reza safdari

Registries are regarded as a just valuable fount of data on determining neonates suffering prematurity or low birth weight (LBW), ameliorating provided care, and developing studies. This study aimed to probe the studies, including premature infants’ registries, adapt the needed minimum data set, and provide an offered framework for premature infants’ registries. For this descriptive study, electronic databases including PubMed, Scopus, Web of Science, ProQuest, and Embase/Medline were searched. In addition, a review of gray literature was undertaken to identify relevant studies in English on current registries and databases. Screening of titles, abstracts, and full texts was conducted independently based on PRISMA guidelines. The basic registry information, scope, registry type, data source, the purpose of the registry, and important variables were extracted and analyzed. Fifty-six papers were qualified and contained in the process that presented 51 systems and databases linked in prematurity at the popular and government levels in 34 countries from 1963 to 2017. As a central model of the information management system and knowledge management, a prematurity registry framework was offered based on data, information, and knowledge structure.  To the best of our knowledge, this is a comprehensive study that has systematically reviewed prematurity-related registries. Since there are international standards to develop new registries, the proposed framework in this article can be beneficial too. This framework is essential not only to facilitate the prematurity registry design but also to help the collection of high-value clinical data necessary for the acquisition of better clinical knowledge.

Kidney failure is a common and increasing problem in Iran and worldwide. Kidney transplantation is recognized as a preferred treatment method for patients with end-stage renal disease (ESRD). Machine learning, as one of the most valuable branches of artificial intelligence in the field of predicting patient outcomes or predicting various conditions in patients, has significant applications. The purpose of this research was to predict kidney transplant outcomes in patients using machine learning.

Since CRISP is one of the strongest methodologies for implementing data mining projects, it was chosen as the working method. In order to identify the factors affecting the prediction of kidney transplant outcomes, a researcher-created checklist was sent to some of nephrologists nationwide to determine the importance of each factor. The results were analyzed and examined. Then, using Python language and different algorithms such as random forest, SVM, KNN, deep learning, and XGBoost the data was modeled.

The final model was multilabel, capable of predicting various kidney transplant outcomes, including rejection probability, diabetic reactions, malignant reactions, and patient rehospitalization. After modeling the input data features, the model was able to predict the four kidney transplant outcomes such as rejection, diabetes, malignancy and readmission with an error rate of less than 0.01.

The high level of accuracy and precision of the random forest model demonstrates its strong predictive power for forecasting kidney transplant outcomes. In this study, the most influential factors contributing to patient susceptibility to the mentioned outcomes were identified. Using this machine learning-based system, it is possible to predict the probability of these outcomes occurring for new cases.

 Due to the increased price of foods in recent years and the diminished food security in Iran, nutrition recommender systems can suggest the most suitable and affordable foods and diets to users based on their health status and food preferences.

 The present study aimed to design and evaluate a recommender system to suggest healthy and affordable meals and provide a tele-nutrition consulting service.

 This applied three-phase study was conducted in 2020. In the first stage, the food items’ daily prices were extracted from credible sources, and accordingly, meals were placed in three price categories. After conducting a systematic review of similar systems, the requirements and data elements were specified and confirmed by 10 nutritionists and 10 health information management and medical informatics experts. In the second phase, the software was designed and developed based on the findings. In the third phase, system usability was evaluated by four experts based on Nielsen’s heuristic evaluation.

 Initially, 72 meals complying with nutritional principles were placed in three price categories. Following a literature review and expert survey, 31 data elements were specified for the system, and the experts confirmed system requirements. Based on the information collected in the previous stage, the Web-based software TanSa in the Persian language was designed, developed, and presented on a unique domain. During the evaluation, the mean severity of the problems associated with Nielsen’s 10 principles was 1.2, which is regarded as minor.

 To promote food security, the designed system recommends healthy, nutritional, and affordable meals to individuals and households based on user characteristics.

Atherosclerosis of coronary arteries is the most common heart disease and indication of coronary artery bypass graft(CABG). After CABG, patients need proper self-care and lifestyle changes to increase their quality of life. The present research aimed to the conceptual design of self-care Android software, focusing on the third phase of cardiac rehabilitation.

