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Background: Neonatal resuscitation is a critical skill that requires regular training and practice; however, traditional training methods are often limited by accessibility, cost and engagement. Serious games (SGs) have emerged as a promising educational tool to address these challenges. This systematic review assesses the effectiveness of SGs in neonatal resuscitation education. Objectives: This systematic review assesses the effectiveness of SGs in neonatal resuscitation education. Methods: This systematic review followed the preferred reporting items for systematic reviews and meta-analysis guidelines and utilized a comprehensive search strategy across multiple databases (PubMed, Scopus, Web of Science, Embase, Cochrane and ProQuest) using keywords and MeSH terms related to neonatal resuscitation and SGs. Studies published in English up to July 25, 2024, focusing on SGs for neonatal resuscitation training were included. Two independent reviewers screened titles, abstracts, and full texts, resolving disagreements through discussion. Results: SGs were as effective as video lectures in improving neonatal resuscitation performance. Also, remote digital SGs provided interactive and enjoyable learning experiences for anesthesiologists and pediatric trainees. Paramedics and registered nurses significantly improved neonatal resuscitation performance and knowledge using SGs. Nursing students exhibited increased knowledge, problem-solving abilities and self-confidence with virtual reality-based SGs. Conclusions: SGs demonstrate promise as a complementary tool for neonatal resuscitation education. They can enhance knowledge acquisition, and skills development, and potentially improve motivation and engagement. However, further research is needed to optimize game design, investigate long-term learning outcomes, and assess skills transfer to clinical practice. Integrating SGs with traditional simulation-based training methods could offer a comprehensive and effective approach to training healthcare professionals in this critical skill set.

Healthcare organizations must urgently prioritize interoperability to enhance the quality of care they provide. However, achieving this collaboration comes with numerous challenges, including differing approaches, data formats, and standards, as well as concerns about privacy, security, technical complexity, and legal and regulatory issues. To tackle these challenges, we determined a set of interoperability solutions. We also developed a comprehensive, component-based, data-driven framework for healthcare systems. Our study's approach involved three main steps: first, conducting a literature review to gather interoperability requirements and solutions from online databases and grey literature; second, carrying out a qualitative study to develop a framework based on the review results and focus group discussions; and third, using the Delphi method to validate the framework with experts. We extracted information from 36 articles during the screening and assessment process. Based on the proposed framework, we organized the identified themes into various categories, including architecture, architecture components, standards, platforms, policies, data sources, consumers, applications, level of interoperability, healthcare facilities, and considerations. Experts believe that establishing a comprehensive architecture for launching interoperability between health information systems can greatly facilitate this process. All framework components (totaling 197) received unanimous approval. The landscape of healthcare delivery is shifting from a focus on diseases to a patient-centered, data-driven approach. There is a growing demand for personalized healthcare systems, which necessitates increased interoperability among all healthcare stakeholders, particularly when dealing with diverse types of data. Our framework is designed to facilitate the implementation of various types of interoperability in healthcare systems.

Abstract Background Forensic medicine is crucial in ensuring that the law and justice are carried out as swiftly, effectively, clearly, and accurately as possible. The significant number and interactions of forensic clinical examination variables, the complexity of their differentiation, and the existence of multiple decision-making paths can lead to erroneous decisions that cause irreparable harm to individuals and society. This study aimed to develop and evaluate a decision support system for determining the amount of wergild and compensation based on forensic medicine clinical examinations and the severity of the patient’s injury. Methods A total of 264 data elements and decision-making rules were identified based on an analysis of information sources and focus group discussions. In addition, a decision tree was used to organize the decision-making rules. Then, a system was developed using algorithms for intelligent decision-making. We included 500 patients in our analysis. This system was ultimately evaluated based on the following criteria: precision and accuracy, sensitivity, specificity, usability, and documentation quality. Results The results indicated that the precision and accuracy, sensitivity, and specificity of the system were 100%. Furthermore, the documentation quality (completeness) increased from 78.2 to 100%. The average score for system usability was 4.35 out of 5, indicating a highly acceptable range. Conclusion The designed system was effective and beneficial for forensic clinical examinations and quantifying physical damage (wergild and compensation). Therefore, this system can be utilized in forensic medicine’s administrative and clinical processes, and its production and commercialization will result in an adequate market penetration rate. In addition, this will empower forensic medicine physicians and reduce decision-making errors. Graphical Abstract

Abstract 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.

