Computer methods and programs in biomedicine update最新文献

{"title":"Using image processing and automated classification models to classify microscopic gram stain images","authors":"Kris Kristensen ,&nbsp;Logan Morgan Ward ,&nbsp;Mads Lause Mogensen ,&nbsp;Simon Lebech Cichosz","doi":"10.1016/j.cmpbup.2022.100091","DOIUrl":"10.1016/j.cmpbup.2022.100091","url":null,"abstract":"<div><h3>Background and Objective</h3><p>Fast and correct classification of bacterial samples are important for accurate diagnostics and treatment. Manual microscopic interpretation of Gram stain samples is both time consuming and operator dependent. The aim of this study was to investigate the potential for developing an automated algorithm for the classification of microscopic Gram stain images.</p></div><div><h3>Methods</h3><p>We developed and tested two algorithms (using image processing an Casual Probabilistic Network (CPN) and a Random Forest (RF) classification) for the automated classification of Gram stain images. A dataset of 660 images including 33 microbial species (32 bacteria and one fungus) was split into training, validation, and test sets. The algorithms were evaluated based on their ability to correctly classify samples and general characteristics such as aggregation and morphology.</p></div><div><h3>Results</h3><p>The CPN correctly classified 633/792 images to achieve an overall accuracy of 80% compared to the RF which correctly classified 782/792 images to achieve an overall accuracy of 99% (<em>p</em> &lt; 0.001). The CPN performed well when distinguishing between GN and GP, with an accuracy of 95% (731/768). The RF also performed well in distinguishing between GN and GP, achieving an accuracy of 99% (767/768) (<em>p</em> &lt; 0.001).</p></div><div><h3>Conclusions</h3><p>The findings from this study show promising results regarding the potential for an automated algorithm for the classification of microscopic Gram stain images.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100091"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46899539","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":"A Blockchain-Based Framework for COVID-19 Detection Using Stacking Ensemble of Pre-Trained Models","authors":"Kashfi Shormita Kushal,&nbsp;Tanvir Ahmed,&nbsp;Md Ashraf Uddin,&nbsp;Muhammed Nasir Uddin","doi":"10.1016/j.cmpbup.2023.100116","DOIUrl":"10.1016/j.cmpbup.2023.100116","url":null,"abstract":"<div><p>In recent years, COVID-19 has impacted millions of individuals worldwide, resulting in numerous fatalities across several countries. While RT-PCR technology remains the most reliable method for detecting COVID-19, this approach is expensive and time-consuming. As a result, researchers have explored various machine learning and deep learning-based approaches to rapidly identify COVID-19 cases using X-ray images. Machine learning based models can reduce costs and have shorter processing times. However, preserving patient confidentiality poses challenges within such third-party-controlled systems, potentially failing to safeguard patients from potential disgrace and discomfort. Nonetheless, blockchain technology offers the potential to securely store sensitive medical data anonymously, without requiring third-party intervention. Consequently, the combination of deep learning and blockchain might offer a viable solution to mitigate the spread of COVID-19 while ensuring patient privacy protection. In this paper, we propose a hybrid model of blockchain and deep learning model for automatically detecting COVID-19 using chest X-rays (CXR). The deep learning model includes a stacking ensemble of three modified pre-trained Deep Learning (DL) models: VGG16, Xception, and DenseNet169. The model obtained an accuracy of 99.10% and 98.60% for binary and multi-class respectively. Further, to ensure COVID-19 patients’ privacy and security, the Ethereum blockchain has been adopted to store information related to COVID-19 cases. In addition, a smart contract on the blockchain has been designed for handling X-ray images in the Interplanetary File System (IPFS).</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48723971","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":"Identifying and defining entities associated with fall risk factors events found in fall risk assessment tools","authors":"Fatimah Altuhaifa ,&nbsp;Dalal Al Tuhaifa ,&nbsp;Eman Al Ribh ,&nbsp;Ezdehar Al Rebh","doi":"10.1016/j.cmpbup.2023.100105","DOIUrl":"10.1016/j.cmpbup.2023.100105","url":null,"abstract":"<div><h3>Purpose</h3><p>The contents of nursing notes play an important role in predicting patient fall risk. Based on data collected from fall risk assessment tools, we aimed to identify and define fall risk factors to support natural language processing, data mining of nursing notes, and automated fall prediction.