Frontiers in Health Informatics
Volume:13 Issue: 1, 2024

The lack of integrated registration of information causes additional burden, inefficiency in health and treatment systems, and finally increases cost and decreases effectiveness. Correct coding requires knowledge and use of the latest editions of the International classification of functioning, disability and health (ICF). This study aimed to investigate the level of knowledge and use of ICF among professors and rehabilitation and optometry specialists at Mashhad university of medical sciences.

This cross-sectional study was conducted from September 2022 to March 2023. First, the pre-designed questionnaire consists of ten multiple-choice questions that were distributed among professors and rehabilitation and optometry specialists at Mashhad university of medical sciences. Second, the data were analyzed. For this purpose, descriptive data was calculated. SPSS version 21 statistical software was used to analyze the data.

The majority of the participants 16 (59.3%) stated that they knew ICF. However, only 13 (48.1%) of them identified the name or abbreviation of ICF correctly, and only 3 of them (11.1%) recognized the ICF components correctly. Fifteen of the participants (55.6%) stated that they did not use the ICF, followed by 3 (11.1%) who stated that they used the ICF in the clinic and 3 (11.1%) in training. The relationship between participants' graduation degree and their familiarity with ICF was significant (p=0.02).

The results of this study show that most of the participants did not have enough information about the ICF and its components. A lack of knowledge about the ICF prevents professionals from following the recommendations of the world health organization. As the medical industry is driven to use evidence in patient care, universities, and educational centers must also provide the necessary grounds and infrastructure for teaching and using international classification tools such as ICF, which will be used by healthcare professionals in the future.

Previous experiences have shown that mental health is one of the areas that is quickly affected during an epidemic. Students, in particular, require guidance and consultation with psychologists in various aspects of their lives. This study aimed to investigate the satisfaction and effectiveness of tele-psychotherapy among students at Mashhad university of medical sciences during the COVID-19 pandemic.

This cross-sectional study was conducted from September 2022 to March 2023. A standard questionnaire developed by Ghalibaf et al. was used. Three experts in related fields confirmed the content validity of the questionnaire, and its internal consistency was assessed using Cronbach's alpha coefficient. Questionnaires were distributed to all students from affiliated faculties at Mashhad university of medical sciences who had utilized tele-psychotherapy during the COVID-19 pandemic. Descriptive statistics were used to summarize and analyze the data, while analytical statistics provided a basis for inference and interpretation.

The majority of students reported engaging in informal counseling through virtual communication methods such as chat and voice (47.7%), while video communication methods like video conferences were less commonly used (0.05%). Overall, most students (50%) expressed moderate overall satisfaction with tele-psychotherapy. However, only a small percentage (16.7%) agreed or strongly agreed that they would continue with this style of counseling in the future. Gender was found to have a significant relationship with satisfaction in tele-psychotherapy (p<0.001), with female students reporting higher levels of satisfaction. Additionally, confidence in the confidentiality of conversations on the Internet also had a significant relationship with satisfaction in tele-psychotherapy (p<0.001).

The COVID-19 pandemic necessitated a sudden shift from face-to-face psychotherapy consultations to tele-psychotherapy for students, presenting them with unique challenges. The results of our study demonstrate that tele-psychotherapy can be a satisfactory option for ensuring continuity during crises. Furthermore, tele-psychotherapy offers flexibility in terms of time and location, allowing for quicker appointments if more frequent sessions are required or during emergencies.

Machine learning, especially deep convolutional neural networks (DCNNs), is a popular method for computerizing medical image analysis. This study aimed to develop DCNN models for histopathology image classification utilizing transfer learning.

We utilized 16 different pre-trained DCNNs to analyze the histopathology images from the animal diagnostic laboratory (ADL) database. During the image preprocessing stage, we applied two methods. The first method involved subtracting the mean of ImageNet images from all images. The second method involved subtracting the mean of histopathology training images from all images. Next, in the 16 pre-trained networks, feature extraction was done from their final six layers, and the features extracted from each layer were fed separately into the linear and non-linear support vector machine (SVM) for classification.

The results obtained from the ADL database show that the classification rate in lung tissue images is much better than that of the kidney and spleen. For example, the lowest detection rate in non-linear SVM for lung tissue is 14.96%, almost close to the highest accuracy in kidney and spleen tissue. The classification accuracy of the spleen images is better than that of the kidneys, with only a slight difference. In linear SVM on lung images, ResNet101 obtained the most accurate result with a value of 99.56%, followed by ResNet50, ResNet152, VGG_16, and VGG_19. In non-linear SVM on lung tissue images, the ResNet101 network with 99.65% and ResNet50 with 99.21%, followed by ResNet152, VGG_16, and VGG_19 obtained the highest detection rate.

The classification results obtained from different methods on the ADL (including kidney, spleen, and lung histopathology images) database, confirmed the validity of transferring knowledge between non-medical and medical histopathology images. Additionally, it demonstrates the success of combining classifiers trained on deep features. This research obtained higher accuracy in the ADL database than the works done.

Mobile-based applications have become increasingly critical for healthcare delivery worldwide over the past few years. Developing a mobile application for hemophilia self-care is one of the tools that can provide helpful information about the disease, reminders, and treatment recommendations. This study aims to determine a minimum data set as the first step in developing a hemophilia self-care mobile application.

