Serious educational games for children: A comprehensive framework.

Introduction: Serious educational games are digital games designed to support teaching or learning objectives that have become popular among children. However, a set of principles is needed to develop a successful educational game. Therefore, this study aimed to provide a comprehensive and valid framework for designing children's serious educational games. Methods: The conceptual framework is developed in two phases. First, a scoping review was conducted in PubMed, Ovid (APA PsycInfo), EMBASE, Scopus, Web of Science, ProQuest, and gray literature on August 1, 2022. Papers in English that reported the serious educational games' principles for children were included. Second, the extracted data from the previous step was reviewed and discussed by the research team to develop the initial framework. Then, it was distributed to 20 experts with relevant knowledge and experience in two rounds to validate and apply their comments within the framework. Results: Of the 12916 papers identified, 15 were included in this study. In the proposed framework named CoDHP, the results were classified into four topics, including (a) content aspects, (b) design requirements, (c) highlighted attributes, and (d) children's preferences. Content aspects comprise four classes (goals and limits, child learning content, learning context, and a long-term program) with 16 principles. Design requirements contain 11 classes (stories and storylines, player characters, game mechanics, interactivity, game challenge, game rules, game help, entertaining games, user interface, accessibility, and setting) with 47 principles, of which 15 are highlighted. Regarding children's preferences for the game elements, various similarities and differences were extracted. For instance, both girls and boys prefer fun and popular games. Based on experts' comments, 21 supplementary principles were recommended to complete the content and design requirements. Conclusion: Game designers or researchers can use the proposed framework as a formative guide to design successful serious games or evaluate children's digital games.

An affordable and immersive virtual reality-based exercise therapy in forward head posture.

Forward Head Posture (FHP) is one of the most commonly occurring musculoskeletal abnormalities. Despite exercise therapy being an effective approach for FHP treatment, it can be long, monotonous, and tedious. Virtual reality (VR) can be used as an innovative solution to address these challenges. We designed an affordable and immersive VR-based exercise therapy (VRET) system for FHP correction. The VRET contents (i.e., exercises and VR scenarios) were determined by physiotherapists and game designers at the focus group meetings. Hardware requirements include a VR box, smartphone, and sensors (i.e., a smartphone accelerometer and an affordable Inertial Measurement Unit (IMU)) to measure head motions and transfer them via Wi-Fi to the VRET system. The IMU was designed using the MPU6050, Arduino Nano, and ESP8266-01S. Gwet's AC1, Game Experience Questionnaires (GEQ), and System Usability Scale (SUS) were used to measure intra-rater reliability, user experience, and system usability, respectively. The determined exercises, including Capital Flexion-Extension and Chin Tuck, were designed in the form of a shooting game. A physiotherapist and twenty-one FHP individuals took part in evaluating the system. High precision was obtained for the designed IMU (i.e., pitch and roll < 0.1° and yaw < 1.3 °). Gwet's AC1 and SUS results showed very good intra-rater reliability (coefficient = 0.892) and excellent usability (score = 87.14), respectively. According to the mean scores of the GEQ, participants were confident about competence, immersion, flow, and positive affect components. The development of low-cost VRET systems for FHP correction is a step towards facilitating rehabilitation challenges by providing positive experiences for users as well as helping them perform therapeutic exercises correctly.

Prevalence of gram-negative bacteria and their antibiotic resistance in neonatal sepsis in Iran: a systematic review and meta-analysis.

