Frontiers in Health Informatics
Volume:12 Issue: 1, 2023

Universities are the origin of society's transformations in various fields and students as the main pillars of the university form the main body of various organizations and organs of the society. For this purpose, it is necessary to have information about the current situation and the attitude of students towards their field. This study was conducted with the aim of investigating the effective factors in the selection of nutrition science students of Varastegan Institute for Medical Sciences.

This is an applied study that was carried out in a descriptive-cross-sectional way. The studied population was students studying nutrition sciences at Varastegan Institute for Medical Sciences. To collect data, a questionnaire was created by a researcher, whose validity was evaluated with the help of expert panels consisting of experts in the health information technology group of Varastegan Institute for Medical Sciences, and the reliability of the questionnaire was determined by determining Cronbach's alpha.

According to the findings of the study, the majority of users were 68.7% female and 31.3% male. 43.3% of students had an average knowledge and awareness of their field and 44.8% had a positive view of their field. 8/ 38% of students chose this field based on the recommendation of others. The satisfaction with the field of study of the current study was 49.3%, which was close to the satisfaction of half of the students under study, and 61.2% of the students were very satisfied with the assignment of medical system code and having an office. 41.8% of nutrition science students were very satisfied with their career future and 41% believe in the existence of a suitable job market.

The most important factor in choosing a field is the assignment of the code of the medical system and having an office at the time of employment, and many students believe that this field is suitable for social values and the existence of a suitable job market, and they consider the relevant job interesting and purposeful to serve the society.

Social media platforms provide easy access to an unprecedented volume of information which could influence the awareness and perception of people during public health crises. The current study aims to explore the trends and content of the posts on Instagram.

We performed a retrospective content analysis of available public messages posted on Instagram. We collected data between 23 January 2020 and 25 March 2020. The inclusion criteria included an Instagram post with a hashtag related to Coronavirus (i.e. # “Corona” and # “Coronavirus”, in the Persian language). Persian hashtags were used for retrieving posts. All posts were categorized into seven categories. We performed descriptive statistics with Microsoft Excel 2019 and SPSS version 26.

A total of 4280 posts were extracted, out of which 1281 were categorized into seven main categories including News (n=205, 26.7%), Criticism (n=136, 17.7%), Education (n=112, 14.6%), Coronavirus’s impact on the healthcare system (n=100, 13%), Combating Coronavirus (n=98, 12.8%), Coronavirus’s impact on society (n=89, 11.6%), Joke (n=28, 3.6%).

Our findings revealed that the trend of posts on social media was influenced by factors such as the nature of the information sources as well as social and political occasions. This study provides insight into health dissemination on social media for future responses to public health crises.

Diabetes is a whole group of diseases in the body regulating blood sugar levels. There is a lack of response to the insulin produced by the pancreas. Until now, there is no definite cause to uncover the disease. If left untreated, other complications may occur so as damage to the organs in the body. The cells are not functioning very well as there is a lack of energy inside the body.

Health literacy is an essential indicator of health care habits and consequences. Health literacy and having the right information is effective in better managing symptoms and problems and improving the overall quality of life. This systematic review aimed to analyze previous studies and collect information on multiple sclerosis patients' health literacy.

The PRISMA guidelines were used to define the systematic review methods. PubMed, Cochrane, Web of Science, Scopus, ScienceDirect Journal, ProQuest, Wiley Online Library, SID, and Magiran databases were searched on 14 January 2022, without restrictions in publication time. We also searched Google Scholar and Research Proposal Information System. Two independent reviewers reviewed the papers' eligibility and extract data into a spreadsheet using a structured form.

Of the 165 articles retrieved, 14 were eventually included in the study. All of the studies’ audiences and targets were MS patients and their families or caregivers. Four studies examined the level of health literacy of individuals. Other objectives included determining variables affecting the relationship between patients' health literacy and behaviors, comparing the effects of lecture-based teaching and peer group experience on improving patients' health literacy, and determining psychometric characteristics of the MS patient’s health literacy questionnaire. Studies assessing people's health literacy revealed that most people have an adequate or acceptable health literacy level.

Improving the level of health literacy is one of the fundamental ways to improve the physical and mental health of MS patients to increase compliance and self-care and medication adherence. Accordingly, policymakers need to work on designing effective programs to develop health literacy and overcome the challenges associated with it.

Low levels of health literacy lead to reduced health, increased length of hospital stays, and increased use of emergency services in patients and impose higher medical costs on individuals. Considering the effect of paramedical students' health literacy on community health promotion, this study aimed to determine the level of health literacy and its associated factors in paramedical students.

This cross-sectional study was performed on 310 paramedical students during a two-month period from January to March 2021. The data collection tool was the Health Literacy for Iranian Adults (HELIA) questionnaire. Due to the COVID-19 pandemic, the questionnaire was designed online, and its link was provided to students.

Among the participants, 247 (79.7%) cases were female, and 63 (20.3%) cases were male with a mean age of 21.16 ± 1.97 years. According to the results, 3.9% of the students had inadequate health literacy, 37.3% had not so adequate health literacy, 46.6% had adequate health literacy, and 12.2% had excellent health literacy. The results of ANOVA and t-test showed a significant association between the mean total health literacy score of students and their age, gender, and semester (P <0.05).

This study findings showed that more than half of the participating students had adequate and excellent levels of health literacy. Since paramedical students are promoters of health in the community, more attention should be paid to the education of these individuals. Therefore, it is necessary to empower them in the field of health literacy.

Breast cancer is one of the most common cancers among women compared to all other ones. Machine learning (ML) techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. Using ML models performed on multidimensional dataset, this article aims to find the most efficient and accurate ML models for tumor classification prediction.

Several supervised ML algorithms were utilized to diagnosis and prediction of cancer tumor such as Logistic Regression Decision Tree, Random Forest and KNN. The algorithms are applied to a dataset taken from the UCI repository including 699 samples. The dataset includes Breast cancer features. To enhance the algorithms’ performance, these features are analyzed, the feature importance score and cross validation are considered. In this research, ML algorithms improved coupled by limited and selective features to produce high classification accuracy in tumor classification.

As a result of evaluation, Logistic Regression algorithm with accuracy value equal to 99.14%, AUC ROC equal to 99.6%, Extra Tree algorithm with accuracy value equal to 99.14% and AUC ROC equal to 99.1% have better performance than other algorithms. Therefore, these techniques can be useful for diagnosis and prediction of cancer tumor and prescribe it correctly.

The technique of ML can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of ML to evaluate breast cancer and indeed, the diagnosis and prediction of breast cancer is compared to determine the most appropriate classifier.

Various training methods such as web-based training tools have been developed to achieve the potential benefits of classification systems developed by the World Health Organization (WHO). Given that users of these tools have different levels of capability, usability problems could reduce the speed and accuracy of learning among users interacting with these tools. This study aims to identify usability problems of web-based training tools under the WHO family of international classifications (WHO-FIC).

In this descriptive and cross-sectional study, ten trained evaluators independently examined WHO-FIC training tools using the heuristic evaluation method. The identified problems were classified into 10 Nielsen’s usability heuristics. Then, their average severity was calculated.

