Developing fracture liaison service framework based on comparative analysis and scoping review.

This research conducts a comparative analysis and scoping review of 105 studies in the field of Fracture Liaison Service (FLS). The resulting two-dimensional framework represents a significant step toward FLS implementation. PURPOSE: The primary goal is to review interventions in real world settings in order to provide the FLS framework that specifies the essential elements of its implementation and offers different perspectives on that. METHOD: This study encompasses two phases: a comparative analysis of existing FLS models, including "Capture the Fracture," "5IQ," and "Ganda," and a scoping review from 2012 to 2022 in PubMed, Web of Science, Scopus, ProQuest, and IEEE databases limited to publications in English. RESULTS: The resulting model of comparative analysis identifies patient identification, investigation, intervention and integration or continuity of care as the four main stages of FLS. Additionally, the elements of quality and information span across all stages. Following comparative analysis, the framework is designed to be used for content analysis of the included studies in the scoping review. The intersection of columns (Who, Where, When, What, How, Quality) with rows (Identification, Investigation, Intervention, and continuity of care) yields a set of questions, answered in tabular form based on the scoping review. CONCLUSION: The framework offers potential benefits in facilitating the adoption of effective approaches for FLS implementation. It is recommended to undertake an in-depth review of each of these components in order to uncover novel and innovative approaches for improving their implementation.

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE-Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals.

Objectives: Cardiac arrhythmia is one of the most severe cardiovascular diseases that can be fatal. Therefore, its early detection is critical. However, detecting types of arrhythmia by physicians based on visual identification is time-consuming and subjective. Deep learning can develop effective approaches to classify arrhythmias accurately and quickly. This study proposed a deep learning approach developed based on a Chapman-Shaoxing electrocardiogram (ECG) dataset signal to detect seven types of arrhythmias. Method: Our DNN model is a hybrid CNN-BILSTM-BiGRU algorithm assisted by a multi-head self-attention mechanism regarding the challenging problem of classifying various arrhythmias of ECG signals. Additionally, the synthetic minority oversampling technique (SMOTE)-Tomek technique was utilized to address the data imbalance problem to detect and classify cardiac arrhythmias. Result: The proposed model, trained with a single lead, was tested using a dataset containing 10,466 participants. The performance of the algorithm was evaluated using a random split validation approach. The proposed algorithm achieved an accuracy of 98.57% by lead II and 98.34% by lead aVF for the classification of arrhythmias. Conclusion: We conducted an analysis of single-lead ECG signals to evaluate the effectiveness of our proposed hybrid model in diagnosing and classifying different types of arrhythmias. We trained separate classification models using each individual signal lead. Additionally, we implemented the SMOTE-Tomek technique along with cross-entropy loss as a cost function to address the class imbalance problem. Furthermore, we utilized a multi-headed self-attention mechanism to adjust the network structure and classify the seven arrhythmia classes. Our model achieved high accuracy and demonstrated good generalization ability in detecting ECG arrhythmias. However, further testing of the model with diverse datasets is crucial to validate its performance.

Effectiveness of serious games in social skills training to autistic individuals: A systematic review.

Autism Spectrum Disorder (ASD) is a complex neurological condition that significantly impacts individuals' daily lives and social interactions due to challenges in verbal and non-verbal communication. Game-based tools for psychological support and patient education are rapidly gaining traction. Among these tools, teaching social skills via serious games has emerged as a particularly promising educational strategy for addressing specific characteristics associated with autism. Unlike traditional games, serious games are designed with a dual purpose: to entertain and to fulfill a specific educational or therapeutic goal. This systematic review aims to identify and categorize serious computer games that have been used to teach social skills to autistic individuals and to assess their effectiveness. We conducted a comprehensive search across seven databases, resulting in the identification and analysis of 25 games within 26 studies. Out of the 104 criteria assessed across these studies, 57 demonstrated significant improvement in participants. Furthermore, 22 of these studies reported significant enhancements in at least one measured criterion, with 13 studies observing significant improvements in all assessed outcomes. These findings overwhelmingly support the positive impact of computer-based serious game interventions in teaching social skills to autistic individuals.

