management tasks. Thus, the main objective of our study was to investigate the pragmatic solutions applied by different studies to enhance adherence to self-management behaviors. METHOD: A systematic review was conducted in five databases from 2010 to August 2021 using keywords. Eligible studies were all English papers that developed self-management programs to enhance patient care in solid organ transplantation. The interventions were analyzed using thematic analysis to determine the main descriptive areas. The quality of the included articles was evaluated using the research critical appraisal program (CASP) tool. RESULTS: Of the 691 retrieved articles, 40 met our inclusion criteria. Of these, 32 studies were devoted to the post-transplantation phase. Five main areas were determined (e-health programs for telemonitoring, non-electronic educational programs, non-electronic home-based symptom-monitoring programs, electronic educational plans for self-monitoring, and Telerehabilitation) according to thematic analysis. Most studies (72.5%) declared that developed programs and applied solutions had a statistically significant positive impact on self-management behavior enhancement in transplant patients. CONCLUSION: The results showed that an effective solution for improving organ transplantation needs patient collaboration to address psychological, social, and clinical aspects of patient care. Such programs can be applied during candidate selection, waiting list, and after transplantation by putting the patient at the center of care.

Global perspective on pediatric growth hormone registries: a systematic review.

BACKGROUND: Registries are considered valuable data sources for identification of pediatric conditions treated with growth hormone (GH), and their follow-up. Currently, there is no systematic literature review on the scope and characteristics of pediatric GH registries. Therefore, the purpose of this systematic review is to identify worldwide registries reported on pediatric GH treatment and to provide a summary of their main characteristics. CONTENT: Pediatric GH registries were identified through a systematic literature review. The search was performed on all related literature published up to January 30th, 2021. Basic information on pediatric GH registries, their type and scope, purpose, sources of data, target conditions, reported outcomes, and important variables were analyzed and presented. SUMMARY: Twenty two articles, reporting on 20 pediatric GH registries, were included in this review. Industrial funding was the most common funding source. The main target conditions included in the pediatric GH registries were: growth hormone deficiency, Turner syndrome, Prader Willi syndrome, small for gestational age, idiopathic short stature, and chronic renal insufficiency. The main objectives in establishing and running pediatric GH registries were assessing the safety and effectiveness of the treatment, describing the epidemiological aspects of target growth conditions and populations, serving public health surveillance, predicting and measuring treatment outcomes, exploring new and useful aspects of GH treatment, and improving the quality of patient care. OUTLOOK: This systematic review provides a global perspective on pediatric GH registries which can be used as a basis for the design and development of new GH registry systems at both national and international levels.

Validity and reliability of the Persian version of the Patient readiness to engage in health information technology (PRE-HIT) instrument.

BACKGROUND: The Patient readiness to engage in health information technology (PRE-HIT) is a conceptually and psychometrically validated questionnaire survey tool to measure willingness of patients with chronic conditions to use health information technology (HIT) resources. OBJECTIVES: This study aimed to translate and validate a health information technology readiness instrument, the PRE-HIT instrument, into the Persian language. METHODS: A rigorous process was followed to translate the PRE-HIT instrument into the Persian language. The face and content validity was validated by impact score, content validity index (CVI) and content validity ratio (CVR). The instrument was used to measure readiness of 289 patients with chronic diseases to engage with digital health with a four point Likert scale. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) was used to check the validity of structure. The convergent and discriminant validity, and internal reliability was expressed by average variance extracted (AVE), construct reliability (CR), maximum shared squared variance (MSV), average shared square variance (ASV), and Cronbach's alpha coefficient. Independent samples, t-test and one-way ANOVA were used respectively to compare the impact of sex, education and computer literacy on the performance of all PRE-HIT factors. RESULTS: Eight factors were extracted: health information needs, computer anxiety, computer/internet experience and expertise, preferred mode of interaction, no news is good news, relationship with doctor, cell phone expertise, and internet privacy concerns. They explained 69% of the total variance and the KMO value was 0.79; Bartlett's test of sphericity was also statistically significant (sig < 0.001). The communality of items was higher than 0.5. An acceptable model fit of the instrument was achieved (CFI = 0.943, TLI = 0.931, IFI = 0.944, GFI = 0.893, RMSEA ≤ 0.06, χ2/df = 1.625, df = 292, P-value ≤ 0.001). The Cronbach's alpha coefficient achieved a satisfactory level of 0.729. The AVE for all factors was higher than 0.50 except for PMI (0.427) and CIEE (0.463) and also the CR for all factors was higher than 0.7, therefore, the convergent validity of the instrument is adequate. The MSV and ASV values for each factor were lower than AVE values; therefore, the divergent validity was acceptable. CONCLUSION: The Persian version of the PRE-HIT was empirically proved for its validity to assess the level of readiness of patients to engage with digital health.