</p></div><div><h3>Methods</h3><p>The PRISMA-ScR guidelines were used to summarize entities associated with the fall risk factors described in fall risk assessment tools. Fall risk factors (concepts) and their related words (entities) were extracted from the tools. In order to clarify the meaning of unclear fall risk factors and classify fall risk factor entities, we searched the websites of the World Health Organization and the governments of Victoria, Australia, and New South Wales (up to 20 December 2021). A nurse and a safety expert reviewed and assessed the extracted concepts and entities for clarity and relevance. Then, the NLPfallRisk tool was developed to extract entities associated with fall risk factors.</p></div><div><h3>Results</h3><p>We identified 20 validated fall risk assessment tools appropriate for hospitals and healthcare facilities. Using these tools, we extracted 19 especially significant risk factors as the most significant and identified 151 entities related to them.</p></div><div><h3>Conclusion</h3><p>We found that fall assessment tools considered a history of falls more frequently than any other risk factor. However, as fall risk tends to be multifaceted, risk assessments must take many factors into account.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47769083","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":"Interpretable hybrid model for an automated patient-wise categorization of hypertensive and normotensive electrocardiogram signals","authors":"Chen Chen ,&nbsp;Hai Yan Zhao ,&nbsp;Shou Huan Zheng ,&nbsp;Reshma A Ramachandra ,&nbsp;Xiaonan He ,&nbsp;Yin Hua Zhang ,&nbsp;Vidya K Sudarshan","doi":"10.1016/j.cmpbup.2023.100097","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2023.100097","url":null,"abstract":"<div><h3>Background and Objective</h3><p>Hypertension is critical risk factor of fatal cardiovascular diseases and multiple organ damage. Early detection of hypertension even at pre-hypertension stage is helpful in preventing the forthcoming complications. Electrocardiogram (ECG) has been attempted to observe the changes in electrical activities of the hearts of hypertensive patients. To automate the ECG assessment in the detection of hypertension, an interpretable hybrid model is proposed in this paper.</p></div><div><h3>Methods</h3><p>The proposed hybrid framework consists of one dimensional - Convolutional Neural Network architecture with four blocks of convolutional layers, maxpooling followed by dropout layers fused with Support Vector Machine classifier in the final layer. The implemented hybrid model is made explainable and interpretable using Local Interpretable Model-agnostic Explanations (LIME) method. The developed hybrid model is trained and tested for patient-wise classification of ECGs using online Physionet datasets and hospital data.</p></div><div><h3>Results</h3><p>The proposed method achieved highest accuracy of 81.81% in patient-wise ECG classification of online datasets, and highest accuracy of 93.33% in patient-wise ECG classification of hospital datasets as normotensive and hypertensive. The visualization of results showed only one normotensive patient's ECG is misclassified (predicted) as hypertensive, with identification of patient number, among the 15 patients (8 normotensive and 7 hypertensive) ECGs tested. In addition, the LIME results demonstrated an explanation to the predictions of hybrid model by highlighting the features and location of ECG waveform responsible for it, thus making the decision of hybrid model more interpretable.</p></div><div><h3>Conclusion</h3><p>Furthermore, our developed system is implemented as an assisting automated software tool called, <em>HANDI</em> (<u>H</u>ypertensive <u>A</u>nd <u>N</u>ormotensive patient <u>D</u>etection with <u>I</u>nterpretability) for real-time validation in clinics for early capture of hypertensive and proper monitoring of the patients.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100097"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49780852","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":"Predicting the evolution of COVID-19 mortality risk: A Recurrent Neural Network approach","authors":"Marta Villegas ,&nbsp;Aitor Gonzalez-Agirre ,&nbsp;Asier Gutiérrez-Fandiño ,&nbsp;Jordi Armengol-Estapé ,&nbsp;Casimiro Pio Carrino ,&nbsp;David Pérez-Fernández ,&nbsp;Felipe Soares ,&nbsp;Pablo Serrano ,&nbsp;Miguel Pedrera ,&nbsp;Noelia García ,&nbsp;Alfonso Valencia","doi":"10.1016/j.cmpbup.2022.100089","DOIUrl":"10.1016/j.cmpbup.2022.100089","url":null,"abstract":"<div><h3>Background:</h3><p>In December 2020, the COVID-19 disease was confirmed in 1,665,775 patients and caused 45,784 deaths in Spain. At that time, health decision support systems were identified as crucial against the pandemic.