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.

Abstract 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

Abstract 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.

Abstract 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.

Abstract 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.

In estimating the risk of heart disease due to non-deterministic risk factors, often diagnosis of angiographic disease is difficult for physicians. For modeling and application of this uncertain and imprecise modality, decision support systems based on fuzzy logic are appropriate and an effective approach. Smartphone-based applications can facilitate the efficient use of these systems in form of evidence-based medicine. The aim of this applied study was developing and evaluating an application to diagnose angiographic disease conditions and the severity of heart attacks through a smartphone-based clinical decision support system. Android application development environment was utilized after extraction of linguistic rules and definition of membership functions needed for decisions. A smartphone-based application was designed using these guidelines and utilizing fuzzy modeling. Then, the app was evaluated in terms of accuracy using the popular Cleveland dataset. According to the results, 10 fuzzy rules for modeling, seven input variables and one decision variable were extracted. Programming was done in Eclipse. The evaluation results indicate that the accuracy of the program was 94%. Decision support systems within the context of mobile health have become some of the most efficient and influential information technology tools in recent years. They can improve patient-care management, provide easier access to the information needed for healthcare decisions and ultimately reduce healthcare costs while improving its quality.

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.

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

Background and Objectives: The use of e-learning in medical education is one of the key issues in the development of information technology; and it would be as a challenge in the future. The purpose of this study was to investigate the attitudes of two groups of faculty members who use e-learning and those who have not experienced the use of e-learning. Finally, the challenges of using e-learning from the perspective of professors are examined. Material and Methods : In this cross-sectional study which was carried out in 1396 in Tabriz University of Medical Sciences, 86 of the professors were entered into the study using the stratified sampling method. The data gathering tool was a researcher-made questionnaire in three sections, including demographic information, attitude and challenges. After confirming the validity by experts, reliability of the questionnaire was determined 0.76 using Cronbach's alpha. Descriptive and inferential statistics methods and SPSS version 16 were used to analyze the obtained data. Results: Professors who had an experience of implementation of e-learning, 69.5% had a positive attitude toward using it and 16.9% had negative attitude. Also, there was a significant relationship between the attitude of the professors using e-learning and their scientific ranking. Regarding the professors with no experience in using e-learning, there was a significant relationship between their attitude, age and teaching history. Conclusion: By identifying the positive effects of e-learning such as helping learning, it may be possible to achieve more favorable outcomes in improving the attitude level of faculty members of the Tabriz University of Medical Sciences and universities with similar situation.

Background: When it comes to rehabilitation following many injuries and disabilities, exercise therapy is a long, arduous, and tedious process. Therefore, there is a need to employ new methods to increase the frequency, duration, and intensity of the exercises, and to boost motivation and satisfaction of the patients in a way that they can better perform the exercises. In this regard, virtual reality (VR) is an emerging technology that can be an effective tool in mitigating therapeutic challenges. Objectives: The main objective of the current study was to evaluate the effectiveness of VR-based exercise therapy to highlight areas for future studies in rehabilitation. Methods: The scoping review methodology was used to comprehensively search for and identify related papers in MEDLINE (PubMed), Cochrane Database of Systematic Reviews, EMBASE, IEEE, and Web of Science, while grey literature was also searched. Studies that used VR for exercise therapy were included in the current review. Quality assessment was performed using the Physiotherapy Evidence Database. The consensus was reached following two reviewers’ independent inclusion screening, data extraction, and appraisal. Results: Among 2887 identified studies, 26 papers were eligible to be included in this review. The results showed the positive effects of VR-based exercise therapy in a variety of conditions or disorders. With regard to treatment objectives, VR-based exercise therapy has been more commonly considered for the improvement of pain (41%), functional ability (31%), and muscular strength (24%). According to the findings, compared to other VR devices, Nintendo Wii and Kinect are 41% and 24% more common, respectively. Conclusions: This review provides evidence for the potential effectiveness of virtual reality-based exercise therapy for the improvement of rehabilitation outcomes. However, further higher-quality research is needed to confirm the observed positive effects.

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.

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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.