This descriptive-analytical study was conducted in 2023 and consisted of three steps. In the first step, relevant databases such as PubMed, Scopus, and Science Direct were reviewed. The data elements collected in the previous step were combined, and their validity was checked and confirmed in the second step. In the last step, all ten hematologists-oncologists at Imam Khomeini hospital complex, affiliated with Tehran university of medical sciences, completed a questionnaire to score the identified elements. Statistical analysis was performed using SPSS (Ver 16).

Based on global guidelines, published research, and specialists' feedback, the minimum data set for hemophilia management was developed. There are four main categories and twenty-three subclasses of identified elements, including demographic data (5 elements), disease management-related data (12 elements), educational data (3 elements), and technical capabilities (3 elements). To determine the importance of each data element, we calculated the percentage points provided by specialists, which were 95.50% for demographic data, 96.45% for disease management-related data, 95.83% for educational data, and 95.83% for technical capabilities.

Due to the lack of hemophilia’s minimum data set at the national level, this study can serve as a turning point toward standardized data collection for this disease. By utilizing these precise, coherent, and standard data elements, hemophilia management and quality of life can be improved.

Heart disease is a major public health concern with millions of reported deaths annually. Data mining techniques have received attention in recent years as a tool aiding diagnosis and prediction of heart disease cases. This systematic review examines the application of data mining methods to cardiac disease diagnosis in order to identify specific types of heart-related disease that are diagnosed using data mining techniques as well as the most successful data mining methods.

This study involved a systematic review of IEEE, Science Direct, Google Scholar, Web of Science, Scopus and MEDLINE databases from 2008 until April 2023. Inclusion criteria were original papers that used data mining methods for heart disease diagnosis. Non-English papers, those without full text, studies conducted on animals, and other types of papers (conference abstracts and letters) were excluded from the study. All the retrieved references were then assessed by title and abstract according to PRISMA, after which full texts of relevant articles were analyzed. The final sample comprised of 47 articles.

Various classification methods have been utilized to diagnose heart-related disease using different mining tools, with genetic neural network data mining method having the highest accuracy among the studied techniques. Results show that predicting cardiac disease is the most commonly performed task. The demographic, bio-clinical, personal and exercise-related attributes, as well as other features used for classification were identified. The findings suggest that data mining methods hold great potential for detecting and preventing heart disease on both individual and population scales.

The study findings have implications for the prevention and treatment of cardiac disease, especially in high-risk individuals. Data mining methods can be widely applied to detect and prevent heart disease on a population scale, as well as supporting decisions for the most suitable treatment for individual patients to prevent death and reduce treatment costs.

Mobile-based self-care applications can help patients with hepatitis increase their awareness about various aspects of the disease. This study aimed to develop and evaluate a self-care mobile app for viral hepatitis.

This study was conducted in three steps. In the first step, a questionnaire containing 24 topics in four sections was used to determine the potential app contents. In the second step, the app was developed using the Android Studio 3 development environment and Kotlin programming language. In the third step, the quality of the app was evaluated using mobile app rating scale (MARS). The Questionnaire for User Interface Satisfaction (QUIS) was used to evaluate user satisfaction.

A high priority was given to the following contents of the medical and health information section; describing ways of transmitting hepatitis (81.7%), dealing with high-risk behaviors (80.6%), and methods of preventing hepatitis (79.6%). The MARS and QUIS evaluations’ results showed that the quality of the app and the user satisfaction with it were at a good level.

Since according to the participants, the topics related to the “medical and health information” section were the most important contents, we recommend addressing this part in designing other self-care apps.

The COVID-19 pandemic has had a profound impact on Iran and numerous other nations, resulting in a surge in infections and fatalities. In response to the COVID-19 crisis, a range of policies and initiatives were enacted, including the deployment of telehealth services. This study aims to outline the requirements for establishing an all-encompassing platform capable of delivering telemedicine services in Iran.

This cross-sectional study was carried out using a researcher-made electronic questionnaire during the period of July to August 2022. All experts in the field of medical informatics and health information systems based in three provinces (Kermanshah, Kerman, and West Azerbaijan) were contacted to fill out the questionnaire, 15 participants completed and returned the questionnaire. Data were analyzed by SPSS using descriptive statistics.

The requirements for the design and implementation of the systems could be divided into internal (technical and infrastructural, security-legal, and environmental), and external categories (technical and infrastructural, and security-legal). The majority of internal and external requirements were related to technical and infrastructure aspects, accounting for 83% and 95%, respectively.

Telemedicine development tools are available to enhance healthcare services in Iran, but there is a need to strengthen the infrastructure and technical equipment to enable the utilization of this technology for improving therapeutic and educational objectives within the healthcare system.

After a cancer diagnosis, the most important thing is to determine the stage and grade of the cancer. Pathology reports are the main source for cancer staging, but they do not contain all the information needed for the staging. However, the text of these reports is sometimes the only available information. We were interested in knowing whether text mining methods can be used to predict staging only from pathology reports.

A total of 698 pathology reports of breast cancer cases and their TNM staging collected from multiple centers in West Azerbaijan Province, Iran were used for this study. After preparing the semi-structured reports, the texts of the reports were imported into a program written by Python V3. Three machine learning algorithms of Logistic Regression, SVM, and Naïve Bayes and a simple pipeline were used for the purpose of text mining. The performance of the algorithms was evaluated in terms of accuracy, precision, recall, and F1 score.