In total, 40 usability problems were identified after merging and eliminating the duplicates. The highest number of problems was related to ICD-10 training tool (n=20). The highest number of problems was related to heuristics of aesthetic and minimalist design (25.0%), and user control and freedom (17.5%). Heuristics of flexibility and efficiency of use and helping users recognize, diagnose and recover from errors had the highest average severity of problems.

Violating heuristics of aesthetic and minimalist design, user control and freedom and recognition rather than recall were among the most common problems of WHO-FIC training tools. Evaluators reported that half of the user interface problems of WHO-FIC training tools were of major and catastrophe type. Solving the usability problems of these tools could lead to ease of work, increased speed of learning and acceptance of these systems among users.

As someone who has been following the development of hyper-automation technologies in healthcare, I wanted to write to you about the many optimistic outcomes that these technologies have already produced. I am writing to express my excitement about many potential and benefits of hyper-automation technologies in healthcare. Hyper-automation, which includes the use of smart technologies such as artificial intelligence, low-code/no-code (LCNC) platforms, machine learning, robotics and other technologies to automate and optimize processes, has the possibility to transform healthcare in many ways.

Heart disease is, for the most part, alluding to conditions that include limited or blocked veins that can prompt a heart attack, chest torment or stroke. Earlier identification of heart disease may reduce the death rate. The cost of medical diagnosis makes it perverse to cure it for the large amount of people early. Using machine learning models performed on dataset. This article aims to find the most efficient and accurate machine learning models for disease prediction.

Several supervised machine learning algorithms were utilized to diagnosis and prediction of heart disease such as logistic regression, decision tree, random forest and KNN. The algorithms are applied to a dataset taken from the Kaggle site including 70000 samples. In algorithms, methods such as the importance of features, hold out validation, 10-fold cross-validation, stratified 10-fold cross-validation, leave one out cross-validation are the result of effective performance and increase accuracy. In addition, feature importance scores was estimated for each feature in some algorithms. These features were ranked based on feature importance score. All the work is done in the Anaconda environment based on python programming language and Scikit-learn library.

The algorithms performance is compared to each other so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, random forest algorithm with F1 score 92%, accuracy 92% and AUC ROC 95%, has better performance than other algorithms.

The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the diagnosis and prediction of heart disease is compared to determine the most appropriate classifier.

Depression and anxiety are prevailing mental health disorders. The presence of several barriers in face-to-face approaches for treating these diseases and the emergence of numerous post-pharmacotherapy complications have made researchers embark on removing barriers in treating these diseases. The research on mental healthcare aiming to develop non-pharmaceutical treatments has presented and evaluated many approaches. One of these approaches is cognitive-behavioral therapy (CBT), which is effective in depression and anxiety treatment. This study aimed to design and evaluate a psychological program based on CBT and smartphones to control and alleviate the symptoms of anxiety and depression.

This study was framed into a 10-session randomized control trial with the assistance of psychologists and valid scientific books. It was designed and implemented in the form of an applied smartphone-based program. After selecting 45 samples and assigning them to intervention and control groups, the researchers examined the effect of the application-based CBT on patients’ depression and anxiety, then analyzed the results with statistical tests with SPSS software.

After the intervention, the data normality was confirmed by statistical analyses. Then, the paired T-test was used to analyze the pre-intervention and post-intervention data on anxiety and depression and the results were obtained with (P=0.001) and (P=0.002) respectively. According to the results, there was a significant difference between the results of the intervention group before and after using the software, while the control group manifested no significant differences.

With its user-centered approach, this software develops a mobile health (mHealth) program that improves and controls anxiety and depression by providing an efficient therapeutic method within a self-care program and removing spatial and temporal barriers.

Hepatitis C virus (HCV) is a major public health threat, which can be treated if diagnosed early, but unfortunately, many people with chronic diseases are not diagnosed until the final stages. Machine learning and its techniques can be very helpful in diagnosis. This study examines the factors affecting hepatitis C diagnosis using machine learning.

A total of 27 features were used with a dataset containing 1385 records of patients with different grades of HCV. The dataset was clean and preprocessed to ensure accuracy and consistency. To reduce the dimension of the dataset and determine the effective features three feature selection, Pearson Correlation, ANOVA, and Random Forest, were applied. Among all the algorithms, KNN, random forests, and Deep Neural Networks were selected to be utilized, and then their evaluation metrics, such as Accuracy and Recall. To create prediction models, fifteen features were selected for the mentioned machine learning algorithms.

Performance evaluation of these models based on accuracy showed that Deep Learning with Accuracy = 92.067 had the highest performance. KNN and Random Forest had almost the same performance after Deep Learning. This performance was achieved on dataset containing features that were selected by ANOVA feature selection.

Machine learning has been very effective in solving many challenges in the field of health. This study showed that using data-mining algorithms also can be useful for HCV diagnosing. The proposed model in this study can help physicians diagnose the degree of HCV at an affordable and with high accuracy.

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.

The aim of the present study was to investigate the different roles of m-Health in pandemic management using the Partial Least Square (PLS) modeling technique. Owing to the limited existing literature regarding theorizing and the lack of the default model in predicting the role of m-Health in pandemic management, this method was used for exploratory modeling.

The PLS model was performed with smart-PLS software for the following steps: estimating weight ratios, considering weight ratios as input, estimating parameters, model-fitting and testing hypotheses. In addition, Factor scores in regression equations were used to estimate structural parameters. PLS algorithm, Cronbach's alpha, and Composite Reliability were used for the measurement and reliability evaluation model Goodness-of-fit. In addition, the R2 index was used to evaluate the model adequacy. Bootstrapping was used for significant coefficients. The Goodness-of-fit of the model was examined via the Standardized Root Mean Square Residual (SRMR) criterion.

It is determined the measurement models goodness-of-fit which the alpha values were as follows: diagnosis construct=0.786, follow-up=0.772, treatment=0.796, health care providers=0.704 and education=0.839 with more than 0.7 for all measures for Composite Reliability, the structural model measures such as R2 were higher than 0.6 for all areas and the overall model goodness-of-fit was -0.007 for SRMR, the five hypotheses developed in the model were confirmed according standardized coefficients more than 1.96 for all paths. Furthermore, the proposed model concerning the positive and significant role of m-Health in diagnosis, treatment and follow-up, education and health providers during the pandemic era was approved.

The results of the present study can be used as a theoretical basis in developing models related to the role of m-Health in pandemic management. Also, health policymakers and practitioners could use the results to manage current and post-coronary conditions and to promote services based on various m-Health apps.

I am writing to express my views on the topic of digital trust in the healthcare industry. With the rapid advancement of technology and the widespread use of electronic health records, it is crucial to understand the impact of digital trust on healthcare. In this letter, I will discuss the importance of digital trust in healthcare, the important challenges faced by the healthcare industry in building and maintaining digital trust, and the potential solutions to address these challenges.

The aim of this systematic review was to investigate the impact of tele-ophthalmology on screening, monitoring and treatment adherence in eye diseases.