This descriptive research has been carried out in two main phases and a total of six steps in order to assess the informational and functional needs of self-care software and design its conceptual model. After searching in reliable scientific sources and guidelines and checking the available Persian software, through the content validity index questionnaire of Lavshe, Welts and Bassel, 23 people from the medical staff with a history of caring for heart patients participated in the step of determining the requirements and the results were analyzed using SPSS Software. Descriptive analysis was done. Finally, the software model was prepared using integrated modeling language and th mind map was drawn.

Sixty two items were identified in 6 general categories (personal information, medical records, interventions, personal health record, essential information, emergency communication). In the phase of determining the requirements by the experts, 52 items were accepted and 10 items were included in the conceptual model of the application program with minor changes and revisions.

Using mobile health in self-care and cardiac rehabilitation will be a suitable solution in order to increase treatment, follow-up and optimal continuation of self-care in patients. By having different functions, this tool can facilitate lifestyle change and help patients in secondary prevention of cardiovascular events and improve their health.

Nutrition recommender systems are one of the most prominent technologies in the field of nutrition informatics that help users to have a healthy diet. These systems are able to suggest the most appropriate foods and meal plans considering the child's physical condition. Considering that children are the most vulnerable age group, one of the most important health goals in the early years of a child's life is achieving balanced and correct nutrition, and as a result this study was conducted with the aim of designing, creating and evaluating a children's nutrition recommendation system.

This research was conducted in 2022 in three stages to assess the needs of the informational elements of the nutrition recommender application. First, in order to determine data capabilities of the program, data needs analysis has been done. In the following, based on the needs assessment, the application was designed and its final version was created. The application was created in the Android Studio environment and then its technical applicability was done using the Nielsen questionnaire by 5 medical informatics and health information management experts.

The required information capabilities of the program were determined and after the statistical analysis of the questionnaire, most of the items raised in it were deemed necessary by the experts participating in the research with a high percentage (90%) and were included in the application program. Then the design and creation of the nutrition recommender application for children 1 to 5 years old was done in the Android Studio programming environment. Finally, the use of Nielsen's exploratory evaluation questionnaire and its technical applicability were evaluated by experts. The average severity of the problems related to Nielsen's ten principles was 1.3, which is classified as minor problems.

Using the nutrition recommender application will be a useful solution to increase parents' awareness of the child's growth status in terms of age, height and head circumference. This program has improved nutritional health, improved mental health, and better growth of children and will play a significant role in preventing the occurrence of diseases by using appropriate nutritional recommendations.

Simulation-based Mobile Education is one of the necessities of the new era as well as an escalating field to meet the needs of different educational systems and levels. Considering the increasing application of simulation-based technologies, and the popularity of Augmented Reality (AR) in the field of education, the present research aims to facilitate the initial steps in understanding the technical requirements and software used in developing these types of apps.

The data required for this research was collected by searching in library resources to identify the commonly used platform/software in designing and developing AR-based educational apps. At the same time, the included studies’ app features/sections were also extracted, and the total of this information was presented in a table. After indicating the mainly used software in the development of these types of apps, a simple app architecture and a how-to-develop model using the most practical software were illustrated.

“3D Model display” and “multiple-choice questions” were identified as the minimum (basic) features of an AR-based educational app. To identify the most commonly used software in creating AR-based educational apps, articles were reviewed, required information was extracted, and gathered in form of a table. The results of the review showed that Unity 3D and Vuforia have the highest usage statistics in creating these types of apps. Finally, a simple model of the process of developing AR-based educational app and a sample output of this type of app were presented based on this information.

Simulation-based education is one of the prominent and growing topics worldwide. Augmented Reality is one of the most renowned and widely used technologies in this field. The results of this study encompass a set of basic features of an AR-based educational app as well as the software that can be used to create such apps. Therefore, this study can assist researchers interested in this field in navigating the simplest path to developing an AR-based educational app by providing them with an initial technical understanding of AR-based education.