</p></div><div><h3>Methods:</h3><p>This study applies Deep Learning techniques for mortality prediction of COVID-19 patients. Two datasets with clinical information were used. They included 2,307 and 3,870 COVID-19 infected patients admitted to two Spanish hospitals. Firstly, we built a sequence of temporal events gathering all the clinical information for each patient, comparing different data representation methods. Next, we used the sequences to train a Recurrent Neural Network (RNN) model with an attention mechanism exploring interpretability. We conducted an extensive hyperparameter search and cross-validation. Finally, we ensembled the resulting RNNs to enhance sensitivity.</p></div><div><h3>Results:</h3><p>We assessed the performance of our models by averaging the performance across all the days in the sequences. Additionally, we evaluated day-by-day predictions starting from both the hospital admission day and the outcome day. We compared our models with two strong baselines, Support Vector Classifier and Random Forest, and in all cases our models were superior. Furthermore, we implemented an ensemble model that substantially increased the system’s sensitivity while producing more stable predictions.</p></div><div><h3>Conclusions:</h3><p>We have shown the feasibility of our approach to predicting the clinical outcome of patients. The result is an RNN-based model that can support decision-making in healthcare systems aiming at interpretability. The system is robust enough to deal with real-world data and can overcome the problems derived from the sparsity and heterogeneity of data.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100089"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9991365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating parameter values and initial states of variables in a mathematical model of coronavirus disease 2019 epidemic wave using the least squares method, Visual Basic for Applications, and Solver in Microsoft Excel","authors":"Toshiaki Takayanagi","doi":"10.1016/j.cmpbup.2023.100111","DOIUrl":"10.1016/j.cmpbup.2023.100111","url":null,"abstract":"<div><h3>Background</h3><p>With the global spread of coronavirus disease 2019 (COVID-19), understanding the mechanisms and characteristics of epidemic waves has become necessary to control its spread. The sixth epidemic wave of COVID-19 in Sapporo, Japan, was analyzed using a new mathematical model called the SI_UI_CI_CPR_UR_C model. The main objectives are (1) introducing the SI_UI_CI_CPR_UR_C model, (2) introducing algorisms by which parameters and initial states were estimated, and (3) estimating values of parameters and initial states, and analyzing the epidemic wave.</p></div><div><h3>Methods</h3><p>Reported numbers of daily new confirmed infected cases, currently infected cases, and cumulative numbers of recovered or fatal cases were collected from the official website of the city of Sapporo. The SI_UI_CI_CPR_UR_C model, based on susceptible-infectious-removed and infection-period-structured models, was employed. Parameter values and initial states of variables were estimated using the least squares method, Visual Basic for Applications, and Solver in Microsoft Excel.</p></div><div><h3>Results</h3><p>The peak time of transmission rate was estimated to be 5.8 to 6.0 days after infection, the peak time of infection confirmation rate was 8.0 to 8.1 days after infection, and the ultimate confirmation ratio of infection was 0.65 to 0.85. It was also estimated that almost all individuals in Sapporo were susceptible to the Omicron variant of the severe acute respiratory syndrome-coronavirus 2.</p></div><div><h3>Conclusion</h3><p>The sixth epidemic wave of COVID-19 was analyzed with the SI_UI_CI_CPR_UR_C model, with which crucial parameters and initial states were estimated. Furthermore, the results indicate that vaccination against the Wuhan strain and the previous infection were insufficient to induce a level of immunity required to prevent infection by the Omicron variant. Further improvement of mathematical modeling for infectious diseases is required to control emerging infectious diseases in the future, even if the threat of COVID-19 is overcome.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48375289","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":"Awareness Level of Huntington Disease: Comprehensive Analysis of Tweets During Huntington Disease Awareness Month","authors":"Nawal H Alharthi ,&nbsp;Eman M Alanazi ,&nbsp;Xiaoyu Liu","doi":"10.1016/j.cmpbup.2023.100117","DOIUrl":"10.1016/j.cmpbup.2023.100117","url":null,"abstract":"<div><h3>Background</h3><p>Unawareness of Huntington disease is prevalent where patients might have a denial of illness, less reporting of symptoms such as changes in behavior or cognitive impairment, or poor coping with the disease. Understanding the awareness level of Huntington disease is crucial to provide more suggestions for public health campaigns.