A systematic review of controlled and randomized clinical trial studies without time limit was explored by searching keywords in the title, abstract and keywords of the studies in the reliable scientific databases Embase, Web of Science, Scopus, PubMed on April 20, 2022. A gray literature search was also conducted using the Google search engine to identify the most recent possible evidence. The quality of the studies was evaluated using the Joanna Briggs Institute (JBI) checklist; that the studies with a score above 7 were included in the analysis.

A total of 40 articles were identified after removing duplicates. After screening the full text of the articles, 5 studies met the inclusion criteria. In four of the studies, tele-ophthalmology was used for tele-screening and tele-monitoring using tele-imaging approaches, live video conferencing, and websites. Also, in one case, telemedicine reminder studies were used to improve treatment adherence. In the majority of studies, tele-ophthalmology was at least as effective as in-person visit services in screening, monitoring, and adherence to treatment.

The results of our systematic review showed that a well-designed tele-ophthalmology program with high-quality cameras and equipment and the use of multiple technologies has the potential to replace or complement in-person visits to an ophthalmologist.

Considering the worldwide spread of the COVID-19 pandemic, it is critical to use electronic health (e-health) to prevent, diagnose, and treat this disease. According to reports on the use of e-health technology in past and present crises, this technology can have the potential to improve the quality and the quantity of provided services and control and manage diseases in epidemic conditions. The important issue is how to implement this technology fairly and facilitate the use of this technology by health care providers and the general public. Moreover, the concerns about the physician-patient relationship, patient privacy and health costs should be addressed. Therefore, it is necessary for health policymakers and planners to develop laws and guidelines to address legal and ethical barriers to the use of this technology, focusing on vulnerable populations, to manage the crisis and also determine the role of insurers in this area.

Nutrition counseling web apps have the ability to improve the quality of health care. The purpose of this study is to design and evaluate the usability of a nutrition counseling web app (virtual clinic) for pregnant women.

It was a descriptive-cross-sectional applied study that first designed and then examined the nutritional counseling web app (virtual clinic) for pregnant women using the heuristic evaluation method. The data was collected with a standard form designed based on the heuristic method. Data analysis was done with SPSS version 26.

The number of known individual problems was 34. The highest number of problems was related to the flexibility and efficiency component and the lowest number was related to the component of helping users in diagnosing, identifying and correcting errors. In the end, all the problems identified in the web app were solved and it was given to the evaluators again, and in the end, a score of zero was assigned to all the components, meaning no problem.

Compliance with existing standards and rules in the design of web app user interfaces, such as the heuristics mentioned in this study, can reduce problems.

Emergency Medical Services (EMS) is one of the vital links in the care chain, and its services need to be improved. These services can be available through mobile-based automation system, in which low usability level of these systems lead to decrease the acceptance, satisfaction, and confidence of users especially the emergency care team. The purpose of this study was the usability evaluation of a national mobile- based automation system among the pre-hospital emergency care team.

This cross-sectional study was conducted on pre-hospital emergency care team members in Semnan and Shahroud Universities of Medical Sciences in 2022. The usability evaluation of the mobile- based EMS automation system was done using the Software Usability Measurement Inventory (SUMI) questionnaire. Multiple logistic regression models were used to analyze data.

One hundred eighty-eight EMS team members from the 31 EMS centers in Semnan province participated in present study. The mean total usability score was 61.93±15.37, the highest mean score was related to the efficiency feature (67.19±19.85) and the lowest mean score was related to the learnability feature (48.21±29.29). There was a reverse and significant relationship between being a manager and the agreement with the usability (p=0.04, OR= -3.383, CI 95%=0.389-29549).

This study showed that although an automation system may be widely used in a country, its usability could be at a low level. In order to improve the different function of these systems, it is suggested to participate various clinical experts include prehospital emergency care team in all stages of designing and developing these systems.

The Internet of Things (IoT) and Cloud computing are two recent technological advances whose potential have not been realized in a range of industries. These technologies have been used in healthcare systems to improve their performance. In this regard, the IoT generates a large amount of data; also, cloud computing is a viable option for data storage and complex computing. The purpose of this study is to identify and categorize various aspects of CIoT-based healthcare in terms of main application domains, sensors, wireless communication technologies, messaging protocols, cloud platforms, and artificial intelligence (AI) algorithms.

We conducted a literature systematic review and reported according to the PRISMA guideline. PubMed, IEEE, Scopus, and Web of Science were searched using the related keywords and their synonyms. Two independent authors reviewed the papers' eligibility according to the defined inclusion and exclusion criteria.

Of the 2,118 papers retrieved, 61 were eventually selected in the study. Results of the present study revealed that the majority of CIoT research works were applied to patient monitoring systems and cardiovascular patient monitoring systems using Amazon and IBM dominating Cloud platforms. In addition, the most widely used communication technologies for CIoT in healthcare are cellular networks (3G and 4G), Wi-Fi, and Bluetooth. Cardiovascular, environmental, and position sensors are also the most common types of sensors used in healthcare CIoT applications. Among the Cloud platform providers, Amazon and IBM have the highest utility in healthcare systems. The majority of the included studies used Cloud-based AI algorithms to diagnose, classify, and predict diseases.

The integration of the Cloud into the IoT can support healthcare systems in terms of processing power, storage capacity, security, privacy, performance, reliability, and scalability. We suggest researchers conduct experimental studies to evaluate the effectiveness of the CIoT approach in healthcare applications.

The cardiovascular intensive care unit (CVICU) registry provides physicians with tools for monitoring, managing, and following up on patients. The CVICU registry provides researchers with the ability to analyze and evaluate integrated data patients. Our goal with this study is to explain the design, development, and deployment of a comprehensive, integrated, qualified cardiovascular intensive care unit registry, as well as to characterize individuals admitted to CVICUs.

From June 2012, a cohort study of ICU admissions for adults (≥18 years) in a teaching hospital’s CVICU began. The study includes retrospective collection of existing data from paper records and hospital information systems (HIS) and ongoing prospective collection using the proposed CVICU registry portal.

Between June 2013 and June 2022, 2587 admissions were included, among which 1041 (40.2%) were women, 1546 (59.8%) were man, and the median age was 58 ranging from 18 to 93 years and their mean (SD) age was 56.8 (13) years. About 11.1% of the patients died in the CVICU. The primary indications for CVICU care included mechanical ventilation (29.7%), weaning time or readmission (4.9%), cardiovascular (17.4%), myocardial infarction (1.7%), diabetes (9.3%), hypertension (14.3%). Of these, about 73% had coronary artery bypass grafting (CABG), 15% valve surgery and the remains has other cardiovascular surgeries. About 39% experienced an on-pump surgery. In addition, patients had 6.4 hours weaning time after operation. The overall CVICU length of stay (LOS) rate was 3.6 days and mean predicted by APACHE IV, APACHE II, SOFA, and SAPS II were 5.67, 3.03, 4, and 4 days, respectively.