According to WHO, 190 million reproductive-aged women are affected by endometriosis. Using self-care interventions has a significant impact on managing endometriosis-related pain. Despite the enormous potential of different endometriosis applications, the medical professionals’ role has been neglected in the process of app development. This study aimed to extract the requirements for developing a mobile-based app for self-care of endometriosis patients through an overview of the literature and validate them according to the expert gynecologists’ point of view.

This cross-sectional descriptive study was carried out in two steps. First, endometriosis-related articles were reviewed. Second, a researcher-made questionnaire (Cronbach’s alpha=0.98) was designed to validate the identified information elements. Elements that obtained at least an average score of 3.2 (60%) out of 5-point Likert scale, were considered as required data elements for designing an android-based mobile app for self-care of endometriosis patients.

Based on the literature review, 36 studies were retrieved and 126 data elements were extracted. The elements were classified into six categories including electronic health record, educational materials, follow-up, pain management, nutritional diet, and lifestyle. All data elements except “using traditional opioids/drugs” were verified.

In this study, a minimum data set was achieved for designing an endometriosis mobile app. Since due to the lack of international standards for designing health apps, the results of this research can be beneficial for the design and development of endometriosis apps. In the current research, an effort has been made to study all related references carefully in order to provide a comprehensive data set for designing and developing an app. This data set could be useful not only for designing a mobile-based app but also for the design of any other systems which is related to endometriosis.

Sleep apnea is one of the most common sleep disorders that facilitating and accelerating its diagnosis will have positive results on its future trend.  This study aimed to diagnosis the sleep apnea types using the optimized neural network. This descriptive-analytical study was done on 50 cases of patients referred to the sleep clinic of Imam Khomeini Hospital in Tehran, including 11 normal, 13 mild, 17 moderate and 9 severe cases. At the first, the data were pre-processed in three stages, then The Electrocardiogram (ECG) signal was decomposed to 8 levels using wavelet transform convert and 6 nonlinear features for the coefficients of this level and 10 features were calculated for RR Intervals. For apnea categorizing classes, the multilayer perceptron neural network was used with the backpropagation algorithm. For optimizing Multi-layered Perceptron (MLP) weights, the Particle Swarm Optimization (PSO) evolutionary optimization algorithm was used.  The simulation results show that the accuracy criterion in the MLP network is allied with the Backpropagation (BP) training algorithm for different types of apnea. By optimizing the weights in the MLP network structure, the accuracy criterion for modes normal, obstructive, central, mixed was obtained %96.86, %97.48, %96.23, and %96.44, respectively. These values indicate the strength of the evolutionary algorithm in improving the evaluation criteria and network accuracy.   Due to the growth of knowledge and the complexity of medical decisions in the diagnosis of the disease, the use of artificial neural network algorithms can be useful to support this decision.

According to World Health Organization (WHO), cardiovascular diseases (CVDs) are the leading cause of death globally. Although significant progress has been made in the diagnosis of CVDs, more investigation can be helpful. Therefore, this study aimed to predict the risk of myocardial infarction (MI) using data mining algorithms.

The applied data were related to the admitted patients in Rajaei specialized cardiovascular hospital located in Tehran. At first, a literature review and interview with a cardiologist were conducted to understand MI. Then, data preparation (cleaning and normalizing the data) was performed. After all, different classification algorithms were applied in IBM SPSS Modeler (14.2) software on the prepared data; and, power of the applied algorithms and the importance of the risk factors in predicting the probability of getting involved with MI was calculated in the mentioned software.

This study was able to predict MI % 75.28 and 77.77% in terms of accuracy and sensitivity, respectively. The results also revealed that cigarette consumption, addiction, blood pressure, and cholesterol were the most important risk factors in predicting the probability of getting involved with MI, respectively.

Predicting studies aim to support rather than replace clinical judgment. Our prediction models are not sufficiently accurate to supplant decision‑making by physicians but have considerable tips about MI risk factors.