The Naïve Bayes algorithm achieved excellent results and a value rate of higher than 91% in all evaluation criteria (accuracy, precision, recall and F1 score). This means that the Naïve Bayes algorithm could classify the reports with high efficiency and its predictions were more correct than the other two algorithms. Naïve Bayes also outperformed SVM and Logistic Regression in terms of accuracy, recall and F1 score. In addition, Naïve-Bayes showed faster inference due to its simplicity and lower computational and training time.

We suggest using the proposed design in this study for predicting breast cancer staging, where there is a need but not all necessary information except pathology reports. This method may not be a useful for clinical management of cancer patients, but it can be safely used for epidemiological estimations.

In this specific research study, a remarkably accurate and significantly simplified approach has been presented.

This research is encompasses three crucial stages. Firstly, the length of the signal is effectively diminished to an optimal magnitude through the utilization of a technique widely known as windowing. This technique plays a pivotal role in reducing the signal to an ideal size, ensuring the subsequent stages are executed with utmost precision. Secondly, the pertinent features are extracted from the shortened signal, specifically focusing on the Fractal Dimension, the Hurst Exponent, and the Ratio of Determinism to Recurrence Rate. These features are chosen due to the inherent nonlinear nature of the signal, as they provide valuable insights into the complex patterns and structures present within. Lastly, the extracted features undergo Logistic Regression, a widely employed classification algorithm, to effectively categorize and classify them. This step plays a crucial role in providing a clear and concise understanding of the underlying characteristics of the signal.

The implementation of the proposed method not only achieves an outstanding accuracy rate of 99.66%, but it also exhibits a linear time complexity, ensuring efficient processing. Additionally, this method leads to a significant reduction in the length of EEG signals, which is of utmost importance in practical applications.

The primary objective of this proposed method revolves around the introduction of an online approach that can seamlessly integrate into healthcare systems. This objective is derived from a comprehensive analysis and evaluation of the obtained results, ensuring the method's practicality and effectiveness are thoroughly assessed.

The Corona epidemic has aggravated the stressful factors on health care systems; in this case, health care workers suffer from job burnout. It is better to use remote psychotherapy methods and appropriate treatment protocols. n this study, the researchers aimed to investigate the impact of telenursing training on job burnout in nurses who had previously contracted COVID-19.

This research employed a quasi-experimental design with a pre and post-test group comparison. It involved two groups, each consisting of 20 nurses who had experienced COVID-19 and exhibited high levels of job burnout. The data collection tool was the Maslach Burnout questionnaire along with demographic information. Both groups completed these questionnaires before the intervention. The experiment group underwent a telenursing training intervention conducted through WhatsApp, consisting of five sessions at five-day intervals. The training encompassed various teaching methods, such as explanatory text, PowerPoint presentations, and audio files. The control group did not receive any intervention. After 20 days from the completion of the training sessions, both groups retook the job burnout questionnaire.

The independent t-tests showed no significant difference in burnout level and severity between experiment and control groups before the intervention (p>0.05). However, after telenursing training in the experiment group, the average scores for burnout level and severity were significantly different between experiment and control groups (p<0.001), which indicates a positive effect of telenursing training on all dimensions of job burnout, including emotional exhaustion, dysfunction, depersonalization, and job conflict.

Telenursing-based training appears to be an effective method for reducing the intensity and levels of burnout among nurses with a history of COVID-19 infection. This suggests that telenursing training can be a valuable tool to mitigate job burnout in this specific group of healthcare professionals.

As a key custodian of public health, the Vice-Chancellor for Treatment Affairs in medical sciences universities plays a pivotal role in delivering healthcare services to the citizens through electronic service desk. In this study, we conducted an evaluation and ranking of the e-service desks managed by the Vice-Chancellor of Treatment Affairs at Iranian medical sciences universities.

A cross-sectional study conducted in Iran between 2022 and 2023 assessed e-service desks in medical sciences universities. The study included 51 desks categorized by their electronic service methods. Subsequently, e-services were evaluated and scored based on components, which are provided by the national administrative organization for employment. The evaluation components encompassed a) use of an e-service desk to provide services; b) provision of an information package; c) availability of a flowchart outlining the service; d) clarity of the service recipient; and e) electronic collection of user opinions. The total points and the average of each desk were calculated to rank them.

The assessed e-service desks were categorized into three groups based on the method of providing electronic services: service provider, service recipient, and not categorized. After evaluating each e-service desk, the highest and lowest points of e-service desks were 708 and 2. The result indicated that only 31 e-service desks achieved an average score of 50% or above.

It could be concluded that electronic government (e-government) has been partially implemented in the field of treatment throughout a developing country. However, the absence of a unified model to serve as a guideline for designing e-service desks has resulted in an undesirable diversity in the services offered and their delivery methods. It is recommended that medical sciences universities adopt and adhere to a unified model when designing e-service desks for the provision of healthcare services electronically.

The prediction of the survival chance of coronavirus disease 2019 (COVID-19) patients is as important as the early detection of the coronavirus. Since patient mortality, factors may differ by location, this study concentrated on identifying the influential factors and predicting survival for COVID-19 patients using machine learning methods in Fars province, Iran.

The research dataset was extracted in the period January 21, 2020, to September 25, 2020, and contains 25858 hospitalized patients’ records with 51 features. These records were classified into two categories: death (label 1) and survival (label 0). The methodology of this research is CRISP standard. A comparison was made between the efficiency of two deep neural network and random forest algorithms in predicting survival. Modeling steps were done with Python language in the Google Colab environment.