BACKGROUND: Neonatal sepsis, particularly gram-negative (GN) bacteria-induced, is a significant cause of morbidity and mortality in newborns. Healthcare professionals find this issue challenging because of antibiotic resistance. This study aims to combine findings to identify the prevalence of GN bacteria and their antibiotic resistance in Iranian neonates with sepsis. METHODS: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The literature search was performed through international databases, including (PubMed/MEDLINE, EMBASE, Scopus, and Web of Science), Iranian local databases (Magiran, Iranmedex, Irandoc, Scimed, and SID), and the first 100 records of Google Scholar. Analytical cross-sectional study checklist from the Joanna Briggs Institute (JBI) was used for the quality assessment of included studies. Comprehensive Meta-Analysis Software Version 2 was used to conduct the meta-analysis. The between-study heterogeneity was investigated by I2 statistics. RESULTS: The prevalence of GN bacteria was estimated to be 53.6% [95% CI: 45.9- 61.1: P = 0.362] in Iranian neonates with sepsis, based on 31 studies with a sample size of 104,566. klebsiella pneumoniae (K.pneumonia) (23.2% [95% CI: 17.5-30.0, P < 0.001]) followed by Escherichia coli (E.coli) (13.5% [95% CI: 9.4-18.9, P < 0.001]) were more prevalent among GN bacteria. The highest resistance in K.pneumoniae was observed in Cefixime (80.6%, [95% CI: 56.3-93.1, P = 0.018]). E.coli showed greater resistance to Ampicillin (61.8%, [95% CI: 44.2-76.5, P = 0.188]. The prevalence of GN bacteria in Iranian neonates with sepsis has a decreasing trend based on the year, as shown by a meta-regression model (P < 0.0004). CONCLUSION: GN pathogens, particularly K.pneumoniae, and E.coli, are the leading cause of neonatal sepsis in Iran. GN bacteria showed the highest resistance to Third-generation cephalosporin and Aminoglycosides.

The effects of virtual reality-based bronchoscopy simulator on learning outcomes of medical trainees: A systematic review.

Background and Aims: Conventional medical training routes of bronchoscopy may decrease patients' comfort and increase procedure-related morbidity. Virtual reality (VR)-based bronchoscopy is a beneficial and safe solution for teaching trainees. The aim of this systematic review was to study the effectiveness of VR-based bronchoscopy simulators on the learning outcomes of medical trainees. Methods: Well-known sources (i.e., Scopus, ISI Web of Science, and Medline via PubMed) were systematically searched using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines on December, 2021. Peer-reviewed English papers that used VR-based simulation for bronchoscopy training were included. The articles that were studying other technologies, or those that were unrelated to the topic, were excluded. The risk of bias was assessed using the Joanna Briggs Institute checklists for quasi-experimental studies and randomized controlled trials (RCTs). Results: Out of 343 studies, 8 of them met our inclusion criteria. An appropriate control group and statistical analysis were the most common and unavoidable sources of bias in included non-RCTs, and lack of blinding in participants was the most common source of bias in RCTs. The included studies evaluated learning outcomes regarding dexterity (N = 5), speed (N = 3), the accuracy of procedures (N = 1), and the need for verbal assistance (N = 1). Based on the results, 100% (5/5) and 66% (2/3) of studies showed that the use of VR-based simulation on the learning outcomes of medical trainees led to improvement in manual ability (i.e., dexterity) and swiftness of execution (i.e., speed in performance), respectively. Additionally, improving the accuracy of subjects' performance, and reducing the need for verbal guidance and physical assistance was reported in studies that evaluated these variables. Conclusion: VR bronchoscopy simulator as a training method for teaching medical trainees, especially for novices has the potential to improve medical trainees' performance and reduce complications. Further studies are needed to evaluate the positive effects of VR-based simulation on the learning outcomes of medical trainees.

Clinical Risk Factors for Early-Onset Sepsis in Neonates: An International Delphi Study.

Background: Despite growing evidence, there is still uncertainty about potentially modifiable risk factors for neonatal early-onset sepsis (EOS). This study aimed to identify potential clinical risk factors for EOS based on a literature review and expert opinions. Methods: A literature search was conducted in PubMed (MEDLINE), Cochrane, Embase, and Scopus databases. Articles in English, published up to May 2021, on clinical risk factors for neonatal EOS were included. Initially, a questionnaire on risk factors for EOS was developed and validated. The fuzzy Delphi method (FDM) was used to formulate the final version of the questionnaire. The validity of the risk factors was assessed using the Chi square test. P<0.05 was considered statistically significant. Results: In the review phase, 30 risk factors were approved by two neonatologists and included in the FDM phase. In total, 25 risk factors met the consensus criteria and entered the validation phase. During the observational study, 114 neonates (31 with and 83 without EOS) were evaluated for two months. The results of the Chi square test showed that cesarean section was not a significant risk factor for EOS (P=0.862). The need for mechanical ventilation and feed intolerance was observed in about 70% of neonates with EOS, and therefore considered significant risk factors for EOS (P<0.001). Finally, 26 potential clinical risk factors were determined. Conclusion: Neonatal-related risk factors for EOS were birth weight, one-min Apgar score, and prematurity. Maternal-related risk factors were gestational age and urinary tract infection. Delivery-related risk factors were premature rupture of membranes, chorioamnionitis, and intrapartum fever.