A Machine Learning Model for Detection of Coronary Artery Disease Using Noninvasive Clinical Parameters.

Background and Objective: Coronary artery disease (CAD) is one of the most prevalent causes of death worldwide. The early diagnosis and timely medical care of cardiovascular patients can greatly prevent death and reduce the cost of treatments associated with CAD. In this study, we attempt to prepare a new model for early CAD diagnosis. The proposed model can diagnose CAD based on clinical data and without the use of an invasive procedure. Methods: In this paper, machine-learning (ML) techniques were used for the early detection of CAD, which were applied to a CAD dataset known as Z-Alizadeh Sani. Since this dataset has 54 features, the Pearson correlation feature selection method was conducted to identify the most effective features. Then, six machine learning techniques including decision tree, deep learning, logistic regression, random forest, support vector machine (SVM), and Xgboost were employed based on a semi-random-partitioning framework. Result: Applying Pearson feature selection to the dataset demonstrated that only eight features were the most effective for CAD diagnosis. The results of running the six machine-learning models on the selected features showed that logistic regression and SVM had the same performance with 95.45% accuracy, 95.91% sensitivity, 91.66% specificity, and a 96.90% F1 score. In addition, the ROC curve indicates a similar result regarding the AUC (0.98). Conclusions: Prediction is an important component of medical decision support systems. The results of the present study showed that feature selection has a high impact on machine-learning performance and, regardless of the evaluation metrics of the machine-learning models, determining the effective features is very important. However, SVM and Logistic Regression were designated as the best models according to our selected features.

Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches.

BACKGROUND: A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared with artificial intelligence predictive models (Artificial Neural Network and Decision Tree) in terms of the prediction of the survival rate of the patients admitted to the intensive care unit. METHODS: This retrospective cohort study was performed on the data of the patients admitted to the ICU of Ghaemshahr's Razi Teaching Care Center from March 20th, 2017, to September 22nd, 2019. The required data for calculating conventional severity classification models (SOFA, SAPS II, APACHE II, and APACHE IV) were collected from the patients' medical records. Subsequently, the score of each model was calculated. Artificial intelligence predictive models (Artificial Neural Network and Decision Tree) were developed in the next step. Lastly, the performance of each model in predicting the survival of the patients admitted to the intensive care unit was evaluated using the criteria of sensitivity, specificity, accuracy, F-measure, and area under the ROC curve. Also, each model was validated externally. The R program, version 4.1, was used to create the artificial intelligence models, and SPSS Statistics Software, version 21, was utilized to perform statistical analysis. RESULTS: The area under the ROC curve of SOFA, SAPS II, APACHE II, APACHE IV, multilayer perceptron artificial neural network, and CART decision tree were 76.0, 77.1, 80.3, 78.5, 84.1, and 80.0, respectively. CONCLUSION: The results showed that although the APACHE II model had better results than other conventional models in predicting the survival rate of the patients admitted to the intensive care unit, the other conventional models provided acceptable results too. Moreover, the findings showed that the artificial neural network model had the best performance among all the studied models, indicating the discrimination power of this model in predicting patient survival compared to the other models.

Developing a Multifaceted Evaluation Tool for Electronic Prescribing System: A Study from a Developing Country.