Experimental results demonstrated that the deep neural network algorithm had better performance than random forest with accuracy, precision, recall, F-score, and receiver operating characteristic of 97.2%, 100%, 93.54%, 96.66%, and 97.9%, respectively. Based on the results of the random forest model, history of hypertension, chronic neurological disorders, chronic lung diseases, asthma, chronic kidney disease and, heart disease were the most important risk factors related to death.

Deployment of our proposed model allows medical professionals to exercise greater caution during the treatment of patients who are most likely to die due to their medical conditions.

The remarkable growth of lung cancer and its associated impacts and consequences, along with the substantial costs it imposes on society, has driven the medical community to pursue programs aimed at further examination, prevention, early detection, and diagnosis. In medicine science, timely discovery and diagnosis of diseases can prevent many life-threatening conditions and save people's lives.

This study aims to predict lung cancer using a novel feature selection method integrated with a classifier. Our approach entails a comprehensive four-stage method. Initially, we calculate feature similarities within a lung cancer dataset using the absolute value of the Pearson correlation coefficient, followed by the clustering of initial features using the community detection algorithm called Louvain. Next, we employ techniques to determine the optimal subset of features using the concept of node centrality. Ultimately, lung cancer diagnosis is executed using the selected features, leveraging a classifier.

Comparative analysis reveals that our proposed method outperforms existing techniques in terms of reduced execution time and improved prediction accuracy. When compared with established methods, our approach demonstrates superior outcomes in terms of the number of selected features and classification accuracy. Our method reduced 12600 features to 118 features and its accuracy was 95.28, 95.49, 95.23 and 95.32 for Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB) and K-Nearest Neighbor (KNN) classifier. The comparison of runtime shows that the proposed method is significantly improved with a runtime of 2.146 seconds compared to other methods.

The proposed feature selection method successfully reduced the initial feature set and significantly decreased computational time. Moreover, the achieved prediction accuracies underscore the reliability of our approach. This significant reduction in feature space while maintaining consistently high prediction accuracies serves as a strong validation of the potency and practical applicability of our methodology in the domain of lung cancer prediction. These compelling results strongly advocate for the potential real-world impact of our approach.

Disability arising from amputation is intricately shaped by both social factors and rehabilitative care. The efficacy of veterans' self-care emerges as a pivotal factor in effectively managing, controlling, and reducing complications, thereby augmenting and enriching their overall quality of life. This research delves into the creation, execution, and assessment of a comprehensive self-care software tailored for amputees, with a focus on harnessing the practical utility of smartphones and their manifest capabilities in the realm of healthcare.

In 2023, an applied developmental study was conducted, encompassing the evaluation, design, development, implementation, and assessment of a mobile application dedicated to the self-care management of veterans with amputations. The mobile application's conception unfolded within the Android Studio environment, utilizing the Java programming language within the Android operating system. A user interface satisfaction questionnaire was used to gauge the app's usability, with feedback from 20 veterans experiencing amputations.

The needs assessment for a comprehensive self-care software tailored for amputation veterans identified requisites across four distinct sectors. Building upon these insights, a holistic self-care software solution was meticulously designed. Evaluating usability and user satisfaction revealed that veterans rated the app at a "good" level, with an average score of 7.88±1.03 (out of 9).

The mobile application proved apt in content, functionality, and quality, presenting a valuable tool for enhancing the lifestyle, education, and self-care practices of veterans with amputations. This conclusion stems from a thorough usability evaluation from the end-users' perspective.

Smartphone addiction has increased in recent years, especially with the onset of COVID-19 among students. It is possible that as the level of eHealth literacy increases among students, their addiction to smartphones decreases. This study aims to investigate this hypothesis.

This cross-sectional study was conducted on 390 medical sciences students. Two standard questionnaires were used to gather data. The first questionnaire was the Smartphone Addiction Inventory Scale, and the second questionnaire was the eHealth Literacy Scale. Data were analyzed using descriptive and analytic statistics.

There was no significant relationship between the gender of the participants and the mean scores of smartphone addiction or eHealth literacy. However, the relationship between the age of the participants and the mean scores of smartphone addiction or eHealth literacy was significant. Only the relationship between the educational level of the participants and the mean scores of smartphone addiction was significant. The correlation between smartphone addiction and eHealth literacy in students was not significant.

Age and educational level were significant factors influencing smartphone addiction. To decrease smartphone addiction and increase eHealth literacy, educational programs should be implemented for medical science students, who play a crucial role as future guardians of health.

There is a close relationship between mental health and psychosocial problems in disaster settings, as well as overlap in the support provided for these problems. Therefore, public health officials need to understand the burden of behavioral health conditions among survivors and the needs of the affected community. This study aimed to develop a mental health minimum data set for an electronic disaster registry system to provide timely, essential, and accurate information to personnel on the ground and policymakers to design a disaster response and develop an action plan rapidly.

The present study is a mixed‑method (sequential exploratory) study. In the qualitative phase, a literature review and semi‑structured interviews with experts were conducted to generate an item pool for the mental health response in disasters. In the quantitative phase the quantitative content validity, content validity ratio and content validity index were used.

proposed data elements, 85 data elements were confirmed according to the opinion of experts and categorized into two main parts, pre-disaster part with three sections; including region profile, mental health local background, regional mental health committee affairs, and post-disaster parts with five sections including disaster information, information of mental health teams, mental health status, mental health interventions, and mental health need assessment.

Collecting this minimum data set is critical for helping policymakers and healthcare providers prevent, control, and manage the mental health impacts of disasters during the response phase. Besides facilitating and promoting disaster prevention and response programs and measures.