Interoperability of heterogeneous health information systems: a systematic literature review.

BACKGROUND: The lack of interoperability between health information systems reduces the quality of care provided to patients and wastes resources. Accordingly, there is an urgent need to develop integration mechanisms among the various health information systems. The aim of this review was to investigate the interoperability requirements for heterogeneous health information systems and to summarize and present them. METHODS: In accordance with the PRISMA guideline, a broad electronic search of all literature was conducted on the topic through six databases, including PubMed, Web of science, Scopus, MEDLINE, Cochrane Library and Embase to 25 July 2022. The inclusion criteria were to select English-written articles available in full text with the closest objectives. 36 articles were selected for further analysis. RESULTS: Interoperability has been raised in the field of health information systems from 2003 and now it is one of the topics of interest to researchers. The projects done in this field are mostly in the national scope and to achieve the electronic health record. HL7 FHIR, CDA, HIPAA and SNOMED-CT, SOA, RIM, XML, API, JAVA and SQL are among the most important requirements for implementing interoperability. In order to guarantee the concept of data exchange, semantic interaction is the best choice because the systems can recognize and process semantically similar information homogeneously. CONCLUSIONS: The health industry has become more complex and has new needs. Interoperability meets this needs by communicating between the output and input of processor systems and making easier to access the data in the required formats.

Developing a model to predict neonatal respiratory distress syndrome and affecting factors using data mining: A cross-sectional study.

Background: One of the major challenges that hospitals and clinicians face is the early identification of newborns at risk for adverse events. One of them is neonatal respiratory distress syndrome (RDS). RDS is the widest spared respiratory disorder in immature newborns and the main source of death among them. Machine learning has been broadly accepted and used in various scopes to analyze medical information and is very useful in the early detection of RDS. Objective: This study aimed to develop a model to predict neonatal RDS and affecting factors using data mining. Materials and Methods: The original dataset in this cross-sectional study was extracted from the medical records of newborns diagnosed with RDS from July 2017-July 2018 in Alzahra hospital, Tabriz, Iran. This data includes information about 1469 neonates, and their mothers information. The data were preprocessed and applied to expand the classification model using machine learning techniques such as support vector machine, Naïve Bayes, classification tree, random forest, CN2 rule induction, and neural network, for prediction of RDS episodes. The study compares models according to their accuracy. Results: Among the obtained results, an accuracy of 0.815, sensitivity of 0.802, specificity of 0.812, and area under the curve of 0.843 was the best output using random forest. Conclusion: The findings of our study proved that new approaches, such as data mining, may support medical decisions, improving diagnosis in neonatal RDS. The feasibility of using a random forest in neonatal RDS prediction would offer the possibility to decrease postpartum complications of neonatal care.

Digital games and virtual reality applications in child abuse: A scoping review and conceptual framework.

Child abuse refers to any form of maltreatment, including physical abuse, sexual abuse, emotional abuse, and neglect that occurs to children under 18 years of age. Digital games and virtual reality (VR) can be used as beneficial solutions for dealing with child maltreatment concerns. This study aimed to present a conceptual framework for showing the applications of these technologies in managing child abuse. The framework is developed in two stages: (1) a scoping review to gather digital games and VR applications for child abuse issues through the search in PubMed, Ovid (APA PsycInfo), Scopus, Web of Science, ProQuest, Institute of Electrical and Electronics Engineers (IEEE), Cochrane Database of Systematic Reviews, and grey literature and (2) developing a conceptual framework based on the review results and validating it by 12 experts. The proposed conceptual framework shows that digital games and VR have been used for six main topics: (1) medical education, (2) prevention, (3) screening, (4) diagnosis, (5) treatment, and (6) forensic medicine in response to child abuse issues. Studies have more focused on child sexual abuse prevention, behavioral monitoring of sexual offenders in forensic medicine, and knowledge or performance assessment of students in medical education. Serious games (SGs), computer simulation, and immersive VR were common technologies for children, students, and forensic medicine, respectively. The experts believe the combination of immersive features of VR with SGs can further encourage user engagement. It appears that digital games and VR can play a positive role in child abuse management. Given the extensive capabilities of these technologies, further studies are needed to show all their potential applications for child abuse problems.