Evaluation of electronic prescribing systems (EPS) can contribute to their quality assurance, and motivate users and policy-makers to implement these systems, directly influencing the health of society. An appropriate evaluation tool plays a determining role in the identification of proper EPS. The present study aimed to develop a multifaceted evaluation tool for assessing the EPS. This study was conducted in two main steps in 2018. In the first step, we conducted a literature review to find the main features and capabilities of the prosperous EPS. In the second step, a Delphi method was used for determining the final criteria for evaluating EPS. After preparing a primary questionnaire based on the first step results, 27 expert stakeholders from related fields participated in this 3-phase Delphi study. The narrative content analysis and descriptive statistics were used for data analysis. The final evaluation tool consists of 61 questions in 10 main dimensions, including practical capabilities of the process/user and patient safety, data storage and transfer, prescription control and renewal, technical functions, user interfaces, security and privacy, reporting, portability, hardware and infrastructure, and system failure/recovery. The evaluation tool developed in this study can be used for the critical appraisal of features of EPS. It is recommended that this multifaceted evaluation tool be employed to help buyers compare different systems and assist EPS software vendors in prioritizing their activities regarding the system development. By using this tool, healthcare organizations can also choose a system that improves many aspects of health care.

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches.

BACKGROUND: Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions. OBJECTIVE: The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome. METHODS: To predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. In doing so, PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The checklist "Quality assessment of machine learning studies" was used to assess the quality of eligible studies. The findings of the studies are presented in the form of a narrative synthesis of evidence. RESULTS: In total, among 2,558 retrieved articles, 22 studies were qualified for analysis. Major adverse cardiovascular events and mortality were predicted in 5 and 17 studies, respectively. According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N = 20) achieved a high area under the ROC curve between 0.8 and 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice. CONCLUSION: Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.

Bone Healing Monitoring in Bone Lengthening Using Bioimpedance.

The most common technique of orthopedic surgical procedure for the correction of deformities is bone lengthening by "distraction osteogenesis," which requires periodic and ongoing bone assessment following surgery. Bone impedance is a noninvasive, quantitative method of assessing bone fracture healing. The purpose of this study was to monitor bone healing and determine when fixation devices should be removed. The left tibia of eight male New Zealand white rabbits (2.4 ± 0.4 kg) undergoing osteotomy was attached with a mini-external fixator. The bone length was increased by 1 cm one week after surgery by distracting it 1 mm per day. Before and after osteotomy, as well as every week after, bone impedance was measured in seven frequency ranges using an EVAL-AD5933EBZ board. Three orthopedic surgeons analyzed the radiographs using the Radiographic Union Scale for Tibial (RUST) score. The Kappa Fleiss coefficient was used to determine surgeon agreement, and the Spearman rank correlation coefficient was used to find out the relationship between impedance measurements and RUST scores. Finally, the device removal time was calculated by comparing the bone impedance to the preosteotomy impedance. The agreement of three orthopedic surgeons on radiographs had a Fleiss' Kappa coefficient of 49%, indicating a moderate level of agreement. The Spearman rank correlation coefficient was 0.43, indicating that impedance and radiographic techniques have a direct relationship. Impedance is expected to be used to monitor fractured or lengthened bones in a noninvasive, low-cost, portable, and straightforward manner. Furthermore, when used in conjunction with other qualitative methods such as radiography, impedance can be useful in determining the precise time of device removal.

Development and evaluation of a teleoncology system for breast cancer during the COVID-19 pandemic.

Background: The present study describes the steps of developing a hybrid teleoncology system to provide treatment plans for breast cancer patients. Materials & methods: This research was conducted in four stages, including developing a proposal for experts, identifying and analyzing system requirements, designing a prototype and implementing and evaluating the final version of the hybrid teleoncology system. Results: The results of the usability evaluation showed that the users evaluated the system at a good level and, in practice, the implemented system was perceived to be useful by specialists in providing treatment plans for cancer patients. Conclusion: The hybrid teleoncology system is a practical alternative to traditional methods for providing treatment plans to breast cancer patients.

Asthma Management System in Primary Care Based on Global Initiative for Asthma and Snell's Drug Interaction: Accuracy and Usability.