Preeclampsia is one of the most serious cases of high-risk pregnancies that endanger women’s health worldwide, especially in developing countries. Preeclampsia is a specific pregnancy syndrome with a prevalence of about 7-14%, which is one of the three leading causes of death in pregnant women. Preeclampsia is the second most common cause of maternal mortality in Iran and accounts for 14% of maternal mortality. The present study was conducted to design, create and evaluate mobile-based preeclampsia self-care application.

This study was conducted in four stages to assess the needs of information elements, design, create and evaluate preeclampsia self-care application. In needs assessment step, 42 specialists, assistants and personnel related to the subject working in the Amir Al-Momenin (AS) Educational, Research and Treatment Center affiliated to Semnan University of Medical Sciences participated. The program was then initially evaluated by 7 physicians, and finally the suggestions provided by users in the design of the program were applied and the final version of the program was completed. The application was designed in the Android Studio environment and then its usability was evaluated using the opinions of 20 mothers and the QUIS tool.

The information elements and functional capabilities required by the program were determined. In addition, the program established communication between the patient and the provider, also created the possibility of care management and control of the disease process. The performance of the program was evaluated by physicians and experts and then evaluated by pregnant mothers in terms of usability. The findings showed that users were satisfied with the application.

The use of mobile-based applications is a useful way to increase knowledge and promote the health of pregnant mothers and facilitate their access to medical information and acquire the necessary skills in their disease. This program helps pregnant mothers with preeclampsia to control their disease by observing proper nutrition and treatment principles to minimize the complications of their disease.

Many risk factors can cause biliary system diseases. Hence, this category of diseases is amongst the most common ones. Active patient cooperation is very important in disease management, self-care, and clinical outcomes improvement. A mobile phone application has a high potential in supporting the patients’ self-management. Therefore, this study was conducted to recognize and define data elements to develop a self-care application for biliary patients.

The current descriptive study was conducted in 2 stages, resource investigation, and data elements’ need assessment. In the first stage, scientific articles available in databases were used for defining required data elements to develop the application for biliary patients, and a checklist of data elements was prepared. In the second stage, a questionnaire was made based on the checklist. Content and face validity were accepted by the research team and the reliability was calculated 87.2%, using the Cronbach’s alpha test. The mentioned questionnaire was given to Gastroenterologists at Imam Khomeini Hospital complex, and the elected data elements were recognized.

In this application, data elements were categorized into seven sections, including demographic and clinical information, data related to the biliary system diseases, postoperative lifestyle information of the biliary patients, reminders, disease management, and informing. Sixty point five percent of the responders gave the highest importance to data elements in the demographic and clinical data section. Data elements related to patients’ education were considered highly important by 54.2% of the responders. Forty three point eight percent gave the highest importance to data elements in interventional applications’ sections, and only 4.2% gave the least importance to this section.

Based on the identified data elements, a self-care application was designed and developed and can be used as a supplement to specialized care for biliary patients.

Due to the complexity of prognosis, diagnosis, and treatment in the process of providing care for patients with oral cancer, a large amount of data elements have been processed. The present study was conducted to provide a minimum data set for managing the data generated in the diagnosis and treatment processes of oral cancer by reviewing the specialized literature, medical records and by gathering expert opinions.
This research was a descriptive cross-sectional study with the following steps: reviewing texts and records, developing a draft of data elements, organizing a panel of experts, Delphi techniques, and creating a final pattern.
The framework proposed in this study for managing the data generated in the diagnosis and treatment processes of oral cancer was divided into six sections: management data with four-axis, historical data with four-axis, paraclinical indicators with two-axis, clinical indicators, data related to the therapeutic measures, and mortality data.
The systematic collection of the data associated with the diagnosis and treatment of the patients with oral cancer could provide a good basis for identifying patients or those who are susceptible to this type of cancer in the community. These data can also be used in programs to prevent the development and/or emergence of the disease, thus the health of the community.