The use of registries equipped physicians and researchers with an integrated data pool to manage and evaluate information. Appropriate patient registries allow effective decision-making for appropriate interventions, resource allocation, and ongoing data monitoring and analysis. Ultimately, this leads to the optimal outcomes for patients. This registry aims to generate valuable knowledge about cardiovascular ICU patients in Iran and to collect accurate and qualified data.

The growing use of online information influences people's healthcare decision making in terms of treatment or consulting a doctor. The readability of a website is a factor that influences the correct understanding of its content. Regarding that there is little information about the readability as well as the credibility of health websites in the field of kidney transplantation, the present study assesses the readability and trustworthiness of websites in this topic.

Google, Yahoo, and Bing search engines were used to search for "Kidney Transplantation" and "Renal Transplantation.". Four readability scales were applied to assess the readability of the first 30 results of each search engine. The HONcode toolbar was applied to recognize credible websites. The relationship between HONcode verification and website position on the search results pages was explored. Furthermore, the difference between the readability scores and website position on the search results pages was tested. The readability difference between search result pages was also examined.

According to the results, the assessed websites are suitable for students or high school graduates. Furthermore, the association between the average readability of websites and website position on the search results pages was significant (p-value<0.05). A significant association between HONcode-verified sites and website position on the search results pages was also revealed (p-value=0.011).

The readability of kidney transplantation websites is far above the recommended level. Therefore, health organizations must consider readability while designing their websites.

The global crisis engendered by the COVID-19 pandemic brought about profound changes in various aspects of daily life, one of which pertains to how group therapies and support meetings are conducted. Of particular interest in this paper is the Alcoholics Anonymous (AA) program, a globally recognized initiative known for its efficacy in helping individuals cope with alcoholism through mutual, peer support in group meetings. The advent of the pandemic, however, challenged this very structure of the program, enforcing a radical transition from in-person meetings to virtual environments, which has posed several hurdles for both organizers and participants. Notably, these changes triggered by restrictive mobility measures have called for innovative adaptations to continue to provide the support needed for recovery and to reduce relapses fuelled by pandemic-induced loneliness and isolation.

As university systems are dealing with a wide range of users, including students, managers, and university employees, these systems can likely satisfy the needs and activities of various users with maximum quality in the shortest possible time. Consequently, it is very essential to observe usability features in the design of such systems. The current study aimed to evaluate the usability of the Hamava system to recognize and resolve its problems and weaknesses.

The present study is a cross-sectional descriptive study that was done in the second half of 2022 on the Hamava system of students of Birjand University of Medical Sciences. Nielsen's Heuristics (ten principles) were used to check the compliance of this system with the usability principles. Descriptive statistics were used to analyze the overall severity of the identified usability factors collected using the checklist.

In the present evaluation, 176 problems were recognized, with the highest number of problems related to the violation of the two principles of aesthetic and minimalist design (n=58), user control and freedom (n=31), and the least problem related to the recognition rather than recall principle (n=4).

The results of the current study revealed that though a large number of users of the Hamava system are students and it is expected that this system will be designed based on the latest standards and the needs of its users, this system also faces usability problems that if not resolved, it can cause an increase in errors, dissatisfaction of users, decrease in the quality of information, waste of users' time, and lack of effective user interaction with the educational system.

The World Health Organization (WHO) has declared the novel coronavirus (COVID-2019) infection outbreak a global health emergency. Drug repurposing, which concerns the investigation of existing drugs for new therapeutic target indications, has emerged as a successful strategy for drug discovery due to the reduced costs and expedited approval procedures.

The crystal structure of a protein essential for virus replication has been filed in the Protein Data Bank recently. Based on this structure and existing experimental datasets for SARS-CoV2(COVID-19) we present results deriving from the virtual screening of a database of more than 1000 drugs in the DrugBank that have been approved by Food and Drug Administration (FDA).

Results showed that some of the known protease inhibitors currently used in HIV and Cancer infections might be helpful for the therapy of COVID-19 also. Results also showed that Levomefolic acid, or vitamin B9, is recommended therapy because of its oral sources and no side effects.

Between all studied FDA-approved drug, VitaminB9 and Etoposide which used for HIV protease inhibitor, revealed strong interaction with protease binding pocket and placed well into the pocket even better than the lopinavir-ritonavir, and since this compound is FDA-approved and successfully passed various testing steps, therefor there is a hope that this drug, could be a potential drug to treating the COVID-19.

Strategic dashboards, including hospital economic monitoring systems, play a major role in analyzing data and making decisions. As the health system is a multidimensional ecosystem, decision-makers and healthcare officials must gather and integrate information from numerous health information systems to monitor and direct healthcare centers. This study aims to develop and usability evaluation of a health information technology dashboard in Iran that utilizes qualitative and economic indicators.

This study was conducted in four phases. It included extracting the requirements of the system through the focus group technique. Based on these results, a comprehensive economic dashboard was developed. Then, the system’s usability is evaluated from the perspectives of experts and end-users by two scales of Nielsen and USE, respectively.

The dashboard was developed on the web and different access levels were defined for users according to their roles. This dashboard provides the ability to integrate information from different systems at the national level for decision-makers. The results of usability evaluation from users' point of view showed that it has a good level of usability. Furthermore, evaluation results revealed that aesthetic aspects and simple design and clarity of system status (0%), privacy (1.49%), "visibility and clarity of the system" and "adaptation between the system and the real world" (2.98%), have the fewest design errors. With 14 problems (20.89%), "recognition rather than remembering" and "compliance with uniformity and standards" have the highest frequency of problems.

The development of an extensive integrated economic health dashboard, based on usability principles that are suitable for its stakeholders regardless of their specialty and granted access level, is welcomed by the health economist, hospital managers, and the ones who have an active role in monitoring and coordinating hospitals or even in greater scales such as national wide decision makers.

The global COVID-19 pandemic has led to a health crisis, emphasizing the need to identify high-risk patients for effective resource allocation and prioritized hospitalization. Previous studies have been limited in their use of algorithms and variables, while this research expands to include lifestyle factors and optimizes hyperparameters for twenty machine learning algorithms, enhancing prediction accuracy and identifying key predictors.

In this cross-sectional study, we analyzed data from 207 COVID-19 patients. The Boruta algorithm was used to select the best features for twenty classification algorithms, and RandomizedSearchCV was utilized to optimize hyperparameters. The models were evaluated using performance metrics such as accuracy, f-measure, and area under the curve (AUC).

The study identified eight key predictors of COVID-19 hospitalization, which include gamma-glutamyl transpeptidase, alkaline phosphatase, diagnosis by CT scan, mean platelet volume, mean corpuscular volume, fasting blood sugar, red blood cell count, and mean corpuscular hemoglobin concentration. By optimizing the hyperparameters of twenty machine learning algorithms, the accuracy and AUC were improved. With an outstanding AUC of 81.25, the XGBClassifier model exhibited superior performance.

The findings of this study can assist clinicians in allocating resources effectively and improving patient care. Additionally, this approach can aid healthcare researchers in leveraging artificial intelligence to manage diseases.