Background: The two main pillars of asthma management include regular follow-up and using guidelines in the treatment process. Patient portals enable regular follow-up of disease, and guideline-based decision-support-systems can improve the use of guidelines in the treatment process. Based on the Global Initiative for Asthma (GINA) and Snell's drug interaction, asthma management system in primary care (AMSPC) includes the capabilities of both mentioned systems. This system was developed to improve regular follow-up and use GINA in the asthma management process. This study aimed to assess the accuracy and usability of the AMSPC based on the GINA and Snell's drug interaction. Materials and Methods: To assess the accuracy of the system, kappa test was used to calculate the degree of agreement between the suggestions made by the system and the physician's decision for a total of 64 patients selected through convenience sampling method. To assess usability, the Questionnaire for User Interface Satisfaction (QUIS) was used. Results: The scores of the Kappa for the agreements between the system and the physician in determining "drug type and dosage", "follow-up time", and "drug interactions" were 0.90, 0.94, and 0.94, respectively. The average score of the QUIS was 8.6 out of 9. Conclusion: Due to the high accuracy of the system in computerizing the GINA and Snell's drug interaction, as well as its proper usability, it is expected that the system be widely used to improve asthma management and reduce drug interactions.

Tele-orthopaedics: A systematic mapping study.

INTRODUCTION: The use of telemedicine in orthopaedics can provide high-quality orthopaedic services to patients in remote areas. Tele-orthopaedics is widely acknowledged for decreasing travel, time and cost, increasing accessibility and quality of care. In the absence of a comprehensive review on tele-orthopaedics applications and services, here, we systematically identify and classify the tele-orthopaedic applications and services, and provide an overview of the trends in the field. METHODS: In this study, a systematic mapping was conducted to answer six research questions, we searched the databases Scopus, PubMed, IEEE Digital Library and Web of Science up to 2019. Consequently, 77 papers were screened and selected on the basis of specific inclusion and exclusion criteria. RESULTS: We found that mobile-based teleconsultation was mostly asynchronous, while non-mobile teleconsultation was synchronous. The results showed that the physician-patient relationship was more common than other interactions, such as physician-physician and physician-robot interactions. In addition, more than half of the services provided by tele-orthopaedics have been used for orthopaedic diseases/traumas in which joint replacement and fracture reduction have been the most important orthopaedic procedures. It has been noted that more attention has been paid to tele-orthopaedics in developed countries such as the USA, Australia, Canada and Finland. DISCUSSION: Telemonitoring (teleconsultation and telemetry) and telesurgery (telerobotics and telementoring) were found to be the two major forms of tele-orthopaedics. Mobile phones were used asynchronously in most of the teleconsultations. The development of different applications may result in the use of multiple smartphones applications in real-time teleconsultation. The use of smartphones is expected to increase in the near future.

Retraction: Using the Modified Delphi Method to Propose and Validate Components of a Child Injury Surveillance System for Iran.

[This retracts the article on p. 1k in vol. 18.].

The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis.

BACKGROUND: Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes. METHODS: This systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95% confidence interval. Q statistics and I2 were then used to calculate heterogeneity. RESULTS: On the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95% CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs. CONCLUSIONS: Our results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

Using the Modified Delphi Method to Propose and Validate Components of a Child Injury Surveillance System for Iran.

Background: Child injuries are a worldwide public health concern. An injury surveillance system (ISS) has a beneficial impact on child injury prevention, but an evidence-based consensus on frameworks is necessary to establish a child ISS. Objectives: To investigate key components of a child ISS and to propose a framework for implementation. Methods: Data were gathered through interview with experts using unstructured questions to identify child ISS functional components. Qualitative data was analyzed using content analysis method. Then, the Modified Delphi method was used to validate functional components. Based on the outcomes of the content analysis, a questionnaire with closed questions was developed to be presented to a group of experts. Consensus was achieved in two rounds. Discussion: In round I, 117 items reached consensus. In round II, five items reached consensus and were incorporated into the final framework. Consensus was reached for 122 items comprising the final framework and representing seven key components: goals of the system, data sources, data set, coalition of stakeholders, data collection, data analysis, and data distribution. Each component consisted of several sub-components and respective elements. Conclusion: This agreed framework will assist to standardize data collection, analysis, and distribution to detect child injury problem and provide evidence for preventive measures.