Online health information is one of the most important and widely used sources of information. Currently, a significant number of individuals use the internet for health-related information to learn about health, disease, health promotion, and threats to their health.

This research examined the top 15 health websites based on their popularity in eBizMBA and Alexa rank from January to February 2022. A total of 30 health websites (15 from each category) were evaluated in terms of credibility by the use of Journal of the American Medical Association (JAMA) and health on the net foundation (HON) code. Also, the readability of the mentioned websites was evaluated by the use of four readability tools.

Most of the websites ranked by both Alexa and eBizMBA met the “Authority” and” Complementarity" criteria. When it comes to the readability of the 30 most visited health websites according to eBizMBA and Alexa analytics in the world, the analysis demonstrated that the readability of highly-ranked health websites was higher than the recommended level.

Some of the websites had deficiencies according to the HONcode and JAMA criteria, and the average readability of the websites did not meet the gold standard. Despite the increasing use of the internet for medical information, these resources' poor quality and readability remain a barrier to informed decision-making of patients.

This study reviews the several metrics used to evaluate the performance of appointment scheduling systems.

The articles in English were searched using PubMed, Scopus, and Web of Science databases and Google scholar search engine until July 23, 2023. We used queueing theory to classify evaluation metrics.

Out of 23403 articles, 75 papers were prepared for detailed analysis. We classify evaluation metrics of appointment scheduling system along with their definition and frequency of use. A total of 24 measures containing twelve (%50), seven (%29), and five (%21) were related to the categories of arrivals (patient), queue (at clinic), and server (physician) were found, respectively.

To the best of our knowledge, this paper was one of the first studies collecting and classifying all evaluation metrics of appointment scheduling system in order to help other researchers. Most metrics pertained to patients which may highlight the importance of the patient’s perspective in evaluating appointment scheduling systems.

For the first time in Iran, a kidney stone clinical information system (CIS) has been designed to manage and calculate and visualize patients’ kidney stone risk profiles. Nevertheless, the usability of this system has not been evaluated yet. Medically, this study aims to evaluate the user-perceived usability of the kidney stone CIS. Technically, the current study aims to determine which user-perceived usability testing approach produces the most informative results about the usability of this system.

Three questionnaires, including system usability scale (SUS), software usability measurement inventory (SUMI), and post-study system usability questionnaire, were applied to carry out the study. A total of 15 users of the kidney stone CIS participated.

The findings revealed that the system is of medium usability. Moreover, of the three methods used, the SUMI echoes the comments of the end-users. Despite the medium usability of the system, it was comprehensive in terms of proper data collection and storage, as well as reporting. The interface design, the lack of appropriate guidance, the time-consuming data entry, and the slow reporting system were aspects needed improvement.

Using a combination of tools is recommended for usability evaluation. Since there is much space in the Global and each sub-scale, whereby measures may improve with SUMI, this research recommends its use for future evaluation of the kidney stone CIS.

Electronic health records are critical for diagnosis and treatment, but their development can be challenging and time-consuming process. Personal health records provide a practical and accessible solution by empowering patients to take an active role in their care. This study presents a personalized electronic health record system for chronic kidney disease patients, highlighting its potential benefits on patient care.

In 2021, a practical study was conducted to develop and evaluate a personal health records software specifically for patients with chronic kidney disease. The analysis phase involved a literature review and validation process to review the dataset and key software functions. The study primarily focuses on the implementation process and exploratory usability evaluation results of the software. The development and evaluation process involved a health information management specialist and a medical informatics expert, and the software was evaluated by health information technology students using the Nielsen usability evaluation checklist.

In the evaluation of the produced software, 12 issues were identified, of which three were related to the principle of user freedom and mastery over the system, three were related to the principle of consistency and standards, one was related to the principle of aiding users in diagnosis, and five were related to error prevention.

This study emphasizes the importance of compliance with clinical standards and user experience in developing successful personal health record software. Previous failures in similar systems have been attributed to non-compliance and unmet user expectations. The study aims to identify obstacles and improve the personal health records production process.

Lung cancer, a highly prevalent disease worldwide, poses a significant risk to individuals. Nodules, which manifest as minuscule masses in the lungs, serve as crucial indicators of the early stages of the disease, with the possibility of being either benign or malignant in nature. Prompt diagnosis of this ailment plays a pivotal role in saving patients' lives, thus rendering the utilization of computed aided diagnosis methods exceedingly valuable within this domain.

The methodology employed for presentation purposes is deeply rooted in the principles of deep learning, a field that epitomizes the amalgamation of artificial intelligence and neuroscience. Delving into the specifics, the initial phase of this process entails the preprocessing of data, wherein the lung area is meticulously isolated from computed tomography (CT) scan images. Subsequently, in the second stage, the identification of nodules is facilitated through the employment of the mask region convolutional neural network (RCNN) technique, which effectively entails the delineation of masks and bounding boxes. The third and final step involves the classification of the identified nodules, achieved through the utilization of a singular convolutional neural network, ultimately segregating the nodules into three distinct categories: benign, malignant, and ambiguous. In order to evaluate the efficacy of the proposed method, the LIDC-IDRI dataset was employed as a means of testing, thereby furnishing tangible evidence to substantiate the claim that the presented method is on par with its counterparts within the realm of detecting and classifying lung nodules.

It is worth noting that the proposed method has yielded a remarkable accuracy rate of 95% in the phase of nodule detection, further bolstering its credibility and reliability. Furthermore, the accuracy rate achieved during the step of nodule classification stands at an impressive 97.3%, thereby cementing the efficacy of the proposed method in a comprehensive manner.

