Frontiers in Health Informatics
Volume:7 Issue: 1, 2018

The perceived usefulness and perceived ease of use have been considered as the main factors affecting the acceptance of the new technologies since last few decades. However, it appears that these only two factors cannot describe the users’ behavior in the environments like the Hospital Information System. From the technology acceptance standpoint at the individual acceptance level, the present paper tends to develop a Technology Acceptance Model with introducing some external factors. This study was conducted in 2017. The research population was included 185 nurses who works in Health Information Management (HIM) departments of Tehran University of Medical Sciences. A questionnaire was developed in order to gather the required data. The validity was obtained by panel of experts and the reliability was examined and then confirmed in a 50 person sample using the Cronbach’s Alpha (α=0.93). The Likert’s five item scale was applied. The data were analyzed using descriptive statistics, exploratory factor analysis, and path analysis. The behavioral intention was affected significantly and positively by the factors of perceived usefulness, perceived ease of use, self-efficacy, end user support, social norm, trust, job relevance, and training, with trust having the highest level of effects. Also perceived ease of use had a significant effect on perceived usefulness along with an indirect effect on behavioral intention through perceived usefulness. The factors of anxiety, voluntariness, and facilitating conditions showed no significant effects on behavioral intention. The factors of trust, perceived usefulness, social norm, end user support, and self-efficacy have an impact on the behavioral intention of the users utilizing the Hospital Information System in the concerning hospitals. These factors could explain 72% of the changes of behavioral intention. Concentrating on them would lead to the improvement of the acceptance and Hospital Information System efficiency.

This article will discuss Semantic Web standards and ontologies in two areas: (1) the research and (2) healthcare. Semantic Web standards are important in the medical sciences since much of the medical research that is available needs an avenue to be shared across disparate computer systems.
Material and This review article was performed based on a literature review and internet search through scientific databases such as PubMed, Scopus, and Web of science and Google Scholar. Ontologies can provide a basis for the searching of context-based medical research information so that it can be integrated and used as a foundation for future research. The healthcare industry will be examined specifically in its use of electronic health records (EHR), which need Semantic Web standards to be communicated across different EHR systems. The increased use of EHRs across healthcare organizations will also require ontologies to support context-sensitive searching of information, as well as creating context-based rules for appointments, procedures, and tests so that the quality of healthcare is improved. Literature in these areas has been combined in this article to provide a general view of how Semantic Web standards and ontologies are used, and to give examples of applications in the areas of healthcare and the medical sciences.

One of the most important issues in managing diabetes is the periodic checkups and tests to prevent the secondary complications of the disease. Low level of literacy in patients with diabetes, and the widespread use of abbreviations and numbers in the lab test results, makes it difficult for the patient to understand and interpret her health status. The purpose of this study is to design an expert system based on clinical guidelines in order to interpret the laboratory test results to patients and provide relevant recommendations in a textual report. The study consists of two phases: the design and the evaluation. Design phase consists of 4 stages. In the first step, based on a Delphi study, the biological and laboratory tests, periodically measured for diabetic patients, were identified. In the second phase, according to the American Diabetes Association guideline, the rules for the interpretation of tests were extracted. In the third stage, an observational study was conducted to identify the elements of explanations that were provided by the physician about the results of patients' tests. In the fourth stage, the template messages were designed. In the evaluation phase, 12 diabetic patients assessed the usability of the generated report in two aspects of the visual design and the content. Five indices of apparent attractiveness, ease of comprehension, applicability, description adequacy, and novelty of content was evaluated with a 5-point Likert scale checklist. The results of the Delphi study revealed that routine tests for diabetic patients included three profiles (e.g. blood glucose, blood lipids, and kidney status), with two examinations (e.g. blood pressure and weight). The structure of the report was designed according to the patient physician communication at visit sessions. Each section of the report includes three types of feedback: descriptive, comparative, and conclusive statements. The average age of participants was 56.4 years with 72.1% women. Patients believed that the report was attractive with an average score of 9.3, and evaluated the report's comprehensiveness with an average score of 9.4. The usability (8.3), the information adequacy (8.7) and the novelty (8.2) were also perceived acceptable by patients. The results showed that the report was acceptable from the perspective of diabetic patients, and patients would like to get more information about their health status. The findings of this study can be used as guidance to design of the next phase of the study, e.g. evaluation of intervention effectiveness