Using modified Delphi method to propose and validate the components of a child injury surveillance system for Iran.

PURPOSE: Child injuries are a public health concern globally. Injury surveillance systems (ISSs) have beneficial impact on child injury prevention. There is a need for evidence-based consensus on frameworks to establish child ISSs. This research aims to investigate the key components of a child ISS for Iran and to propose a framework for implementation. METHODS: Data were gathered through interview with experts using unstructured questions from January 2017 to December 2018 to identify child ISS functional components. Qualitative data were analyzed using content analysis method. Then, modified Delphi method was used to validate the functional components. Based on the outcomes of the content analysis, a questionnaire with closed questions was developed and presented to a group of experts. Consensus was achieved in two rounds. RESULTS: In round I, 117 items reached consensus. In round II, 5 items reached consensus and were incorporated into final framework. Consensus was reached for 122 items comprising the final framework and representing 7 key components: goals of the system, data sources, data set, coalition of stakeholders, data collection, data analysis and data distribution. Each component consisted of several sub-components and respective elements. CONCLUSION: This agreed framework will assist in standardizing data collection, analysis and distribution, which help to detect child injury problems and provide evidence for preventive measures.

Internet of things in medicine: A systematic mapping study.

CONTEXT: The current studies on IoT in healthcare have reviewed the uses of this technology in a combination of healthcare domains, including nursing, rehabilitation sciences, ambient assisted living (AAL), medicine, etc. However, no review study has scrutinized IoT advances exclusively in medicine irrespective of other healthcare domains. OBJECTIVES: The purpose of the current study was to identify and map the current IoT developments in medicine through providing graphical/tabular classifications on the current experimental and practical IoT information in medicine, the involved medical sub-fields, the locations of IoT use in medicine, and the bibliometric information about IoT research articles. METHODS: In this systematic mapping study, the studies published between 2000 and 2018 in major online scientific databases, including IEEE Xplore, Web of Science, Scopus, and PubMed were screened. A total of 3679 papers were found from which 89 papers were finally selected based on specific inclusion/exclusion criteria. RESULTS: While the majority of medical IoT studies were experimental and prototyping in nature, they generally reported that home was the most popular place for medical IoT applications. It was also found that neurology, cardiology, and psychiatry/psychology were the medical sub-fields receiving the most IoT attention. Bibliometric analysis showed that IEEE Internet of Things Journal has published the most influential IoT articles. India, China and the United States were found to be the most involved countries in medical IoT research. CONCLUSIONS: Although IoT has not yet been employed in some medical sub-fields, recent substantial surge in the number of medical IoT studies will most likely lead to the engagement of more medical sub-fields in the years to come. IoT literature also shows that the ambiguity of assigning a variety of terms to IoT, namely system, platform, device, tool, etc., and the interchangeable uses of these terms require a taxonomy study to investigate the precise definition of these terms. Other areas of research have also been mentioned at the end of this article.

Evaluating the factors that influence cloud technology adoption-comparative case analysis of health and non-health sectors: A systematic review.

Cloud technology has brought great benefits to the health industry, including enabling improvement in the quality of services. The objective of this review study is to investigate the reported factors affecting the adoption of cloud in the health sector by comparing studies in the health and non-health sectors. This article is a systematized review of studies conducted in 2018. From 541 articles, 47 final articles were selected and classified into two categories: health and non-health studies; conclusions were drawn from the two sectors by comparing their effective factors. Based on the results of this review, the factors were categorized as technological, organizational, environmental, and individual. The results of this review study could be a beneficial guide to the health empirical research on cloud adoption. Individual domains have not been examined in health sector studies. Since the process of adoption of new technologies in organizations is time-consuming, due to the lack of managerial knowledge about the efficient factors, recognition of these factors by decision-makers while planning for cloud adoption becomes of great importance. The findings of this review study aim to help health decision-makers by increasing their awareness of the cloud and of the factors that impact decisions at both the organizational and individual levels.

Barriers and facilitators of implementing child injury surveillance system.