The purpose of this work to provide an intelligent system for reducing the amount of the workload of the physicians in this field. After examining and studying some data set, the LIDC-IDRI data set is presented, for this work because of being suitable for two works of detecting nodule place and nodule classification, lots of data, and because of being reliable and used in previous reliable works that can provide the ability to compare the results.

Drug-disease interactions (DDSIs) are associated with increasing morbidity, mortality, and healthcare costs. These interactions are preventable if recognized and managed properly. Medication safety is critical in kidney transplant patients due to polypharmacy, co-morbidities, and susceptibility to adverse events. Clinical decision support systems (CDSSs) can play a key role therein. Therefore, this study aims to report on the process of developing an innovative, patient-centered, context-aware CDSS for managing DDSIs in kidney recipients.

Clinically important DDSIs were identified in the medications of patients at a kidney transplant outpatient clinic. Subsequently, rules for their detection and management were extracted based on pharmacology references and clinical expertise. A CDSS was developed and piloted following recommendations on medication CDSS design principles.

The knowledge base for this CDSS was developed with clinical context sensitivity. We defined priority levels for alerts, established associated display rules, and determined necessary actions based on the transplantation clinical workflow. The DDSI-CDSS correctly detected 37 DDSIs and displayed nine warnings and 28 cautionary alerts for the medications of 113 study patients (32.7% DDSI rate). The system fired three warnings for diltiazem in bradyarrhythmia, and two for each of the following medications and underlying diseases: aspirin in asthma, erythropoietin alfa in hypertension, and gemfibrozil in gall bladder disease. The potential consequences of the identified DDSIs were GI complications (17%), deterioration of the existing disease/condition (6.1%), and an increased risk of arrhythmias (2.6%), thrombosis (2.6%), and hypertension (1.7%). Complying with system alerts and recommendations would potentially prevent all these DDSIs.

This study delineates the process of developing an evidence-based DDSI-CDSS for kidney transplantation, laying the groundwork for future advancements. Our results underscore the clinical significance of these interactions and emphasize the imperative for their accurate and timely detection, particularly in these vulnerable patients.

Given the emergence of startup businesses in digital health and the importance of their products for human well-being, there is a critical need for consistent and continuous evaluation of these products. Therefore, producing high-quality and standard products in the health sector helps maintain the competitive market both nationally and internationally. Due to the lack of an evidence-based system that provides comprehensive educational information and an empowerment platform in health fields, this study aimed to design a system for empowering startup businesses to evaluate digital health products.

This study was conducted in three phases: 1) descriptive, 2) development, and 3) evaluation. In the first phase, data elements and functional and non-functional requirements were identified through a literature review and validation by experts. In the second phase, the system was developed using SQL Server 2019, C# language, and an interface was created using ASP.NET Core in Visual Studio 2022, along with programming languages HTML, CSS, Bootstrap, and Java to design a database. In the evaluation phase, the designed system was assessed based on Nielsen’s questionnaire and usability indicators.

Twenty functional and eleven non-functional components were identified for the design of the system, which were confirmed by related experts. The system comprises main modules, including educational resources, empowerment, a network of startups, a discussion forum, experiences, and a library for evaluating digital health products. In the empowerment section, the modules of consultation, webinars, coaching programs, tools, indicators, and evaluation frameworks were developed and implemented. The highest average severity of issues was related to the error prevention section rather than reminders, while the lowest average severity of issues was associated with the consistency and standards principle.

The design of empowerment systems in startup businesses can provide a suitable platform for enhancing evidence-based evaluation for their companies. 

Digital health technologies are transforming healthcare delivery globally. The purpose of the current study was to identify and map the current status of digital health applications in Iran through providing graphical/tabular classifications on the studies conducted in this field.

Following PRISMA guidelines, relevant English-language papers on digital health in Iran published from 2012 until 2023 in online scientific databases, including PubMed, Scopus, Web of Science and IEEE Xplore were screened. A total of 97 papers were selected for data extraction data including digital heath technologies, medical fields, application areas and users.

The number of publications has grown considerably since 2016. The most common digital health technologies were artificial intelligence including machine learning (34%), mobile health (25%) and telehealth (16%). These were mostly applied in infections (16%), nutrition/metabolism disorders (13%), mental health (20%) and cancers (12%). The key application areas were education (21%), therapy (16%) and diagnosis (15%). The primary users were patients (45%) and healthcare professionals (42%).

Digital health studies in Iran are continuously evolving. The activities are focused on a few technologies like artificial intelligence, with applications in diverse medical subfields for objectives like education and diagnosis. These results help identify research gaps and future directions for advancing digital health in Iran.

Medical tourism is a recent form of travel that involves rapidly increasing overseas trips to address a variety of health issues. Medical tourism websites play a vital role in offering information and resources for people looking for medical treatment overseas. A well-designed and easy-to-use website can help build trust and credibility with potential patients. The aim of this study is to assess the usability of the leading medical tourism websites in Iran.

The present study was a descriptive and cross-sectional study. In this study, the usability of five top medical tourism website in Iran were evaluated using heuristic evaluation method. The checklist was based on Nielsen’s heuristics. The criteria for selecting the top websites were Alexa Rank Checker.

