E-health plays a crucial role in E-government by proposing healthcare services based on information technology. However, the way to administer these services by using E-health solutions is one of the challenging issues. One of these significant challenges is how one integrates heterogeneous healthcare information of the different point of care systems. This paper introduces the Iranian integrated care electronic health record using the information gathered from several point-of-care systems in healthcare enterprises in Iran. This service-oriented architecture has a remarkable characteristic – its accessibility to medical knowledge and medical concepts through archetypes and ontology, respectively. The Ministry of Health and Medical Education of the Islamic Republic of Iran has designed and implemented this national architecture.

Introduction:The aim of this study was to identify patients at-risk, enhancing self-care management of HF patients at home and reduce the disease exacerbations and readmissions.Method:In this research according to standard heart failure guidelines and Semi-structured interviews with 10 heart failure Specialists, a draft heart failure rule set for alerts and patient instructions was developed. Eventually, the clinical champion of the project vetted the rule set. Also we designed a transactional system to enhance monitoring and follow up of CHF patients. With this system, CHF patients are required to measure their physiological measurements (vital signs and body weight) every day and to submit their symptoms using the app. additionally, based on their data, they will receive customized notifications and motivation messages to classify risk of disease exacerbation. The architecture of system comprised of six major components: 1) a patient data collection suite including a mobile app and website; 2) Data Receiver; 3) Database; 4) a Specialists expert Panel; 5) Rule engine classifier; 6) Notifier engine.Results:This system has implemented in Iran for the first time and we are currently in the testing phase with 10 patients to evaluate the technical performance of our system. The developed expert system generates alerts and instructions based on the patient’s data and the notify engine notifies responsible nurses and physicians and sometimes patients. Detailed analysis of those results will be reported in a future report.Conclusion:This study is based on the design of a telemonitoring system for heart failure self-care that intents to overcome the gap that occurs when patients discharge from the hospital and tries to accurate requirement of readmission. A rule set for classifying and resulting automated alerts and patient instructions for heart failure telemonitoring was developed. It also facilitates daily communication among patients and heart failure clinicians so any deterioration in health could be identified immediately.

IntroductionIt is estimated that Iran accounted for about 1% of hip fracture burden of the world in 2007, but these data are based on incomplete evidence. As the country’s population is ageing, it is expected that a dramatic rise in hip fracture incidence will result. There is no single national study that accurately estimates the incidence of all hip fractures in the country or identifies the direct costs for affected patients. To help fill this gap, the current study has been designed to determine the incidence of hip fracture associated with osteoporosis in the Iranian population and to assess the direct costs involved.Methods and analysisThis is a cross-sectional analysis of 2 years of hospital admissions due to hip fracture in Iran from October 2014 to October 2016 using an electronic health record called SEPAS. SEPAS is a nationwide health information system established by Information Technology (IT) and the Statistics Department of the Ministry of Health. SEPAS has recorded more than 8.5 million inpatient hospitalizations since October 2014. Our study will identify reported hip fracture data in SEPAS among admitted adult hospital patients aged ≥50 in Iran. International Classification of Diseases ICD-9 and 10 will be used as diagnostic codes. Study factors are demographic data, types of fracture, types of treatment, duration of admission, early complications, in-hospital mortality and direct cost of fracture treatment. The accuracy of the SEPAS fracture data will be ascertained through a pilot study that compares the SEPAS data with the data directly extracted from medical records of the Shariati Hospital in Tehran during the study period.Ethics and disseminationThe study protocol was approved by the Ethics Committee of the National Institute for Medical Research Development of Iran. Dissemination plans include academic publications, conference presentations and social media.

Today, the demand for health-oriented systems to facilitate and improve treatment processes is growing. For different information systems with different structures and technologies to be able to communicate with each other, a single gateway is required. The gateway acts as an interface between information systems and unifies protocols, rules, and standards related to communication processes. Health-related systems need a unique regulator that explains data models, coding, and data exchange structures. Moreover, the gateway has control over information systems and the data transmitted between them. In this paper, we explain an integrated gateway of health information exchange named DITAS which is a bridging point between health-related systems.

Background: The algorithmic classification of infected and healthy individuals by gene expression has been a topic of interest to researchers in numerous domains, including cancer. Several studies have presented numerous solutions, such as neural networks and support vector machines (SVMs), to classify a diverse range of cancer cases. Such classifications have provided some degrees of accuracy, which highly depend on optimization approaches and suitable kernels. Objectives: This study aimed at proposing a method to classify cancer-prone and healthy cases under breast cancer and colorectal cancer (CRC), using machine learning methods efficiently, increasing the accuracy of the classification process. Methods: This study presented an algorithm to diagnose individuals prone to breast cancer and CRC. The novelty of this algorithm lies in its suitable kernel and the feature extraction approach. By the application of this algorithm, this study first identified the genes closely associated with these types of cancers and, then, tried to find individuals susceptible to the concerned cancers using SVM. The present study highlighted the indirect gene expressions associated with these cancers, which might show health status complications for the patients. To this end, the algorithm consists of SVMs in conjunction with the k-fold method for validation. Results: The results confirmed the superior performance of this approach, compared to the common neural networks. The algorithm’s identification accuracy values were 98.077% and 99.806% for breast cancer and CRC, respectively. The graphic representation of the cause-effect relationships was also provided to help researchers better understand the trend of cancer or other types of diseases. Conclusions: The feature extraction method highly affects the accuracy of the classification. In addition, relying on indirect disease-triggering genes’ expressions highlights a cause-effect relationship between genes and diseases. Such relationships can form Markov models in the clinical domain leading to treatment paths and prediction of patient outcomes.

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Dental implant placement in patients with Guillain‐Barre syndrome could be accomplished, and it may turn into a successful treatment for edentulous sites and functionally stabilized for long life. However, a proper patient selection, accurate medical consultation with physician, atraumatic surgery, and other important cautions should be considered.