Myocardial Infarction Prediction and Estimating the Importance of its Risk Factors Using Prediction Models.

Background: According to World Health Organization (WHO), cardiovascular diseases (CVDs) are the leading cause of death globally. Although significant progress has been made in the diagnosis of CVDs, more investigation can be helpful. Therefore, this study aimed to predict the risk of myocardial infarction (MI) using data mining algorithms. Methods: The applied data were related to the admitted patients in Rajaei specialized cardiovascular hospital located in Tehran. At first, a literature review and interview with a cardiologist were conducted to understand MI. Then, data preparation (cleaning and normalizing the data) was performed. After all, different classification algorithms were applied in IBM SPSS Modeler (14.2) software on the prepared data; and, power of the applied algorithms and the importance of the risk factors in predicting the probability of getting involved with MI was calculated in the mentioned software. Results: This study was able to predict MI % 75.28 and 77.77% in terms of accuracy and sensitivity, respectively. The results also revealed that cigarette consumption, addiction, blood pressure, and cholesterol were the most important risk factors in predicting the probability of getting involved with MI, respectively. Conclusions: Predicting studies aim to support rather than replace clinical judgment. Our prediction models are not sufficiently accurate to supplant decision-making by physicians but have considerable tips about MI risk factors.

Development and validation of the Neonatal Abstinence Syndrome Minimum Data Set (NAS-MDS): a systematic review, focus group discussion, and Delphi technique.

OBJECTIVES: Neonatal abstinence syndrome (NAS) is a combination of symptoms in infants exposed to any variety of substances in utero. Information systems and registries help to collect information about these patients; however, there is always a deep gap between complete and accurate information to be collected, understood, and applied in the health care system; thus, defining a minimum data sets (MDS) as one of the primarily steps of designing a registry system is essential. The aim of this study was to develop an MDS of the registry for infants with NAS in Iran. METHODS: This research is a descriptive cross-sectional study. In this study, three steps were carried out to develop the MDS including systematic review, Delphi technique, and focus group discussion. A systematic review was conducted in relevant databases to identify appropriate related data. In the second phase, a focus group discussion was used to classify the extracted data elements by contributing neonatologists. Finally, data elements were chosen through the decision Delphi technique in two distinct rounds. Collected data were analyzed using SPSS 22 (SPSS Inc., Chicago, IL). RESULTS: By reviewing related papers and available NAS registries in other countries, 145 essential data elements were identified. They were classified into two main categories based on the eight experts' opinions including maternal with two sections and infant with two sections. After applying two rounds of Delphi technique, the final data elements for maternal and infant categories were 42 and 31, respectively. Thus, on completion of the survey, 73 data elements were approved. CONCLUSION: The proposed MDS for NAS can help to store an accurate and comprehensive data, document medical records, integrate them with other information systems and registries, and communicate with other healthcare providers and healthcare centers. This MDS can contribute to the provision of high-quality care and better clinical decisions.

Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects.

Background: Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine. Methods: This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed. Results: We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed. Conclusion: The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Using data mining techniques to fight and control epidemics: A scoping review.

The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge. Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping review. The review results showed that the most favorite DM belonged to Natural language processing (22%) and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.