The median of usability problems rates in website1, website2, website3, website4, and website5 were 11.1%, 7.7%, 9.9%, 10% and 9.1%, respectively. The median severity of identified problems for website1, website2, website3, website4, and website5 were 2.6%, 2.9%, 2.9%, 2.9% and 2.8%, respectively. The principles of “help and documentation”, “visibility of site status”, and “Security and privacy” in the five websites were categorized into the “highest rate of problems”, “lowest rate of problems”, and “highest severity of problems” groups, respectively.

By addressing usability issues of medical tourism websites, organizations can better serve their users and ultimately contribute to the growth of the medical tourism industry. 

Chatbots, computer programs emulating natural language conversations, have gained attention in healthcare. Recent advances address issues like obesity, dementia, oncology, and insomnia. A comprehensive assessment of their utility is essential for widespread adoption. This study aims to summarize chatbots' role in healthcare.

The methodology involved a systematic review of English-language literature up to May 8, 2023, from databases of Embase, PubMed, Web of Science, and Scopus. Selection followed a two-step process based on inclusion/exclusion criteria. The PRISMA checklist and AMSTAR-2 tool ensured quality.

The review encompassed 38 articles. Findings reveal chatbots primarily promote healthy lifestyles, improving mental well-being. They are widely used for treatment, education, and screening due to their accessibility.

Chatbots hold transformative potential in healthcare, especially in mental health, cancer management, and public health. They are poised to revolutionize the industry, offering innovative solutions and improving patient outcomes. 

Many patients, especially in rural and remote areas, face the problem of unavailability of specialists. Today, virtual visit technology is used by the public as a solution to solve the problem of lack of access to specialists and reduce unnecessary costs. Therefore, the purpose of this study is to identify the strengths, weaknesses, opportunities and threats (SWOT) of the virtual visit system from the point of view of physicians and patients.

This cross-sectional qualitative study was conducted from June to August 2023 in Kermanshah. Semi-structured and in-depth interviews were conducted face-to-face with five physicians and ten patients. All interviews were transcribed verbatim. Data analysis utilized the SWOT framework and the software Atlas.ti8.

The results of this study revealed that virtual visits have strengths such as minimal infrastructure requirements, cost reduction, and human resource efficiency. Weaknesses include the absence of physical examination, reduced quality of examinations, and limitations for certain patients. Opportunities include quick visits in remote areas and a decrease in health disparities, while threats encompass potential privacy breaches, violation of patient information confidentiality, lack of motivation among health users, and limited access to technology.

Based on the study findings, in healthcare crises, telemedicine technology proves effective in providing primary healthcare services. Telemedicine plays a crucial role in health by enhancing access and reducing costs. To optimize this process, a comprehensive strategy in digital health, involving infrastructure investment and stakeholder feedback, is evident. Sharing experiences and addressing challenges empower managers to enhance healthcare services through virtual visits, adapting to the prevailing conditions.

Coronary artery disease (CAD) stands among the leading global causes of mortality, underscoring the critical necessity for early detection to facilitate effective treatment. Although Coronary Angiography (CA) serves as the gold standard for diagnosis, its limitations for screening, including side effects and cost, necessitate alternative approaches. This study focuses on the development and comparison of machine learning techniques as substitutes for CA in CAD screening, leveraging routine clinical and laboratory data.

Various machine learning classification algorithms—decision tree, k-nearest neighbor, artificial neural network, support vector machine, logistic regression, and stacked ensemble learning—were employed to differentiate CAD and healthy subjects. Feature selection algorithms, namely LASSO and ReliefF, were utilized to prioritize relevant features. A range of evaluation metrics, including accuracy, precision, sensitivity, specificity, AUC, F1 score, ROC curve, and NPV, were applied. The SHAP technique was employed to elucidate and interpret the artificial neural network model.

The artificial neural network, support vector machine, and stacked ensemble learning models demonstrated excellent results in a 10-fold cross-validation evaluation using features selected by LASSO and ReliefF. With the LASSO feature selection algorithm, these models achieved accuracies of 90.38%, 90.07%, and 90.39%, sensitivities of 94.43%, 93.03%, and 93.96%, and specificities of 80.27%, 82.77%, and 81.52%, respectively. Using ReliefF, the accuracies were 88.79%, 88.77%, and 90.06%, sensitivities were 92.12%, 91.66%, and 93.98%, and specificities were 80.13%, 81.38%, and 80.13%, respectively. The SHAP technique revealed that typical and atypical chest pain, hypertension, diabetes mellitus, T inversion, and age were the most influential features in the neural network model.

The machine learning models developed in this study exhibit high potential for non-invasive screening and diagnosis of CAD in the Z-Alizadeh Sani dataset. However, further studies are essential to validate and apply these models in real-world and clinical settings.

Rumors published during the COVID-19 pandemic have damaged people's understanding of a particular medical condition, as well as the conditions necessary for crisis management. The present study aimed to explore the extent of misinformation, including Fake, False, and Misleading news on COVID-19, which is available on the Internet.

Data were obtained from "Poynter," a fact-checking database that unites fake news and debunks or fact-checks them with supporting articles from fact-checkers in more than 70 countries. The retrieved claims were classified into five categories of myths presented on the WHO website. To have a better understanding of the entity of claims, the most common keywords presented in the retrieved news title were extracted using Python3 programming language.

The findings showed that False and Misleading news retrieved in 2020 was primarily related to the transmission category, while in 2021, most of the False and Misleading claims were related to the prevention category. The most repetitive keyword was related to "COVID-19 vaccine”. Also, most of the misinformation has been distributed through social media. Most misinformation has been published through the Facebook social network (59%).

