IntroductionThe successful implementation of health information technologies requires investigating the factors affecting the acceptance and use of them. The aim of this study was to determine the most important factors affecting the adoption of health information technologies by doing a systematic review on the factors affecting the acceptance of health information technology.MethodsThis systematic review was conducted by searching the major databases, such as Google Scholar, Emerald, Science Direct, Web of Science, Pubmed, and Scopus. We used various keywords, such as adoption, use, acceptance of IT in medicine, hospitals, and IT theories in health services, and we also searched on the basis of several important technologies, such as Electronic Health Records (HER), Electronic Patient Records (EPR), Electronic Medical Records (EMR), Computerized Physician Order Entry (CPOE), Hospital Information System (HIS), Picture Archiving and Communication System (PACS), and others in the 2004–2014 period.ResultsThe technology acceptance model (TAM) is the most important model used to identify the factors influencing the adoption of information technologies in the health system; also, the unified theory of acceptance and use of technology (UTAUT) model has had a lot of applications in recent years in the health system. Ease of use, usefulness, social impact, facilitating conditions, attitudes and behavior of users are effective in the adoption of health information technologies.ConclusionBy considering various factors, including ease of use, usefulness, and social impact, the rate of the adoption of health information technology can be increased.

ObjectivesThe incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care.MethodsThis is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives.ResultsThe main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables.ConclusionsIt is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.

Background Students with complex health care services process face constant challenges with regard to health education. The mobile devices are an important tool that can install various applications for using information such as clinical guidelines, drug resources, clinical calculations, and the latest scientific evidence without any time and place limitations. And this happens only when students accept and use it. Objective The purpose of this article is to identify the factors influencing students in their intention to use mobile health (mHealth) by using Unified Theory of Acceptance and Use of Technology (UTAUT) model. Methods A standard questionnaire was used to collect the data from nearly 302 Lorestan University of medical science students including nutrition and public health, paramedicine, nursing and midwifery, pharmacy, dentistry, and medical schools. The data were processed using LISREL (Scientific Software International, Inc., Lincolnwood, Illinois) and SPSS (IBM Corp., Armonk, New York) softwares and the statistical analysis technique was based on structural equation modeling (SEM). Result A total of 300 questionnaires including valid responses were used in this study. The results showed that mediator of age did not affect the predictors of intention to use mHealth, and the level of education and gender directly affected the intention to use. In addition, effort expectancy, facilitating condition, and behavioral intention directly and indirectly have effect on use, whereas the result revealed no significant relationship between two important processes of performance expectancy and social influence with students' behavioral intention to use the mHealth. Conclusions The present study provides valuable information on mobile health acceptance factors for widespread use of this device among students of universities of medical sciences as a base infrastructure for a variety of information about health services and learning. Review and comparison of results with other studies showed that mHealth acceptance factors were different from other end users (elderly, patients, and health professionals).

The results confirmed that several factors in the TAM2 that were important in previous studies were not significant in paraclinical departments and in government-owned hospitals. The users' behavior factors are essential for successful usage of the system and should be considered. It provides valuable information for hospital system providers and policy makers in understanding the adoption challenges as well as practical guidance for the successful implementation of information systems in paraclinical departments.

This review study aimed to compare the electronic prescription systems in five selected countries (Denmark, Finland, Sweden, England, and the United States). Compared developed countries were selected by the identified selection process from the countries that have electronic prescription systems. Required data were collected by searching the valid databases, most widely used search engines, and visiting websites related to the national electronic prescription system of each country and also sending E-mails to the related organizations using specifically designed data collection forms. The findings showed that the electronic prescription system was used at the national, state, local, and area levels in the studied countries and covered the whole prescription process or part of it. There were capabilities of creating electronic prescription, decision support, electronically transmitting prescriptions from prescriber systems to the pharmacies, retrieving the electronic prescription at the pharmacy, electronic refilling prescriptions in all studied countries. The patient, prescriber, and dispenser were main human actors, as well as the prescribing and dispensing providers were main system actors of the Electronic Prescription Service. The selected countries have accurate, regular, and systematic plans to use electronic prescription system, and health ministry of these countries was responsible for coordinating and leading the electronic health. It is suggested to use experiences and programs of the leading countries to design and develop the electronic prescription systems.