Silica nanoparticles (SNP) are extremely promising tools in nanotechnology and nano medicine. In most of applications such as capture and release of bacteriophage viruses the nano-structures of silica are coated by bio-compatible groups such as amine compounds. The presence of amino groups on the surface of the biosensors enables the installation of analyte receptors and antifouling agents such as oligo (ethylene oxide). Therefore, in this study, the electronic and structural properties of Core-Shell amino- Silica Nanoparticles are investigated.

In this investigation, we aim at obtaining the optimized structures and evaluate the geometries of the ground state for (SiO2) n (n=16, 20) nanoclusters. The electronic properties computed by density functional theory with GGA approximation and SCC-DFTB with hybrid Slater-Koster files are investigated and the effect of functionalization on such properties is discussed.

Solvolysis of studied structures is examined and it is shown that the highest occupied and lowest unoccupied molecular orbital states shift to obviously higher energy levels, which lead to more stable hydrogenated nanoclusters. The stability of nanoclusters rises by functionalization with amino and methylamine groups. Charge analysis of functionalized systems indicates the reactivity of nanoclusters. The results obtained in this paper are useful for chemical and biochemical applications of silica nanostructures.

Results show that the length of amine hydrocarbon chain can control the electronic and magnetic properties of studied silica nanocluster (SNP) with different number of SiO2 unit. Pure ultra-small nanocluster shows the impressive spin splitting around the Fermi level, which is due to the spin splitting of outer silicon atoms. This feature of silica nanoclusters may be notable for applications in electronics.

Focus group discussions are a well-established method for acquiring insights from experts in different fields. This method requires special amendments when it is used for the development of different tools in the healthcare domain. The objective of this paper is to present the lessons learned from online focus group sessions held for the development of a heuristic usability evaluation tool for mobile health applications.

Two online focus group sessions were conducted with the participation of ten medical informatics experts to develop the tool. The sessions were recorded using screen recording software. The comments provided by the experts were categorized, and the lessons learned from these sessions were identified and reported.

The experiences achieved from the online focus group sessions were categorized into the following ten lessons: 1) Engage the participants fully in online session discussions; 2) Use an appropriate and interesting format; 3) Select an appropriate number of people for online sessions; 4) Invite people having the closest expertise related to the research topic; 5) Employ a technical support technician in addition to the coordinator; 6) Prevent the emergence of a new topic in sessions; 7) Arrange the required hardware and software facilities before the session; 8) Prepare the content in an appropriate language; 9) Use online tools to schedule sessions; 10) Use screen-recording software.

This paper reports the lessons learned from holding online focus group sessions in the process of developing a heuristic usability evaluation tool for mobile health applications. Although these lessons were learned in a study focusing on the development of a usability tool, they can also be used to improve the results of focus group sessions in other fields of medical informatics.

The potential for Information and Communication Technology (ICT) in healthcare is immense, revolutionizing the delivery of medical services and improving patient outcomes. ICT efficiently manages health information, facilitating electronic health records (EHRs) and streamlined communication among healthcare professionals, leading to significant changes, especially in underserved areas.

This cross-sectional study took place between March and April 2023 among healthcare professionals in Kericho County, Kenya. Participants were selected using simple random sampling and completed a self-administered questionnaire. Data on the ICT status of health facilities were collected using a checklist. The qualitative component involved key informant interviews with a health record and information department officer. Collected data were entered into Excel and analysed using R software for quantitative data and thematic analysis for qualitative data.

The study engaged 201 participants. Findings showed a 67.66% [95% CI=0.607, 0.741]; p-value<0.0001, uptake of ICT among healthcare workers. Those with computer training were approximately 10 times [OR = 10.867, 95% CI=3.121, 40.99] more likely to utilize ICT in service delivery than those without IT training. Operating at least one healthcare database was associated with over 2 times [OR=2.552, 95% CI=0.7475, 8.7195] higher likelihood of ICT uptake compared to those without this skill. Health facilities with eHealth platforms showed, on average, 38% higher [OR=1.386, 95% CI=0.7661, 2.223] utilization of ICT than those without.

IT training for personnel is crucial, ensuring they can operate preferred health management and information systems (HMIS) within the sector. The presence of an IT department and the use of ICT for administrative purposes significantly affected the general uptake of ICT in health facilities. Additionally, infrastructure such as roads, power, and security had a significant association with ICT compliance. Improving these supportive elements will considerably enhance ICT uptake in healthcare.

Substance abuse has been recognized as a national problem in Iran, in which poisoning with pharmaceutical drugs, opioids and alcohols is common. Due to the lack of a comprehensive information system related to Substance abuse, it is challenging to manage the treatment and follow-up of patients caused by drug abuse poisoning. The purpose of this research was to develop a registry system for substance abuse poisoning in - Urmia University of Medical Sciences.

This research is a practical development study that was done in 4 phases. In the first phase, minimum data set (MDS) of the system were determined. In the second phase, the registration system was designed. The third phase includes the implementation of the system, and finally, the system was evaluated by QUIS questionnaire.

A total of 58 data elements in 6 classes were recognized as essential for this system from the point of view of experts. The system was implemented on the ASP.NET platform using C# language and SQL Server database in the poisoning department of Taleghani Hospital in Urmia. The evaluation of usability of the system showed score obtained in the 6 main categories were in set of terms of the system 8.52, screen 8.36, ability to learn 8.8, general functionality 8.04, user interface 7.98, and the general interaction is 7.73 respectively.

Considering the capabilities of the system for registering poisonings caused by drug abuse, it seems necessary to implement this system in the form of a national network, in order to make the necessary interventions to control drug abuse while using resources efficiently.

As our population ages, the need for specialized medical care for older adults is becoming increasingly important. At the same time, advances in information technology are revolutionizing the way we approach healthcare, providing opportunities for improved diagnosis, treatment, and patient outcomes.

Colorectal cancer is one of the most common gastrointestinal cancers, and it is the third and fourth most common cancer among Iranian men and women, respectively. Patients suffering from cancer have different information needs, and one of the most important and reliable sources of information for them is their physicians. Therefore, this study aimed to assess the sources and information needs of patients suffering from colorectal cancer and physicians' viewpoints regarding this issue.

This cross-sectional questionnaire survey-based study was conducted from May to December 2017. All patients diagnosed with colorectal cancer and physicians in oncology outpatient clinics at a specialized cancer hospital and a radiotherapy oncology center in Mashhad, Iran were invited to participate in the study using the census technique. The patient questionnaire was about patients' attitudes toward the consultant's information and disease, information leaflets, the treatments and complications and information sources for the disease. The physician questionnaire was about the information the physician would give to the patients.

The mean ages of the patients and physicians participating in this study were 50.72±16.15 and 40.03±11.08 years, respectively. Most of the cases (44.8%) wanted to know everything about their illness as much as possible, while 45.5% of the patients needed the information. The majority (85.9%) of the participants were willing to know about all possible therapies regarding their illness, while 63.3% of the physicians provided treatment options that were suitable for the patient in their view, and only 33.3% of the physicians told the patients all possible treatment options.