Introduction: In order to design and develop an efficient electronic health record (EHR) in Iran, the assimilation of health information systems based on a common language for the interpretation and sharing of information with HER is essential. The current research provides an object-oriented design for gynecology data management based on unified modeling language (UML). By applying this model, it is possible to improve interoperability capabilities of Iranian EHR. Methods: This applied, developmental, and cross-sectional study was carried out in 2015. First, published minimum data sets on gynecology and prenatal care in Iran were evaluated. Then, experts assessed and verified the most comprehensive minimum data set for Iran. In the next step, an object-oriented model was designed based on specific data patterns which were extracted from international standards matched with Iranian EHR to satisfy the interoperability requirements of healthcare information. Finally, the object-oriented design was created by applying UML in visual paradigm software package and the prototype of the system was developed. Results: The designed system was directly used by 3 people as main users (clinical secretor, physician, and system administrator). Moreover, this system can interact with the Iranian EHR as an external actor. Use case, activity, sequence, and class diagrams were drawn. Data elements were coded based on national coding standards. Conclusion: Coordination of standardization of different components using software analysis and design is necessary for the successful implementation and pervasive acceptance of EHRs. Object-oriented analysis and design provide a suitable tool for the modeling complexity in software systems. Applying content and technical standards in the design and implementation of EHRs render it useful and applicable in terms of information integration and interoperability.

Introduction: Coronary artery disease (CAD) is one of the major causes of disability and death in the world. Accordingly utilizing from a national and update guideline in heart-related disease are essential. Finding interesting rules from CAD data and comparison with guidelines was the objectives of this study. Methods: In this study 1993 valid and completed records related to patients (from 2009 to 2014) who had suffered from CAD were recruited and analyzed. Total of 25 variable including a target variable (CAD) and 24 inputs or predictor variables were used for knowledge discovery. To perform comparison between extracted knowledge and well trusted guidelines, Canadian Cardiovascular Society (CCS) guideline and US National Institute of Health (NIH) guideline were selected. Results of valid datamining rules were compared with guidelines and then were ranked based on their importance. Results: The most significant factor influencing CAD was chest pain. Elderly males (age >54) have a high probability to be diagnosed with CAD. Diagnostic methods that are listed in guidelines were confirmed and ranked based on analyzing of local CAD patients data. Knowledge discovery revealed that blood test has more diagnostic value among other medical tests that were recommended in guidelines. Conclusion: Guidelines confirm the achieved results from data mining (DM) techniques and help to rank important risk factors based on national and local information. Evaluation of extracted rules determined new patterns for CAD patients.

Objective: To systematically review the current literature of simulation in healthcare including the structured steps in the emergency healthcare sector by proposing a framework for simulation in the emergency department.Methods: For the purpose of collecting the data, PubMed and ACM databases were used between the years 2003 and 2013. The inclusion criteria were to select English-written articles available in full text with the closest objectives from among a total of 54 articles retrieved from the databases. Subsequently, 11 articles were selected for further analysis.Results: The studies focused on the reduction of waiting time and patient stay, optimization of resources allocation, creation of crisis and maximum demand scenarios, identification of overcrowding bottlenecks, investigation of the impact of other systems on the existing system, and improvement of the system operations and functions. Subsequently, 15 simulation steps were derived from the relevant studies after an expert’s evaluation.Conclusion: The 10-steps approach proposed on the basis of the selected studies provides simulation and planning specialists with a structured method for both analyzing problems and choosing best-case scenarios. Moreover, following this framework systematically enables the development of design processes as well as software implementation of simulation problems.

Introduction: Specialized registry in the NICU (neonatal intensive care unit) is necessary to gather process and report data in an accurate and timely manner. An electronic registry could enable the mother and infant to be monitored and traced through complete and comprehensive data. Aim: Registry has been developed on the basis of a literature study and view point of participants about effectiveness and efficiency was evaluated. Materials and Methods: A situational analysis was performed to achieve a clear view from current status of NICU data collecting and reporting, then a customized three tier architecture was proposed to design and implement a specialized registry. User satisfaction was evaluated by system usability scale among 30 participants to assess user’s attitude about system. Results: A comprehensive data model with seven categories was defined by 65 entities, which actually retains part of the infants information. System usability scale was in a high range about 70%. The highest agreement is on reduced registration error. The advantages of system were perceived by users and they had positive attitude about it. Conclusion: The use of a suitable architecture and standard templates lead to enhanced acceptability, effectiveness, performance and functionality. Quality of data and health promotion has a direct relationship with system usability. These systems have a great potential to increase safety of patients by providing fast and easy access to information, monitoring and decision support operations.

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Determining the Requirements and Barriers for Using Barcode Technology in the Hospitals of Tabriz, Iran

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