PURPOSE: Child injuries are a global public health problem and injury surveillance systems (ISS) can be beneficial by providing timely data. However, ISS implementation has challenges. Opinions of stakeholders of ISS implementation barriers and facilitators are a good source to understand this phenomenon. The aim of this study is to investigate barriers and facilitators of implementing ISS in Iran. METHODS: This is a qualitative study. Data were gathered through interviews with 14 experts in the field of child injury and prevention from Iranian Ministry of Health and Medical Education (MOHME), medical universities, pediatrics hospitals, general hospitals and health houses during January 2017 to September 2017. Data collection and analysis continued until data saturation. Data were analyzed using content analysis through identifying meaning units. RESULTS: Barriers were classified in three main categories and nine subcategories including management barriers (including performance, coordination and cooperation, supervision and attitude), weakness in data capture and usage (including data collection, data recording and data dissemination) and resource limitation (including human and financial resources). Facilitators identified in three areas of policy making (including empowerment and attitude), management (including organization, function and cooperation and coordination) and data recording and usage (including data collection/distribution and data recording). CONCLUSION: The most important barrier is lack of national policy in child injury prevention. The most important facilitator is improving MOHME function through passing supportive regulations. Effective data usage and dissemination of information to those requiring data for policy making can help reduce child injuries. Coalition of stakeholders helps overcome existing barriers.

Hospital Real-Time Location System (A Practical Approach in Healthcare): A Narrative Review Article.

BACKGROUND: The Hospital Real-time Location Systems (HRTLS), deal with monitoring the patients, medical staff and valuable medical equipment in emergency situations. Therefore, the study aimed to propose Hospital Real-Time Location Systems based on the novel technologies in Iran. METHODS: In this narrative-review, the articles and official reports on HRTLS, were gathered and analyzed from related textbooks and indexing sites with the defined keywords in English or Persian. The search of databases such as IDTechEx, IEEE, PubMed Central, Science Direct, EMBASE/Excerpta Medica, Scopus, Web of Science, Elsevier journals, WHO publications and Google Scholar was performed to reconfirm the efficiency of HRTLS from 2006 to 2017. RESULTS: Various technologies have been used in the current systems, which have led to the reduced error rate, costs and increased speed of providing the healthcare services. Applications of these systems include tracking of patient's, medical staff and valuable medical assets. Besides, achieving the patient & staff satisfaction is among other basic applications of these Systems. The accurate data exchange and processes control are considered as positive aspects of this technology. CONCLUSION: HRTLS has great importance in healthcare systems and its efficiency in medical centers is reliable; hence, it seems necessary to determine the organization's requirements, apply novel technologies such as cloud computing and Internet of things, and integrate them to get access to maximum advantages in Iranian healthcare centers.

Security Requirements of Internet of Things-Based Healthcare System: a Survey Study.

INTRODUCTION: Internet of Things (IoT), which provides smart services and remote monitoring across healthcare systems according to a set of interconnected networks and devices, is a revolutionary technology in this domain. Due to its nature to sensitive and confidential information of patients, ensuring security is a critical issue in the development of IoT-based healthcare system. AIM: Our purpose was to identify the features and concepts associated with security requirements of IoT in healthcare system. METHODS: A survey study on security requirements of IoT in healthcare system was conducted. Four digital databases (Web of Science, Scopus, PubMed and IEEE) were searched from 2005 to September 2019. Moreover, we followed international standards and accredited guidelines containing security requirements in cyber space. RESULTS: We identified two main groups of security requirements including cyber security and cyber resiliency. Cyber security requirements are divided into two parts: CIA Triad (three features) and non-CIA (seven features). Six major features for cyber resiliency requirements including reliability, safety, maintainability, survivability, performability and information security (cover CIA triad such as availability, confidentiality and integrity) were identified. CONCLUSION: Both conventional (cyber security) and novel (cyber resiliency) requirements should be taken into consideration in order to achieve the trustworthiness level in IoT-based healthcare system.