Child abuse is a significant global concern, with short-term and long-term consequences for the lives of children who are victims of violence. For effective action by decision-makers, the information and analysis obtained from surveillance systems must be used to determine the problem, the magnitude of maltreatment, its relationship with other issues, and the likelihood of preventing maltreatment. Due to the importance of child abuse and the management and control of this event, this study was conducted to design and evaluate a child abuse surveillance system. The system was designed in Visual Studio version 2017 using the C# programming language and ASP.NET framework. SQL Server was used to store the data. The design of the child abuse surveillance system was evaluated according to the usability evaluation. The minimum data set was indexed to collect and store data on abused children by the standard format. Web-based child abuse surveillance system (CASS) has 3 types of users. The system evaluation results showed that the highest number of problems were related to the principle of "help and documentation". Designing a CASS is a practical step in managing and controlling the data of abused children. This system and registration of information will help professionals, managers, and decision-makers make the right decisions to take care of injured children with up-to-date information

The quality management and financial control of drugs have been considered as a priority for healthcare managers. The drug classification and coding systems, as an information management tool, could be beneficial. The review aims to extract the characteristics of the drug classification systems and identify their main applications in the drug management processes.

For this purpose, the library sources including e-databases PubMed, Scopus, Web of Science, and Google Scholar search engine, e-files, and specialized websites were searched using keywords “Drug”, “Classification system”, “Coding system”, and “Terminology” alongside their synonyms. The search results were limited to the drug classification systems that categorize drugs and pharmaceutical information using code sets with an appropriate granularity level.  

Twenty-eight drug classification systems were included. Half of these systems are used internationally, and the others are used nationally. All included systems were divided into three categories, based on their features. The domain classification of systems includes human drugs, animal drugs, herbal medicines, dosage forms, drug side effects, and ingredients of medicinal products. Most of them are hierarchically designed. The code structure of these systems was mainly numerical, and some of them were alphabetical-numeric or alphabetical. They are mostly applied for unique identification, interoperability, statistics, pharmacovigilance and drug-related problems, marketing, and artificial intelligence methods.  

The drug classification systems are designed in different ways with respect to their applications. The development of multipurpose systems and provision of  efficient mapping among these systems could be beneficial to improve the drug management processes.

Sleep apnea syndrome can be considered as one of the most serious risk factors of sleep disorder. Due to the lack of information about this disease, many causes of unexpected deaths have been identified. With increasing the number of patients with this disease around the world, many patients suffer apnea complications. Most of them are not treated because of the complex and costly and time - c onsuming polysomnography (PSG) diagnostic procedure.

This descriptive - analytical study was performed on 50 patients referred to sleep clinic of Imam Khomeini Hospital in Tehran, Attempts to design, and develop a system for detection of sleep apnea and its severity using ECG signals, RR intervals and airflow. The random forest algorithm and MATLAB2016 were used in the design of the system that the algorithm inputs are extracted 8 features nonlinear in time - frequency domain from airflow and ECG signals and 10 nonlinear features of RR intervals.

The accuracy for normal, obstructive, central and mixed apnea was obtained at 95.3%, 97.92%, 99.60%, and 97.29%, respectively, and the accuracy For detection of normal, mild, moderate and severe apnea was obtained 96%, 94%, 94%, 96% respectively. According to the results, the proposed system can correctly classify the types of sleep apnea and its severity.

The proposed system, which has high performance capability in addition t o increasing the physician speed and accuracy in the diagnosis of apnea can be used in home systems and the areas where healthcare facilities are not sufficient.

Among sleep-related disorders, Sleep apneahas been under more attention and it’s the most common respiratory disorder in which respirationceases frequently which can lead to serious health disorders and even mortality. Polysomnography is the standard method for diagnosing this disease at the moment which is costly and time-consuming. The present study aimed at analyzing vital signals to diagnose Sleep apneausing machine learning algorithms.

This analytical–descriptive was conducted on 50 patients (11 normal, 13 mild, 17 moderate and 9 severe patients) in the sleep clinic of Imam Khomeini hospital. Initially, data pre-processing was carried out in two steps(noise elimination and moving average algorithm). Next, using thesingular value decompositionmethod, 12 features were extracted for airflow. Finally, to classify data, SVM with quadratic, polynomialand RBF kernels were trained and tested.