Social networks have an elevated share in disseminating misinformation, and Facebook was recognized as the most popular one in terms of disseminating such news. The WHO and health-related institutions should prevent the spread of this news by informing and using the latest technologies and trying to reduce this information to pave the way for a pandemic to move towards control or possible termination.

Contemporary wound care (WC) complexities strain healthcare systems and challenge informal caregivers (ICs), especially in home settings. Mobile health applications (mHealth apps) offer real-time solutions, and telemedicine's rise emphasizes its potential to address these challenges. The aim of this study was to systematically evaluate and synthesize existing literature on mHealth app interventions for WC, with a specific emphasis on understanding the involvement, impact, and contributions of ICs in these interventions.

This study followed the PRISMA guidelines for systematic reviews. Articles were sourced from three databases (PubMed, Cochrane Library, and CINAHL), focusing on WC via mHealth with IC involvement. Quality assessment tools, including the Newcastle-Ottawa Scale and the Cochrane Collaboration tool, were used to ensure high research standards.

Upon meticulous examination of the articles, a mere six accurately aligned with the primary objectives of the research. Modern strides in healthcare technology have undeniably augmented both patient care and education. Several studies from different nations have delved into various wound categories, including pressure ulcers, diabetic foot ulcers, surgical wounds, and burns. The participant count in these scholarly investigations fluctuated between 15 and 70. Remarkably, among these six, only a single study concentrated on ICs.

Wound management requires an integrated technology, education, and IC training approach. Our review suggests that research on mHealth app interventions for ICs in WC needs to be more represented in global literature. Given this gap, we advocate for enhanced joint efforts to ensure that WC advances with digital healthcare without overlooking the IC population.

Laryngeal disorders are a common health problem that affects people of all ages, genders, and races. One of the main symptoms of laryngeal disorders is changes in the voice, which can be used as an indicator for the presence of such disorders. In this paper, we present a data mining approach for using voice as an indicator for laryngeal disorders.

We collected a dataset of voice recordings from individuals with and without laryngeal disorders including 434 people from two clinical centers in Tehran. The dataset was created using a powerful signal processing program and then based on the difference between male and female voice, the dataset was separated into two datasets. Finally, a Deep Neural Network was implemented for modelling using Python programming language and F1-score, Accuracy, Sensitivity, Specificity, and AUC as the model’s evaluation metrics were reported.

Among all the acoustic features, 23 features were selected for the male dataset and 25 features for the female data set. For the male dataset the final model achieved F1-Score of 0.915 and Accuracy of 0.910. For the female dataset the result was 0.884 of F1-Score and 0.896 of Accuracy.

Our results show that machine learning algorithms can accurately classify voice recordings into two groups: individuals with laryngeal disorders and those without. The high accuracy achieved by the algorithms suggests that voice can be used as an objective and automated diagnostic tool for laryngeal disorders. 

The presence of pigmented skin lesions is a significant global concern in the prevention of skin cancer. Detecting skin cancer at an early stage is essential for proper management and effective treatment. This study aimed to combine image processing and data mining to develop an intelligent model to screen skin cancer from skin lesions.

The images were taken in a clinic by smartphone. Patients over 40 years of age participated in the study. During the segmentation phase, the lesions were separated from the original images through machine vision techniques. Various features such as symmetry, border irregularity, color variation, and diameter were extracted from the images, while some features were also obtained through face-to-face examination. Finally, a neural network was employed to classify whether the lesion was cancerous or non-cancerous. In addition, MATLAB version 2022 was considered to design the model.

The study results indicated excellent segmentation. Using a neural network-based model, skin lesions were classified with a high level of accuracy, with 98.4% accuracy and 97% sensitivity. The results indicated the designed model significantly screened skin cancer with high accuracy.

This model can help patients to manage self-care and become aware of their skin lesions before consulting a physician.

Pressure ulcers are a significant health concern. In today's tech era, mobile health applications offer real-time data, efficient scheduling, and automation, leading to cost-effective care and improved patient satisfaction. This study aimed to assess the feasibility of a novel preventive mobile health application named "PressureUlcerAdvisor," targeted at informal caregivers of outpatients at risk of developing pressure ulcers.

In this rigorous quasi-experimental study, 23 informal caregivers were assigned to the intervention group and 22 to the control group (n=45). Feasibility was assessed by considering app utility, ease of use, knowledge gaps in prevention, pressure ulcer rates, and caregivers' self-efficacy. Data was collected at baseline, two, and four months and analyzed using a combination of descriptive statistics and inferential analysis, ensuring the reliability and validity of the findings.

The study found a significant improvement in the prevention knowledge (p=0.040) and patient support efficacy (p=0.049) between the control and intervention groups. The control group did not show any progress, while the intervention group did. Notably, the initial acceptance of the app, though low, showed a promising upward trend, improving significantly after four months (p=0.010). Additionally, after four months, perceived usefulness was positively associated with patient support efficacy (r=0.40, p=0.05) despite the perceived ease of use remaining steady.

This investigation suggests that the innovative preventative mobile health application, 'PressureUlcerAdvisor,' can be a game-changer for caregivers. It is well-received among caregivers caring for outpatients at risk of developing pressure ulcers. The study's positive outcomes, including a significant improvement in prevention knowledge and patient support efficacy, hint at the potential of this application to significantly aid caregivers' efforts in pressure ulcer prevention, empowering them with knowledge and tools to provide better care.