Frontiers in health informatics最新文献

{"title":"Overview and Classification of Evaluation Metrics of Appointment Scheduling Systems","authors":"Mohammad Reza Mazaheri Habibi, Fahimeh Mohammad Abadi, Hamed Tabesh, Hassan Vakili Arki, A. Abu-Hanna, Saeid Eslami","doi":"10.30699/fhi.v13i0.573","DOIUrl":"https://doi.org/10.30699/fhi.v13i0.573","url":null,"abstract":"Introduction: This study reviews the several metrics used to evaluate the performance of appointment scheduling systems.Material and Methods: 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.Results: 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.Conclusion: 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.","PeriodicalId":477354,"journal":{"name":"Frontiers in health informatics","volume":"33 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140082536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Readability and credibility evaluation of most-visited health websites based on eBizMBA and Alexa global ranking","authors":"S. Rahmatizadeh, Saeideh Valizadeh-Haghi, Hamed Nasibi-Sis, Hossein Motahari-Nezhad","doi":"10.30699/fhi.v13i0.567","DOIUrl":"https://doi.org/10.30699/fhi.v13i0.567","url":null,"abstract":"Introduction: 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.Material and Methods: 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.Results: 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.Conclusion: 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.","PeriodicalId":477354,"journal":{"name":"Frontiers in health informatics","volume":"18 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Prevalence of Smartphone Addiction and Its Relationship with the Level of e-Health Literacy in Medical Sciences Students","authors":"Sadrieh Hajesmaeel-Gohari, Fateme Mirzapourestabragh, Maryam Zeidabadi-Nejad","doi":"10.30699/fhi.v13i0.536","DOIUrl":"https://doi.org/10.30699/fhi.v13i0.536","url":null,"abstract":"Introduction: 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.Material and Methods: 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.Results: 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.Conclusion: 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.","PeriodicalId":477354,"journal":{"name":"Frontiers in health informatics","volume":"59 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart Solutions for Veterans: Enhancing Amputee Self-Care through Mobile Technology","authors":"Ali Khatib, L. Gholamhosseini, Mahboobeh Afzali, Iman Nikkhoo, Victor Lami","doi":"10.30699/fhi.v13i0.563","DOIUrl":"https://doi.org/10.30699/fhi.v13i0.563","url":null,"abstract":"Introduction: 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.Material and Methods: 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.Results: 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).Conclusion: 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.","PeriodicalId":477354,"journal":{"name":"Frontiers in health informatics","volume":"411 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140455551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survival Analysis of Patients with COVID-19 using Deep Neural Network and Random Forrest Techniques","authors":"A. Yazdani, L. Erfannia, Ali Farzaneh, Omar Ali","doi":"10.30699/fhi.v13i0.512","DOIUrl":"https://doi.org/10.30699/fhi.v13i0.512","url":null,"abstract":"Introduction: 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.Material and Methods: 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.Results: 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.Conclusion: 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.","PeriodicalId":477354,"journal":{"name":"Frontiers in health informatics","volume":"218 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140459633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Telenursing Training on Job Burnout in Nurses with a History of COVID-19","authors":"Fatemeh Azarian Nejad, M. Naderifar, Elaheh Asadi-Bidmeshki, Mohammadreza Firouzkohi, Abdolghani Abdollahimohammad","doi":"10.30699/fhi.v13i0.544","DOIUrl":"https://doi.org/10.30699/fhi.v13i0.544","url":null,"abstract":"Introduction: 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.Material and Methods: 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.Results: 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.Conclusion: 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.","PeriodicalId":477354,"journal":{"name":"Frontiers in health informatics","volume":"44 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140461424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Machine Learning Algorithms in Determining the Stage of Breast Cancer from Pathology Reports","authors":"Shirin Samadzad-Qushchi, Parinaz Eskandarian, Z. Niazkhani, Ali Rashidi, H. Pirnejad","doi":"10.30699/fhi.v13i0.519","DOIUrl":"https://doi.org/10.30699/fhi.v13i0.519","url":null,"abstract":"Introduction: 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.Material and Methods: 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.Results: 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.Conclusion: 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.","PeriodicalId":477354,"journal":{"name":"Frontiers in health informatics","volume":"2 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139687088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Requirements for Developing a Telehealth-Based Health Management Platform in Iran","authors":"Parastoo Amiri, Hamid Sharifi, M. Soofi, K. Bahaadinbeigy, Ali Mohammadi","doi":"10.30699/fhi.v13i0.537","DOIUrl":"https://doi.org/10.30699/fhi.v13i0.537","url":null,"abstract":"Introduction: 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.Material and Methods: 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.Results: 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.Conclusion: 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.","PeriodicalId":477354,"journal":{"name":"Frontiers in health informatics","volume":"58 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140494790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Evaluation of a Self-Care Mobile Application for Viral Hepatitis Patients","authors":"Mehri Ansari, Reza Khajouei, Sadrieh Hajesmaeel-Gohari, P. Davoodian, S. Hosseini Teshnizi","doi":"10.30699/fhi.v13i0.531","DOIUrl":"https://doi.org/10.30699/fhi.v13i0.531","url":null,"abstract":"Introduction: 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.Material and Methods: 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.Results: 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.Conclusion: 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.","PeriodicalId":477354,"journal":{"name":"Frontiers in health informatics","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140501785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of Heart Disease Using Data Mining Techniques: A Systematic Review of Influential Factors and Outcomes","authors":"Elahe Gozali, Sadrieh Hajesmaeel-Gohari, Kamal Khademvatani, Rahime Tajvidi Asr","doi":"10.30699/fhi.v13i0.541","DOIUrl":"https://doi.org/10.30699/fhi.v13i0.541","url":null,"abstract":"Introduction: 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.Material and 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.Results: 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.Conclusion: 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.","PeriodicalId":477354,"journal":{"name":"Frontiers in health informatics","volume":"55 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140505487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}