Journal of Health and Biomedical Informatics
Year:2017 | Volume:4 | Issue:1|Papers Count:8

Introduction: The arrival of diverse and disparate hospital information systems by different-brands, on various platforms in the field of electronic health, has led to problems of interoperability.Interoperability is defined as the ability for two (or more) systems or components to exchange information and to use the information that has been exchanged. This paper aims to provide the hospital information system interoperability model. Provided that it cover all aspects of the interoperability.Method: This study is an applied- descriptive. The study includes three phase, at first required indicators to achieve all aspects of interoperability in hospital information system will be identified.In the following, provide the hospital information system interoperability model. Finally, this model with using a scenario based on a software architecture analysis method will be evaluated.Results: In this study, in order to achieve complete interoperability in Hospital Information System has been used a HSB approach. The presented model has necessary indicators for achieving all levels of interoperability (Technical, syntactic, semantic and organizational). EHealth experts confirmed this model using with architecture trade-off analysis method (ATAM).Conclusion: Achieving interoperability in healthcare information systems due to the complexity, diversity and standards is very difficult. And this challenges include technical, syntactic, semantic organizational. Increasing degree of interoperability and compatibility between hospital information systems cause to facilitate cooperation system together and increase efficiency, clinical performance and management of this systems.

Introduction: Cardiovascular diseases are the leading cause of death worldwide. The world health Organization has estimated 12 million deaths per year worldwide, due to the cardiovascular diseases.The main aim of this study was to design a smart computer-aided system for the diagnosis of heart disease in patients.Methods: In this study descriptive and analytical study, data of 270 people with 13 features were used.The fuzzy system and support vector machine classifier were combined using facilities of MATLAB software and were simulated by a system of core i5 and windows7, as the operating system, to diagnose patients with heart disease.Results: Fuzzy technique and support vector machine algorithm that were used for the diagnosis of heart disease was efficient in rapid diagnosis and consequently increasing the patient chance for survival. Evaluation criteria in this system were rates of categorization and sensitivity and the system performance for these two indicators were respectively 85% and 85.8%.Conclusion: According to the results, the proposed system can diagnose patients with cardiovascular diseases with a relatively high accuracy.

Introduction: Epilepsy is a common chronic neurological disease. In epilepsy treatment process, both of appropriate treatment options and patient education should be considered. Nowadays, mobile technologies have been known as a proper platform for improvement of patients’ knowledge in chronic diseases. The aim of this study was to assess the perspectives of epileptic patients and physicians about the required educational content in developing an educational mobile application for epilepsy.Methods: In this cross-sectional study, the perspectives of 100 patients with epilepsy who were members of the Iranian Epilepsy Association and 15 physicians who were members or colleagues of this association were surveyed about the patients’ educational needs. The applied questionnaire included 19 questions in three areas (disease information, lifestyle and used medications). Data were analyzed by the use of descriptive statistics (mean and standard deviation).Results: A variety of items such as first aid, cause, symptoms, complications and treatment of epilepsy, the effect of exercise, sleep, driving, occupation, marriage, pregnancy, nutrition in epilepsy, information about antiepileptic drugs and their complications, and the importance of the regular use of medications should be considered in the educational content of the application.Conclusion: This study indicates that the educational content for developing a mobile application for patients with epilepsy from the perspectives of patients and physicians includes three domains of life style, disease information and medication. Totally, 15 educational requirements were identified that should be considered in developing the application.

Introduction: Problems in thyroid gland are more common than in other glands of human body, and if they are not diagnosed early, thyroid storm or myxedema coma is likely to happen that might lead to death; therefore, on-time diagnosis of thyroid disorders (Hypothyroidism or hyperthyroidism) based on Laboratory and clinical tests is necessary. The main object of this research was to present a model based on data mining techniques that is capable of predicting thyroid diseases.Methods: This study was a descriptive-analytic study and its database included 7200 independent records based on 21 risk factors derived from UCI data reference. From all records, 70% were used for training and 30% for testing. First, neural networks performance was reviewed in order to diagnose thyroid diseases, and then an algorithm for combination of neural networks through hierarchical method was presented.Results: After modeling and comparing the generated models and recording the results, accuracies of predicting thyroid disorders using neural network and hierarchical method were found to be 96.6% and 100% respectively.Conclusion: Reducing misdiagnosis of thyroid diseases has always been one of the most important aims of researchers. Using methods based on data mining can decrease these errors. This study showed that using combination of neural networks through hierarchical method improves diagnosis accuracy.

Introduction: Sharp rise in health care costs associated with the reduction of hospitals' support by government has increased the importance of insurance organizations as the main source of hospitals' income. An important part of hospitals’ demands is related to the medical records of Iranian Health Insurance organization that are subject to deductions and are deduced from the hospitals’ demands after investigating the cause of defect in the process.Methods: The present study was conducted as a retrospective descriptive research. In this study, sampling was not conducted and all incomplete files of patients covered by Iranian Health Insurance Organization in the reference hospital (n=30) were entered into the study.Data of checklists were entered into Excel software version 22 and the causes were determined by using Fishbone diagram. Six-sigma scale was used to analyze the quality of the process.Results: Process errors were detected in six groups and the highest error rates were respectively related to pharmaceutical orders control stages, entering them in cards and requesting drug through drug delivering system. The quality of the reviewed process was evaluated as moderate level based on the calculated sigma. The highest rate of error was related to the records of patients covered by rural fund of non-global services.Conclusion: Measures such as staff training, monitoring and updating the record keeping system and improving external parts coordination can greatly reduce errors in three above-mentioned stages.

Introduction: Non-melanoma skin cancer (NMSC) has recently been one of the three most common cancers in Iran. Inappropriate management of the disease has led to an increase in the prevalence and overhead costs. Data mining techniques are helpful in the analysis of patient records and accurate management of diseases. This study aimed to find hidden patterns and relationships in the data of NMSC patients using data mining algorithms.Methods: In this applied study, study population consisted of medical records of 828 NMSC patients referred to the Cancer Institute of Imam Khomeini Hospital in Tehran during 2006-2015.Demographic variables and NMSC risk factors were clustered using K-Means algorithm. Apriori algorithm was applied as well for extraction of association rules and determination of patient’s common information with a confidence of ³ 0.9.Results: According to the studied variables, NMSC patients were classified in four clusters and three important factors influencing the disease were identified as abnormal BMI, high risk occupations and previous history of cancer. Seven rules were approved by association rules and the highest associations were found between the past history of the disease, the involved site, the relapse, and the type of NMSC.Conclusion: For the first time, this study could highlight the most important factors affecting NMSC using data mining methods. These factors should be considered either in self examination or screening skin tests in high-risk groups. In future studies, the contribution of physiological, ecological and genetic factors in the development of skin cancer should be jointly investigated as well.

Introduction: Nowadays, in this industrial modern world, the incidence of chronic diseases has been significantly increased. Gestational diabetes mellitus is one of the major health problems that if not treated, it will cause serious complications for mother and her child. The purpose of this research was to find ways for determining the risk of gestational diabetes mellitus and making early diagnosis to prevent it in the initial stages of pregnancy.Methods: This applied-survey research used two approaches of neural network and decision tree in experimental analysis of data and prediction. The extracted data were normalized and analyzed through Matlab software.Results: The results showed that data-based method is effective in improving the accuracy of prediction and has good performance in discovering implied knowledge and diagnosis of hidden relationships among data. In both methods, decision errors were acceptable and very close to each other.Conclusion: Based on the obtained results, data mining methods can be used in health centers for less familiar diseases in order to achieve on-time diagnosis, patient management and to decrease treatment costs.