Information regarding the illness, diagnosis, and treatment is one of the essential needs for patients suffering from colorectal cancer. It seems that it is necessary to have a good and organized plan to provide the patients suffering from colorectal cancer with the required information and increase their health information literacy as one of their undeniable rights.

Real-time variations in brain activity are determined by electroencephalogram (EEG) data. EEG signals are commonly used in studies to analyze human emotional states. Emotions EEG signals vary from person to person because they each have different emotional responses to the same stimuli. The objective of this study was using EEG signals in emotion recognition.

We specifically focused on employing convolutional neural network (CNN) for detecting image-based emotions in long-term EEG data. After filtering, the EEG data is divided into short sections based on a certain time window and they are converted into EEG plot images. Each of these is classified by convolutional neural networks.

In comparison with the existing methods, the error rate has been reduced and the accuracy rate is better than the existing methods. The mean accuracy of the compared articles is 62.87, 70.50, 74.88, 82.88 and 68.11, but the average accuracy of the proposed method is 85.13.

This research demonstrates the potential and accuracy of CNN in recognizing emotions from scalp EEG plot images. The study contributes to the growing field of emotion recognition and paves the way for future advancements in utilizing CNN for analyzing EEG signals, ultimately aiming to use as an effective method for computer-aided recognition.

Mucormycosis, the third most prevalent invasive fungal disease ranked, has a very high mortality rate. The timely diagnosis, coupled with the prompt administration of drug and surgical interventions, yields a substantial reduction in mortality rates. To accomplish these objectives, the acquisition of accurate data and information assumes paramount importance. A minimum data set serves as a crucial tool for data collection, offering healthcare managers a standardized information resource. Consequently, the aim of this study was to develop a MDS to mucormycosis in Iran.

This research was conducted using a practical approach, employing a two-step Delphi method. An extensive literature review was conducted aimed at extracting relevant information specific to mucormycosis. two individual checklists were developed. Following these checklists underwent rigorous evaluation through the Delphi, involving the 20 experts, consisting of five specialists each in the fields of infectious diseases, dermatology, otolaryngology, and health information management.

Experts thoroughly examined a total of 72 out of 86 items on the demographic information checklist throughout both the initial and second phases of the Delphi process. Furthermore, within the clinical information checklist, experts meticulously evaluated 303 out of 323 data elements during the first and second phases of the Delphi process. The demographic checklist was structured into four categories basic information, demographic information, insurance information, and referral information. The clinical checklist consisted of seven categories, encompassing risk factors, types of mucormycosis disease, clinical signs and symptoms, diagnosis, species, treatment, and outcomes.

In recent years, the prevalence of underlying diseases has witnessed an upward trend, resulting in a subsequent escalation in the number of mucormycosis patients. In light of the burgeoning incidence of mucormycosis cases in Iran, it becomes imperative to establish a standardized MDS for this disease.

Chronic pain is a significant clinical problem in the world. There is not quite effective treatment for chronic pain due to its complex nature. However, timely retrieval of accurate and comprehensive information through organized clinical and epidemiological studies is an essential prerequisite for providing high-quality clinical care and more accurate health care planning. This can be achieved by the creation of an electronic registry system as a strong source of information. The purpose of this study was to develop and evaluate a chronic pain registry for patients with chronic pain syndromes.

In this study, Onion architecture with the MVC design pattern was selected in design phase. Using onion architecture leads to more flexible and reusable codes and results in easier development and maintenance. In the development phase, MYSQL DBMS and the PHP programming language, which are suitable for developing the web-based system, were used.

The minimum data set was determined in the previous study. This dataset covered six areas: demographic information, initial pain assessment, medical history, mental health and well-being, diagnostic measures, and diagnosis and treatment plan. A web-based pain registry system was developed based on the minimum data set.

There are many studies for development of web- based pain registries in the world but there is a few information about technical architecture and structure in design phase. In this study, we focused on the technical architecture design of system. Using onion architecture leads to more flexible and reusable codes and results in easier development and maintenance. In the current study, it was chosen to use MYSQL and the PHP programming language, which is suitable for developing the web-based system. Finally, a web-based registry system was developed to store and report on the information of patients suffering from chronic pain. It can manage and control chronic pain and facilitate future research.

Online and social network-based training and clinical interventions during pregnancy and immediately after pregnancy can be beneficial for women, empowering them in their role as a mother. Nowadays, due to the spread of the COVID-19 accompanied by a reduction in the number of referrals for maternity care, there has been a higher incidence of urinary infections among the pregnant women. Taking these into account, the present study aims to shed light on the effect of the WhatsApp-based learning on awareness, attitude, and self-efficacy in relation to UTIs among pregnant women compared to the pamphlet-based learning.

This semi-experimental study was conducted on 96 pregnant women at 30 weeks and less than 30 weeks of pregnancy who referred to the government Obstetrics and Gynecology Clinics in Isfahan city in 2021. The participants were simple randomly assigned to one of three independent groups, namely two intervention groups and one control group. Intervention took 6 weeks and the data were collected both before and after the intervention using a standardized questionnaire. SPSS software V21.0 and non-parametric statistical tests, including Wilcoxon, Kruskal-Wallis, and Mann-Whitney were used for data analysis.

The results showed that the mean score of the awareness before and after intervention increased from 11 to 26 and from 11 to 15 (X2=36.00, p<0.001) in the WhatsApp-based and the pamphlet-based learning group, respectively. Furthermore, the mean score of the attitude before and after intervention increased from 54 to 82 in the WhatsApp-based and from 53 to 58 (X2=60.00, p<0.001) in the pamphlet-based learning group. For self-efficacy, the mean score before and after intervention changed from 47 to 71 in WhatsApp-based and from 46 to 51 (X2=62.00, p<0.001) in the pamphlet-based learning group. In the control group, there is no difference in the mean score of the awareness, attitude and self-efficacy before and after the intervention.

Online training and clinical interventions during pregnancy can be beneficial for women. In fact, sharing health information among pregnant women through social networks is related to better pregnancy management. It also increased their awareness, attitude and self-efficacy.

Recently developed mobile apps for controlling COVID-19 have the potential to help fight the pandemic. But assurance regarding the quality of available apps is essential to proving their validity for usage. This study was aimed at evaluating and ranking the apps in Persian developed for COVID-19 in Iran.

122 apps for COVID-19 in the Persian language were founded in the Miket, CafeBazar, ParsHub, and Charkhooneh app markets. Based on inclusion criteria, 13 apps were selected. The apps were evaluated by two independent reviewers and ranked according to a validated evaluation and ranking tool specifically for the Persian apps for information content, usability, design, ethics, security, privacy, and subjective quality. Kendall’s coefficient of concordance was used to calculate the agreement between two raters based on the mean of their scores for each app (p-value<0.05).

Five functional and subjective quality criteria were used. Mask was the app with the highest level of the specific score (mean score: 4.10, subjective quality: 4). The Corona test-Davoudi was the app with the lowest level of the specific score (mean score: 1.85, subjective quality: 1.50), which needs more improvement. The reviewed apps mainly need improvement for data security and privacy, requiring more technical tasks.