One of the challenges facing medical science is the time and correct diagnosis of diseases. Particularly with regard to certain diseases such as the types of cancer, which are the leading causes of death worldwide, their early diagnosis has a significant impact on the control and treatment of this disease. The use of intelligent decision support systems with high precision can be a good way to reduce human error due to fatigue and lack of experience. Therefore, the present study tries to predict the disease by using data mining techniques and taking into account the variables that influence the prediction of laryngeal cancer. This study is an analytical study. The data from the 249 cases referred to Shafa Hospital in Kerman in 2017 have been obtained. This study is based on the Crisp methodology and in the MATLAB software environment. First, in order to understand the laryngeal cancer, a review of related studies was conducted and interviewed by specialist physicians. Then, according to expert opinion, 24 variables were identified as effective factors in predicting laryngeal cancer. After clearing and preparing data, an artificial neural network model was used to predict the risk of laryngeal cancer. In the following, another model of the combination of the genetic algorithm and the neural network was created. Using genetic algorithm, 9 functional features of prediction of laryngeal cancer were determined from among the 24 selected variables, and artificial neural network was used to predict the risk of laryngeal cancer. Finally, the criteria for accuracy, specificity, and sensitivity were used to evaluate the two models. The genetic algorithm reduced the complexity of the model by reducing the number of features from 24 to 9, but improved the average precision from 80% to 84%. Also, the model made with the characteristics selected by the genetic algorithm, increased the specificity and accuracy criteria by 13% and 8%, respectively. Combining the genetic algorithm with the neural network, in addition to improving the accuracy of prediction of laryngeal cancer, accelerates the diagnosis process, especially at the data collection stage, by reducing the number of characteristics required. Therefore, using this model as a smart decision system is suggested.

Cardiovascular diseases have high morbidity and mortality rate. Disease registry system is a clinical information system designed and implemented for patient information management and one of the essential steps in its implementation is the analysis of collected data. Since the basis of data collection in each data registry system is data element, this research was conducted to compare data elements and data analysis in myocardial infarction registry system in selected countries and Iran. This research was applied in a comparative way. The research community included the registry of myocardial infarction in the United States, Switzerland, Malaysia and Iran. The data collection method was to study the documentation and interview with the registry specialists and analyze the findings was done by drawing comparative tables Of the 26 extracted data elements, there were only 16 cases in Iran's registry system. In all registry systems, data elements were defined in the data dictionary in order to unify the definitions. Data analysis was done regularly in all three countries of the United States, Switzerland and Malaysia, but in Iran, data analysis was limited to the number of patients and the distribution of age and sex of patients. In this study, an overview of the data elements and the method of data analysis in the myocardial infarction registry system was presented that could be used in designing this registry system.

Medical image processing aimed at reducing human error rates attracted many researchers. The Segmentation of magnetic resonance image for tumor detection is one of the recognized challenges in the treatment of the disease. Considering the importance of this issue in the present study, the diagnosis of brain tumor is considered. One of the most popular and most widely used methods in the field of segmentation of images of resonance imaging of the brain is the k-means clustering algorithm, which, despite the diagnosis of a tumor, fall in to local optimum problem, followed by a reduction in the accuracy of the diagnosis tumors are malignant. In this study, we aimed to solve this problem and subsequently increase the accuracy of diagnosis of malignant tumors, a GA-clustering combination of clustering based on k-means and genetic algorithms. How to combine in the way that the genetic algorithm is applied to each repetition of the K-means algorithm and, by scanning more in the space of the answer, is trying to find higher quality cluster centers. The effectiveness of the proposed method has been investigated on a number of images of BRATS standard collections. It is also compared with the K-means algorithm. The results show that the proposed algorithm provides better results than the K-means algorithm.

In recent decades, the use of Decision-Fusion techniques has attracted the attention of many scholars and academics. The use of this technique to manage challenges such as diversity and scalability in the Big Data is very common in various industries, including the health care industry. Hence, a comprehensive review of studies on the use of this technique in the field of health care and its review of the types of applied methods, the type of used data, the obtained data, the use and purpose of technique can be useful. Therefore, the protocol of this Scope Review article will be presented to examine this technique in the field of health care. The protocol was designed based on O'Malley and Arksey's five-step framework in combination with Levac and colleages’ enhancement. First, a field-specific structure was defined for study. This structure consists of three main aspects: the purpose and hypothesis, modeling, model achievements. Considering this structure, the 5-step framework was created for the study. Three databases, PubMed, science direct, and EMBASE were selected for search and an appropriate strategy for incorporating health related articles that utilized this technique was used. Data was extracted based on defined aspects, and categories were created based on their frequency. To analyze the extracted data from articles, frequency analysis, descriptive statistical methods and qualitative thematic analysis will be used. This paper is the first study of Scope Review regarding the use of Decision-Fusion technology in health care. Reference frame questions aspects are designed as field-specific. To clarify the research questions, O'Malley Arksey's five-step framework was used in combination with Levac et al.enhancement. A classify scheme for the category of the aspects [for the categorization of the dimensions] was presented based on the frequency of their values A classify scheme for the category of the aspects [for the categorization of the dimensions] was presented based on the frequency of their values.