Oncology Informatics for Lynch Syndrome Research and Care: A Literature Review.

PURPOSE: This study aims to review and evaluate available informatics platforms for research and management purposes of Lynch syndrome (LS) to identify gaps and needs for future development. METHODS: LS informatics tools were identified through literature search in four publication databases (1 and Scopus). First, the LS and functional elements of every informatics tools for LS were introduced. Then, current existing LS informatics tools were reviewed and explained. RESULTS: A detailed review of implemented studies shows that many types of informatics platforms are available for LS management (ie, prediction model, clinical decision support system, database website, and other tools for research and management purposes of LS). Moreover, several dimensions of existing LS informatics tools were discussed and features and positive findings were reported. CONCLUSION: Reviewing the literature reveals that several LS informatics tools were focused on gene-specific estimate, cancer risk prediction, identifying/screening patients, supporting personalized care of individuals with LS, and storing mismatch repair mutations information. Nevertheless, these platforms do not fully cover the care and research purposes. For instance, future developments of LS tools require more attention to dynamic knowledgebase, extra-colonic lynch-related cancers on the basis of precision medicine, variants of unknown significance, and support from diagnosis to surveillance for patient follow-up. Insights and recommendations provided in this study could help researchers and developers to meet the existing challenges in future developments.

A framework for information technology-based management against COVID-19 in Iran.

BACKGROUND: The COVID-19 pandemic has become a global concern. Iran is one of the countries affected most by the SARS-CoV-2 outbreak. As a result, the use of information technology (IT) has a variety of applications for pandemic management. The purpose of this study was to develop a conceptual framework for responding to the COVID-19 pandemic via IT management, based on extensive literature review and expert knowledge. METHODS: The conceptual framework is developed in three stages: (1) a literature review to gather practical experience with IT applications for managing the COVID-19 pandemic, (2) a study of Iranian documents and papers that present Iran's practical experience with COVID-19, and (3) developing a conceptual framework based on the previous steps and validating it through a Delphi approach in two rounds, and by 13 experts. RESULTS: The proposed conceptual framework demonstrates that during pandemics, 22 different types of technologies were used for various purposes, including virtual education, early warning, rapid screening and diagnosis of infected individuals, and data management. These objectives were classified into six categories, with the following applications highlighted: (1) Prevention (M-health, Internet search queries, telehealth, robotics, Internet of things (IoT), Artificial Intelligence (AI), big data, Virtual Reality (VR), social media); (2) Diagnosis (M-health, drones, telehealth, IoT, Robotics, AI, Decision Support System (DSS), Electronic Health Record (EHR)); (3) Treatment (Telehealth, M-health, AI, Robotic, VR, IoT); (4) Follow-up (Telehealth, M-health, VR), (5) Management & planning (Geographic information system, M-health, IoT, blockchain), and (6) Protection (IoT, AI, Robotic and automatic vehicles, Augmented Reality (AR)). In Iran, the use of IT for prevention has been emphasized through M-health, internet search queries, social media, video conferencing, management and planning objectives using databases, health information systems, dashboards, surveillance systems, and vaccine coverage. CONCLUSIONS: IT capabilities were critical during the COVID-19 outbreak. Practical experience demonstrates that various aspects of information technologies were overlooked. To combat this pandemic, the government and decision-makers of this country should consider strategic planning that incorporates successful experiences against COVID-19 and the most advanced IT capabilities.