There is a need for improvement, particularly in terms of privacy and data security, for Persian COVID-19 apps. Develop a valid guideline that could be effective in improving app quality. In addition, the modern technologies that have already proven successful worldwide should be considered by mobile app developers.

Accurate diagnosis is crucial for brain tumors, given their low survival rates and high treatment costs. However, traditional methods relying on manual interpretation of medical images are time-consuming and prone to errors. Attention-based deep learning, utilizing deep neural networks to selectively focus on relevant features, offers a promising solution.

This paper presents an overview of recent advancements in attention-based deep learning for brain tumor image analysis. While the reviewed models have demonstrated respectable performance across different datasets, they have yet to achieve state-of-the-art results.

Advanced techniques, including super-resolution image reconstruction, multi-swin-transformer blocks, and spatial group-wise enhanced attention blocks, have shown improved segmentation network performance. Integration of graph attention, swin-transformer, and gradient awareness minimization with positional attention convolution blocks, self-attention blocks, and intermittent fully connected layers has considerably enhanced the efficiency of classification networks.

While attention-based deep learning has shown improvements in performance, challenges persist. These challenges include the requirement for large datasets, resource limitations, accurate segmentation of irregularly shaped tumors, and high computational demands. Future studies should address these challenges to further enhance the efficiency of brain tumor diagnoses in real-world settings.

In light of the global spread of COVID-19 and its profound impact on public health and casualties, nurses have been thrust onto the front lines in the battle against this disease, resulting in heightened psychological distress and anxiety. Addressing these issues promptly and effectively is crucial during these challenging times. Therefore, this study aims to investigate the impact of telenursing training in reducing death anxiety among nurses with a history of COVID-19.

This quasi-experimental study involved two groups of 20 nurses with a history of COVID-19 and higher levels of death anxiety. Data were collected using Templer's death anxiety questionnaire and a demographic information questionnaire. In the test group, the intervention was conducted through WhatsApp groups over five sessions. Training methods to reduce death anxiety were presented through explanatory text, PowerPoint presentations, and audio files, with five-day intervals between sessions. The control group did not receive any intervention. Twenty days after the sessions, both groups completed the death anxiety questionnaire again. Data were analyzed using t-tests and chi-square tests.

The findings indicated a significant difference in the average score of death anxiety between the test and control group after telenursing training (p<0.05).

Telenursing training effectively reduces death anxiety among nurses with a history of COVID-19. Telenursing proves to be a cost-effective and organized intervention for managing symptoms, early diagnosis of complications, ensuring post-care quality, exchanging information, and providing health education.

Colorectal cancer remains a significant health challenge, particularly in its advanced Stage III. Timely forecasting of recurrence and metastasis in these patients is crucial for optimizing postoperative care and treatment strategies. The aim of this study is to predict the likelihood of recurrence and metastasis in stage III colorectal cancer patients who have undergone laparoscopic surgery and laparotomy.

In this retrospective analysis, a total of 528 patients with Stage III colorectal cancer were included. Among them, 386 underwent laparoscopy, and 142 underwent laparotomies. logistic regression was employed to assess the influence of the surgical approach on the binary outcomes of recurrence and metastasis. The data were analyzed using SPSS 25, and Odds Ratios along with significance testing were performed with a threshold of p < 0.05 to determine statistical significance.

In the laparoscopy group, the recurrence rate was 23.7%, and although older patients (61-98 years) exhibited a higher risk of recurrence (Odds Ratio:1.88, 95% CI:0.92-3.84, p=0.083), this difference did not reach statistical significance. Gender did not significantly impact recurrence. In the laparotomy group, the recurrence rate was 29.6%, and neither age nor gender had a significant influence on recurrence. Notably, in the laparoscopy group, metastasis was significantly associated with age (Odds Ratio:5.044, 95% CI:2.08-12.23, p=0.001), while gender did not play a significant role in metastasis. Similarly, in the laparotomy group, neither age nor gender significantly affected metastasis.

This study underscores age's influence on recurrence and metastasis rates in laparoscopic treatment for stage III colorectal cancer, highlighting the need for tailored approaches in elderly patients. In contrast, laparotomy seems to be less affected by age, with tumor size emerging as a crucial predictor of disease progression. Surgical approach significantly impacts outcomes in stage III colorectal cancer, with age affecting laparoscopy outcomes more than laparotomy. These findings emphasize the importance of personalized treatments and call for further research to validate results and enhance patient outcomes in advanced colorectal cancer.

Kidney stones and its treatment, control of possible complications after lithotripsy and prevention of kidney stones recurrence, are the most substantial concerns of patients and physicians. Nowadays, the high capabilities of mobile technologies have become especially important for the prevention and control of diseases and patients' self-care. Use of mobile technologies, applications and smart tools can be a good way to prevent and control kidney stones. The present study was designed to identify the data requirements to design a self-care application for patients with kidney stones undergoing extracorporeal lithotripsy.

This paper is a descriptive study and was performed in Sabalan Hospital in Ardabil affiliated to the Social Security Organization of Iran. A needs assessment questionnaire was designed by reviewing the scientific sources, databases, guidelines and medical records of patients. Ten clinical and technical specialists surveyed the questionnaire.

Four main characteristics of demographic data, clinical data, disease monitoring and application capabilities were identified. Familiarity with kidney stone disease, familiarity with treatment methods, familiarity with diet, reminders, interaction with the treating physician were the most important capabilities identified for the application.

The findings of the survey showed that all identified items and components scored above average. The identified components were approved by experts and considered necessary.

In the digital age, since the application of artificial intelligence (AI) is increasingly penetrating the world, the cultivation of AI literacy has become increasingly important for everyone. This systematic review investigated the level of AI literacy among healthcare professionals and students.

We searched the databases PubMed, Embase, Scopus, and Web of Science for relevant material. The evidence gathered from the studies included in this systematic review was reported in this study using preferred reporting items for systematic reviews and meta-analyses (PRISMA). Studies that assessed the level of AI literacy among medical and healthcare professionals and students met the inclusion criteria for this study. The quality of the included study for this review was assessed using the analytical cross-sectional critical assessment checklist developed by the Joanna Briggs Institute (JBI). The same standard checklist was used for data extraction.

Of the 10 included studies, 4 (40%) reported a low level of preparation, knowledge, and literacy. In a study, it was also shown that radiologists had acceptable literacy about AI, and it seems that they had a better study of this field compared to other specialists. Another study showed that initially the level of AI literacy was not acceptable but improved significantly after training. Two studies also hailed AI's contribution to improving healthcare.

Evidence from this review indicated that half of the studies on the AI literacy of professionals and students were very low, and other included studies also reported the basic literacy of AI acceptably. Finally, in all included studies, AI training courses and their application in healthcare were considered necessary for professionals and students, and they were trying to improve the educational infrastructure.

In the pursuit of improving effective health service delivery, developing nations including Ghana are progressively integrating electronic health record systems into healthcare frameworks. This research explored the proficient utilization of an EHR called the Lightwave Health Information Management System used by healthcare professionals in Ghana.