After applying different kernel functions on SVM, the RBF kernel showed the most efficient performance.After 10 fold cross validation method for evaluation, the mean accuracy obtained for normal, apnea, and hypopnea modes were 92.74%, 91.70%, 93.26%.

The results show that in online applications or applications where the volume and time of calculations and at the same time the accuracy of the result is very important, The disease can be diagnosed with acceptable accuracy using machine learning algorithms.

Large amounts of hospital costs are not reimbursed annually by health insurance as deductions. Therefore, reducing deductions is very important for the hospital. In the study of design and implementation of analytical dashboard of insurance deductions based on medical intelligence business, to improve financial management with the aim of focusing on assessing the level of satisfaction and its applicability has been done.

To design the questionnaire, first 27 questions were prepared through library studies and interviews with members of the hospital board of directors, and the validity and consistency of its items were determined through content validity and Cronbach’s alpha coefficient. Data were analyzed in SPSS software and the results were used to design and implement the dashboard.

The study is of development-applied type. In the first phase, to determine Content Validity Ratio CVI (Content Validity Index), and CVR (Content Validity Ratio) a researcher-made questionnaire was provided to 20 experts. In the second phase, by building a data warehouse in SQL (Structured Query Language), the information of the tables related to the deductions of the hospital HIS system was transferred to it and the operational information of the organization was extracted and converted into DW format and the map information was tested. OLAP (Online Analytical Processing) services were then loaded on the created analytics database. In the last step, Power BI tool was selected and used to create business intelligence mechanisms, display and visualize information. In the third phase, using the QUIS (Questionnaire for User Interface Satisfaction) standard questionnaire, the level of satisfaction and usability of the dashboard was evaluated by 15 experts.

In this study, two questionnaires were used. CVR was measured in all items of the first questionnaire, more than 0.50 and CVI was measured in the upper areas of 0.90 and Cronbach’s alpha coefficient was obtained between 0.8 and 0.9, which indicated a good level. The second questionnaire was to evaluate the level of satisfaction and usability of the dashboard that the average of the total evaluation based on the indicators of the QUIS questionnaire is equal to 85.40. Therefore, the level of satisfaction and usability of the dashboard was “very good” for the evaluators.

Due to the outbreak of coronavirus disease 2019 (COVID-19), applying telehealth and telemedicine to prevent the spread of the disease is inevitable.

Therefore, this study aimed to investigate the application of telehealth and telemedicine in the human coronavirus epidemic.

The systematic search was conducted using Medline (through PubMed), Scopus, and ISI Web of Science to identify relevant studies published until June 10, 2020. The inclusion criteria were the usage of telemedicine and telehealth as healthcare servicesduring COVID-19, severe acute respiratory syndrome (SARS), or Middle East respiratory syndromeepidemics.This review was performed according to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

In total, 598 articles were identified after the removal of the duplicates. After the systematic screening, 18 studies met the inclusion criteria.The analysis showed that only one study was related to SARS, and the rest were on COVID-19 disease. Teleconsultation and televisit were more prevalent (55.6%) than the other types of telemedicine services. The most mentioned obstacles were access to suitable technologies and lack of assessment or follow-up to achieve outcomes.

The results indicated that telehealth and telemedicine could have advantages, such as prevention of the spread of COVID-19, reduction of the healthcare burden, and maintenance of appropriate patient care. In addition to these benefits, several limitations and obstacles, including organizational, technological, and patient-related barriers, may be encountered. Hence, it is better to consider the necessary arrangements before implementing telemedicine.

Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine.

This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed.

We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed.

The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Medical diagnostic laboratories are among the most important centers in the treatment cycle of patients. Today, the conscious choice of such laboratories is one of the challenges that patients face in the treatment process. This study was conducted with the aim of improving the knowledge of software users in the field of laboratory sciences and also facilitating the conscious and intelligent selection of the laboratory required by users. 

This is a descriptive-developmental research with an applied approach. The steps consisted of library studies, questionnaire-based needs assessment, collection of knowledge and identity data, design through drawing UML diagrams, implementation using Java programming language, and software evaluation.  