A mechatronics data collection, image processing, and deep learning platform for clinical posture analysis: a technical note.

Static and dynamic posture analysis was a critical clinical examination in physiotherapy and rehabilitation. It was a time-consuming task for clinicians, so a semi-automatic method can facilitate this process as well as provide well-documented medical records and strong infrastructure for deep learning scenarios. The current research presents a mechatronics platform for static and real-time dynamic posture analysis, which consisted of hybrid computational modules. Our study was a developmental and applied research according to a system development life cycle. The designed modules are as follows: (1) a mechanical structure includes patient place, 360-degree engine, mirror, laser, distance meter, and cams; (2) a software module includes data collection, electronic medical record, semi-automatic image analysis, annotation, and reporting, and (3) a network to exchange raw data with deep learning server. Patients were informed about the research by their healthcare provider and all data were transformed into a Fourier format, in which the patients remained autonomous without a bit of information. The results show acceptable reliability and validity of the instruments. Also, a telerehabilitation application was designed to cover the patients after diagnosis. We suggest a longer time for data acquisition. It will lead to a more accurate and fully automated dynamic posture analysis. The result of this study suggest that the designed mechatronics device used in conjunction with smartphone application is a valid tool that can be used to obtain reliable measurements.

Applications of virtual and augmented reality in infectious disease epidemics with a focus on the COVID-19 outbreak.

The pandemics of major infectious diseases often cause public health, economic, and social problems. Virtual reality (VR) and augmented reality (AR), as two novel technologies, have been used in many fields for emergency management of disasters. The objective of this paper was to review VR and AR applications in the emergency management of infectious outbreaks with an emphasis on the COVID-19 outbreak. A search was conducted in MEDLINE (PubMed), Embase, IEEE, Cochrane Library, Google Scholar, and related websites for papers published up to May 2, 2020. The VR technology has been used for preventing or responding to infections by simulating human behaviors, infection transmission, and pathogen structure as a means for improving skills management and safety protection. Telehealth, telecommunication, and drug discovery have been among the other applications of VR during this pandemic. Moreover, AR has also been used in various industries, including healthcare, marketing, universities, and schools. Providing high-resolution audio and video communication, facilitating remote collaboration, and allowing the visualization of invisible concepts are some of the advantages of using this technology. However, VR has been used more frequently than AR in the emergency management of previous infectious diseases with a greater focus on education and training. The potential applications of these technologies for COVID-19 can be categorized into four groups, i.e., 1) entertainment, 2) clinical context, 3) business and industry, and 4) education and training. The results of this study indicate that VR and AR have the potential to be used for emergency management of infectious diseases. Further research into employing these technologies will have a substantial impact on mitigating the destructive effects of infectious diseases. Making use of all the potential applications of these technologies should be considered for the emergency management of the current pandemic and mitigating its negative impacts.

An intelligent system for improving adherence to guidelines on acute stroke.

OBJECTIVES: A timely, accurate assessment and decision-making process is essential for the diagnosis and treatment of the acute stroke, which is the world's third leading cause of death. This process is often performed using the traditional method that increases the complexity, duration, and medical errors. The present study aimed to design and evaluate an intelligent system for improving adherence to the guidelines on the assessment and treatment of acute stroke patients. METHODS: Decision-making rules and data elements were used to predict the severity and to treat patients according to the specialists' opinions and guidelines. A system was then developed based on the intelligent decision-making algorithms. The system was finally evaluated by measuring the accuracy, sensitivity, specificity, applicability, performance, esthetics, information quality, and completeness and rates of medical errors. The segmented regression model was used to evaluate the effect of systems on the level and the trend of guideline adherence for the assessment and treatment of acute stroke. RESULTS: Fifty-three data elements were identified and used in the data collection and comprehensive decision-making rules. The rules were organized in a decision tree. In our analysis, 150 patients were included. The system accuracy was 98.30%. Evaluation results indicated an error rate of 1.69% by traditional methods. Documentation quality (completeness) increased from 78.66% to 100%. The average score of system quality was 4.60 indicating an acceptable range. After the system intervention, the mean of the adherence to the guideline significantly increased from 65% to 99.5% (P < 0.0008). CONCLUSION: The designed system was accurate and can improve adherence to the guideline for the severity assessment and the determination of a therapeutic trend for acute stroke patients. It leads to physicians' empowerment, significantly reduces medical errors, and improves the documentation quality.