A descriptive cross-sectional study design and a multi-stage sampling technique (stratified and simple random sampling) were used to recruit 1126 respondents for this study. Weighted averages were computed to determine scores for all the indicators measuring the effectiveness construct.

The study found that LHIMS improved productivity, patient data gathering, sharing of patient information among service providers, care continuity, data exchange among facilities, decision-making, and coordination/organization of care. Also, health professionals’ work experience, educational qualification, and training status were statistically significant predictors of effective use of the LHIMS at the multivariate level. Age and professional type were statistically significant only at the bivariate level.

The study concludes that incorporating the LHIMS improves healthcare professionals' effectiveness in gathering patient information while reducing the likelihood of errors by promptly notifying them of any inaccuracies. The study emphasized the importance of training for effectively using the LHIMS.

Rheumatoid arthritis is a systemic, chronic autoimmune disease that affects the joints, and limited mobility. The disease is progressive and can significantly impact a patient's quality of life. Today, mobile applications have the potential to address specific health needs and provide therapeutic interventions. The initial stage of constructing and advancing a healthcare information system involves the utilization of a minimum data set, which comprises essential and standardized data components aimed at capturing and overseeing patient care. This study aims to identify key components for a mobile-based self-care application for patients with rheumatoid arthritis.

In this descriptive analytical study, two steps were undertaken. Firstly, a review of related articles and existing apps was conducted. Secondly, a researcher-developed questionnaire with a high reliability coefficient (Cronbach's alpha=0.97) was used to validate the identified information elements. Elements that scored at least an average of 3.2 (60%) on a 5 point Likert scale were deemed necessary data components for the design of an android-based mobile app catering to the self-care needs of rheumatoid arthritis patients.

Based on the analysis findings, experts identified crucial technical requirements for a mobile-based self-management system. The system should include features for documenting drug side effects and providing educational content and physical exercise videos. Additionally, these requirements encompass reminders for medication, doctor appointments, and physical activities. Priorities also include clinical information, lifestyle management, and patient demographics.

Overall, the implementation of such a system has the potential to enhance patients' self-management skills, promote active involvement in self-care, and facilitate communication with healthcare providers.

A minimum data set improves the potential of data standardization and overcoming the problem of low-quality speech therapy data by providing coherent, complete, and uniform data elements. Therefore, this study was conducted to compare speech therapy minimum data set among different countries.

A systematic review was conducted without time limits in PubMed, Scopus, Web of Science, Embase, SID, Magiran, Elmnet databases, and in the Google search engine to retrieve articles, speech therapy forms, and speech therapy registry sites. Keywords related to speech therapy minimum data set including minimum data set, registry, and speech therapy, were used. First, studies were reviewed based on titles and abstracts. Then, the selected studies from the previous stage were examined independently by two researchers. A similar standard checklist was used to extract and compare the findings.

A total of 1710 related records were extracted for review, and finally, six main articles and 11 forms were included in this review. The six original articles included two related to speech therapy minimum data set in the United States, two related to Iran, and one related to Australia and Germany. A comparative review of the most important data elements obtained from the articles and input forms in this review, including identity and admission information, referral information, history, assessment of verbal skills, assessment of non-verbal skills, assessment of organs of production, assessment of cognitive skills, assessment of other aspects of speech, and linguistic and cultural considerations, were information elements related to diagnoses, recommendations, and treatment plans.

It could be concluded that an agreed classification system is needed to facilitate communication between speech therapists. This potentially enables further testing of diagnostic and therapeutic hypotheses with more coherent and simultaneous data collection. The challenge ahead is to create a comprehensive and universally agreed-upon classification system that meets the needs of professionals and researchers.

The emergency department is amongst the most important parts of the hospital, which has a great impact on the performance of other departments. In this regard, Emergency Department Information System (EDIS) plays a vital role in providing effective and appropriate healthcare services.

To evaluate the user-perceived usability of admission and discharge sections of the Emergency Department Information System of Shohada-e-Tajrish Educational Hospital, the study is carried out utilizing SUS and PSSUQ scales. Research participants include all users in the admission and discharge sections of the emergency department of the Hospital.

The mean score of the system usability based on the results of the SUS scale was 49.62±23.23, which is lower than the acceptable level (score above 68). The results generated by the PSSUQ tool revealed that the information quality (4.79), interface quality (4.91), and system usefulness (4.42) of admission and discharge sections of the EDIS do not have appropriate scores and need to be improved in this regard. . The worst usability score was related to system usefulness There was no statistically significant correlation between the age of participants and usability scores. Moreover, there was no statistically significant difference between the user-perceived usability and the education level of participants (p>0.05).

The study revealed that users are not satisfied with system usability. In the event that the usability of the system is suboptimal, the user fails to effectively utilize the system, thereby impeding the attainment of the intended ultimate objective for which the system has been designed. Regarding the importance of admission and discharge sections in the EDIS, strides need to be made to improve the usability of the system which is used at the emergency department of Shohada-e-Tajrish educational hospital.

According to the research conducted in recent years and after the evolution of social networks, the statistics of obesity and the consumption of fast foods and high-calorie foods have increased significantly. Also, food blogging has become much more widespread and popular in the space of social networks, so we are investigating the impact of social networks on people's nutritional habits.

The purpose of this research is to examine the relationship and impact of social networks on people's nutritional habits. For this research, a 20-question questionnaire was answered by 60 college students and the obtained data was analyzed by SPSS 26.

The most social media has been used are Instagram, Telegram, and YouTube, and 22% use social media between 3 and 4 hours a day. More than 58% used ready food less than 2 days a week. But 56% of them reported that social media didn’t have a noticeable effect on their informed decision in choosing food.

This study showed that the use of social media can be effective in people's lifestyle and food pattern and food intake.

Managing resources is one of the most important challenges that healthcare providers worldwide face during the COVID-19 pandemic. In recent years, machine learning has been developed to provide valuable help in predicting disease and estimating the duration of their stay. This study aimed to identify the machine learning models for predicting length of stay in COVID-19.

Online databases, including Scopus, PubMed, Web of Science, and Science Direct, were searched, and a hand search through Google Scholar and grey literature was done up to August 2023 and updated in December 2023 to identify articles to find all relevant studies. To manage the process and check the quality of included articles PRISMA guidelines and CASP checklist were used and data was extracted using a data extraction form.

Among all 489 research articles, 10 studies met the inclusion criteria. The best models reported in the included articles were random forest (n=3), gradient boosting (n=2), XGBoost (n=2), SVM (n=1), KNN (n=1), and DataRobot (n=1). Except one of the studies that used quantitative modeling and reported MSE and MAE as evaluation criteria, other studies used qualitative modeling and reported accuracy, specificity, and F1-score. The focus of the included articles was on the general and ICU departments as the important resources in the hospital and emphasized the use of machine learning to predict the length of stay.

The results of this systematic review showed that a data mining approach and using a machine learning algorithm can help to manage the critical resources of the hospital especially when we are faced with a pandemic disease like COVID-19.