A comprehensive system of laboratory information and experiments can be performed in all laboratories in Tehran, based on factors such as location access, types of laboratories and types of tests, a system was designed that allows users to access the most appropriate laboratory centers with high speed and less mobility, sufficient information, and in accordance with their needs. The evaluation was done using a researcher-made questionnaire whose validity and reliability were confirmed. The target population consisted of eleven specialists and forty ordinary users. According to the Likert criterion, the results obtained from the answers of all participants in the study to the questions of the questionnaire were higher than 4.05.  

The software showed that the factors that had priority in the need assessment significantly increased user satisfaction and also provided ease of use of laboratory services in accordance with userschr('39') needs.

 Cross-border health care, whether sanctioned by the governments (out-of-country care) or initiated by the patients (medical tourism), is on the rise globally. The medical tourism industry has shown great potential for improvement in Iran.

 We aimed to examine the representation of data elements on Iranian public and private medical tourism websites and evaluate their content maturity.

 A descriptive cross-sectional design was used to gather and statistically analyze data from 12 medical tourism websites.

 The highest level of data representation was provided for medical tourism attractions and destinations, followed by the geographical location of hospitals and the establishment of tourist agencies. Very few websites featured data elements on admission forms. Fisher's exact test revealed no statistically significant difference in the representation of data elements between the providers of medical tourism websites (P > 0.05). Most privately owned medical tourism websites and only one in the public sector had a moderate-content status. No website was confirmed to be rich or poor regarding their maturity.

 More investment is required for the development of information and communication technology infrastructures in the Iranian medical tourism industry to attract foreign service-users, although many other factors such as political and economic forces may be involved.

Taking a wide range of medications in Multiple sclerosis (MS) patients can lead to side effects and drug interactions. Therefore, the use of intelligent systems such as drug monitoring systems can help in the effective and timely treatment of MS disease. In this regard, the present study was conducted to design, development, and evaluation of the drug monitoring system for multiple sclerosis patients.

The present descriptive-developmental study was performed in four stages. In the first stage, by searching the library resources and valid guidelines, the minimum data set was determined and provided to neurologists and MS fellowships in the form of checklists for validation. Then the software was designed logically and coded based on the opinion of experts. In the last stage, the software was evaluated by end-users.

The information elements in the software design were categorized into patient demographic information, medical history, clinical signs, imaging procedures, laboratory tests for ocrelizumab and fingolimod drugs, counseling, and treatment data. Finally, the performance of the drug monitoring system was evaluated with an average of 7.9 and was approved by users.

The results of software evaluation showed that the drug monitoring system can help general practitioners, neurologists, and MS fellowships in monitoring and follow-up of patients and lead to increased patient safety.

Mobile-based programs have been developed as tools to help both patients and physicians in various fields especially in dermatology. Therefore, the main objective of this study was to review the features and contents of dermatology applications.

The methodology was comparative and descriptive. Applications in the field of dermatology were evaluated and compared through this research. Inclusion criteria included applications that have been downloaded more than 100 times in Google Play and App Store and applications designed in diagnosis in various fields of dermatology such as treatment, management, remote consulting, and self-care areas. Exclusion criteria included those developed before 2010 and those related to the non-dermatology areas. Besides, different features were considered for comparison based on literature review and expert consultation. Next, the recognized applications were reviewed and compared based on determined categories.

Based on criteria, a total of 33 applications were identified through searching. Of these, 33.3% of Apps were in the field of education, and 24.2% were in self-care. Regarding disease, applications were categorized into nine different domains. Of these, 61% of applications were covering different types of skin diseases. Also, in examining the frequency of the features of the evaluated programs, providing recommendations and suggestions with 57.57% and educational contents and the possibility of uploading images with 51.51%, respectively, had the highest frequency among the features of the programs.

Analysis showed that education and self-care domains have a high rank among others. It indicates that developing such applications could facilitate patient education and self-management by himself or caregivers. However, this area needs more attention and the using health information technology capabilities to make applications smarter in this area.