Telepsychiatry- Based Care for the Treatment Follow-Up of Iranian War Veterans with Post-Traumatic Stress Disorder: A Randomized Controlled Trial.

BACKGROUND: Physical limitations, distance, and time are major obstacles to access to mental health services for veterans and soldiers. This study was aimed at comparing the efficacy of telepsychiatry and face-to-face consultation as methods of treating post-traumatic stress disorder (PTSD). The comparison was based on treatment costs, access to health services, completion of therapy sessions, and patient satisfaction as variables. METHODS: This research was a double blinded clinical trial supported by Tabriz University of Medical Sciences and conducted in 2015 to 2016 in Tabriz, Iran. Totally, 60 patients were included in the study. Through simple randomization, the patients were divided into experimental and control groups, both of whom were treated through face-to-face consultations for the first 3 sessions. Six follow-up sessions were then held remotely with the experimental group and face to face with the control group. Data were collected using a self-designed and reliable questionnaire and entered in SPSS, version 16. Intergroup comparisons were performed using descriptive statistical measures. Finally, the results were tested using the t test method. RESULTS: A significant relationship was found between the use of information technology and increased patient satisfaction, completion of therapy sessions, and reduction in treatment costs; however, no significant difference was found between the groups in terms of reduction in waiting time and access to a psychiatrist. CONCLUSION: Telepsychiatry is an effective means of delivering mental health services to psychiatric outpatients living in remote areas with limited resources. The results provide preliminary support for the use of telepsychiatry in treating PTSD and improving access to care. Trial Registration Number: IRCT2016020826449N1.

National Minimum Data Set for Antimicrobial Resistance Management: Toward Global Surveillance System.

BACKGROUND: Success of infection treatment depends on the availability of accurate, reliable, and comprehensive data, information, and knowledge at the point of therapeutic decision-making. The identification of a national minimum data set will support the development and implementation of an effective surveillance system. The goal of this study was to develop a national antimicrobial resistance surveillance minimum data set. METHODS: In this cross-sectional and descriptive study, data were collected from selected pioneering countries and organizations which have national or international antimicrobial resistance surveillance systems. A minimum data set checklist was extracted and validated. The ultimate data elements of the minimum data set were determined by applying the Delphi technique. RESULTS: Through the Delphi technique, we obtained 80 data elements in 8 axes. The resistance data categories comprised basic, clinical, electronic reporting, infection control, microbiology, pharmacy, World Health Organization-derived, and expert-recommended data. Relevance coding was extracted based on the Iranian electronic health record coding system. CONCLUSION: This study provides a set of data elements and a schematic framework for the implementation of an antimicrobial resistance surveillance system. A uniform minimum data set was created based on key informants' opinions to cover essential needs in the early implementation of a global antimicrobial resistance surveillance system in Iran.

The design and evaluation of an antimicrobial resistance surveillance system for neonatal intensive care units in Iran.

INTRODUCTION: Neonatal intensive care units (NICUs) have complex patients in terms of their diagnoses and required treatments. Antimicrobial treatment is a common therapy for patients in NICUs. To solve problems pertaining to empirical therapy, antimicrobial stewardship programs have recently been introduced. Despite the success of these programs in terms of data collection, there is still inefficiency in terms of analyzing and reporting the data. Thus, to successfully implement these stewardship programs, the design of antimicrobial resistance (AMR) surveillance systems is recommended as a first step. As a result, this study aimed to design an AMR surveillance system for use in the NICUs in northwestern Iranian hospitals to cover these information gaps. METHODS: The recommended system is compatible with the World Health Organization (WHO) guidelines. The business intelligence (BI) requirements were extracted in an interview with a product owner (PO) using a valid and reliable checklist. Following this, an AMR surveillance system was designed and evaluated in relation to user experiences via a user experience questionnaire (UEQ). Finally, an association analysis was performed on the database, and the results were reported by identifying the important multidrug resistances in the database. RESULTS: A customized software development methodology was proposed. The three major modules of the AMR surveillance are the data registry, dashboard, and decision support modules. The data registry module was implemented based on a three-tier architecture, and the Clinical Decision Support System (CDSS) and dashboard modules were designed based on the BI requirements of the Scrum product owner (PO). The mean values of UEQ measures were in a good range. This measures showed the suitable usability of the AMR surveillance system. CONCLUSION: Applying efficient software development methodologies allows for the systems' compatibility with users' opinions and requirements. In addition, the construction of interdisciplinary communication models for research and software engineering allows for research and development concepts to be used in operational environments.

Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic.

OBJECTIVES: Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to determine the triage level. METHODS: A combination of the Rule-Based Reasoning (RBR) and Fuzzy Logic Classifier (FLC) approaches were used to predict the triage level of patients according to the triage specialist's opinions and Emergency Severity Index (ESI) guidelines. RBR was applied for modeling the first to fourth decision points of the ESI algorithm. The data relating to vital signs were used as input variables and modeled using fuzzy logic. Narrative knowledge was converted to If-Then rules using XML. The extracted rules were then used to create the rule-based engine and predict the triage levels. RESULTS: Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of the documentation also improved from 76.72% to 98.5%. CONCLUSIONS: Designed system was effective in determining the triage level of patients and it proved helpful for nurses as they made decisions, generated nursing diagnoses based on triage guidelines. The hybrid approach can reduce triage misdiagnosis in a highly accurate manner and improve the triage outcomes.

A systematic review and meta-analysis on the treatment of liver hydatid cyst: Comparing laparoscopic and open surgeries.

BACKGROUND AND STUDY AIMS: There is an academic debate regarding surgical interventions for liver hydatid cyst disease. The purpose of the current systematic review and meta-analysis study was to analyse the pros and cons of open surgery and laparoscopic techniques, considering the outcomes of liver hydatid cysts. METHODS: Descriptive Boolean queries were used to search PubMed and Scopus for articles published between January 2000 and December 2016 to evaluate the outcomes of liver hydatid cyst in terms of mortality, post-operative complications, cure rate and recurrences. The data related to the four outcomes of liver hydatid cyst were extracted, assessed and then used as their corresponding effect sizes in the meta-analysis process. RESULTS: Six studies totally consisting of 1028 patients [open surgery group=816 (+7 converted to lap) and laparoscopic group=212] were analysed. In this meta-analysis study, random effects models of outcomes (i.e. post-operative complications, mortalities, recurrences and cure rate) of the two procedures were OR=0.852, LL=0.469, UL=1.546, Z=-0.526, p=0.599 (for post-operative complications); OR=0.849, LL=0.141, UL=5.105, Z=-0.179, p=0.858 (for mortality); OR=0.903, LL=0.166, UL=4.906, Z=-0.119, p=0.906 (for recurrence); and OR=0.459, LL=0.129, UL=1.637, Z=-1.201, p=0.230 (for cure rate). Meta-analysis and illustrated forest plots showed that there are no superiorities between the two approaches. The results of heterogeneity tests of the above mentioned outcomes were Q=8.083, df=5, p=0.152, I2=38.142% for post-operative complications; Q=0.127, df=2, p=0.938, I2=0% for mortality; Q=4.984, df=2, p=0.083, I2=59.874% for recurrence; and Q=10.639, df=5, p=0.059, I2=53.001% for cure rate. The results of regression tests based on Egger's, smoothed variance based on Egger (SVE) and smoothed variance based on Thomson (SVT) showed that the p values are not significant, and there are neither significant statistical differences nor publication bias between the outcomes of the two treatment procedures. CONCLUSION: The results show no promising trends towards advantages of open versus laparoscopic surgeries in the treatment of liver hydatid cyst. However, informative measurement values for comparing these surgeries could be derived for complications, recurrence, mortality and cure rates. Furthermore, all three tests, namely Egger's, SVE and SVT regression models, were used to assess publication bias and showed no evidence for the existence of publication bias.