</p></div><div><h3>Objective</h3><p>This study explores the level of awareness of Huntington's disease among users of social media. We will also explore the tweeting behavior during Huntington disease awareness month, and search any missing area related to the awareness by following the framework of Social Media-Based Public Health Campaigns.</p></div><div><h3>Method</h3><p>We extracted tweets from April 2021-Jun 2021. We used both quantitative and qualitative methods to analyze the data. We used Python programming and various natural language processing tools to process and analyze data for a quantitative investigation. We also carried out a qualitative content analysis to identify themes and subthemes in the data.</p></div><div><h3>Result</h3><p>We discovered that the most popular hashtag is #LetsTalkAboutHD, and after looking over the data, it seemed to us that the word \"support\" was used more than 54 times during that time. According to the findings of our analysis of the twitter distribution pattern in terms of time, the most tweets were sent between May 13 and May 16, particularly on Wednesday, which was the busiest day. Also, the United States and Alaska had the highest levels of engagement when the pattern of tweets based on geographic location was examined. The most common pattern in the tweets that we separated based on patterns was news, which was followed by research and clinical trials.</p></div><div><h3>Conclusion</h3><p>Awareness campaigns needs to follow the framework of social media-Based Public Health Campaigns to provide more comprehensive information about Huntington disease and increase the awareness level among patients and families.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46317886","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":"How do current digital patient decision aids in maternity care align with the health literacy skills and needs of clients?: a think aloud study","authors":"Laxsini Murugesu ,&nbsp;Mirjam P. Fransen ,&nbsp;Anna L. Rietveld ,&nbsp;Danielle R.M. Timmermans ,&nbsp;Ellen M.A. Smets ,&nbsp;Olga C. Damman","doi":"10.1016/j.cmpbup.2023.100120","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2023.100120","url":null,"abstract":"<div><h3>Background</h3><p>Patient decision aids (PDAs) have shown to be effective in facilitating shared decision-making (SDM) in maternity care. However, many PDAs are difficult to use for clients because of high cognitive demand.</p></div><div><h3>Objective</h3><p>This study aimed to explore how current digital PDAs support clients’ health literacy skills (understanding, appraising, and applying information) and fit their needs for support in SDM in maternity care.</p></div><div><h3>Methods</h3><p>Clients (n=21) in Dutch maternity care were invited to use five PDAs during think aloud interviews. The interviews were transcribed verbatim, coded with open and axial coding, and analysed using thematic analysis. A framework of health literacy skills for SDM was used to categorize the themes.</p></div><div><h3>Results</h3><p>Clients reported a need for support to appraise and understand the purpose of PDAs. Most clients adequately used both benefit/harm information about available options and available Value Clarification Methods (VCM), indicating that these main PDA elements supported them to actively process this information in their decision-making process. However, these elements were only appreciated and adequately used when clients understood the pregnancy- and labour related terminology used. A lack of balanced probability information about outcomes of options for mother and child hindered further information use. VCM were only used when presented attributes were relevant for clients.</p></div><div><h3>Conclusions</h3><p>Clients were in general able to process and use information presented in PDAs in maternity care tested in this study, thus PDAs were aligned with health literacy skills. Adequate understanding of terminology and perceived relevance of specific information elements were important preconditions.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49762644","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":"Rianú: Multi-tissue tracking software for increased throughput of engineered cardiac tissue screening","authors":"Jack F. Murphy,&nbsp;Kevin D. Costa,&nbsp;Irene C. Turnbull","doi":"10.1016/j.cmpbup.2023.100107","DOIUrl":"10.1016/j.cmpbup.2023.100107","url":null,"abstract":"<div><h3>Background:</h3><p>The field of tissue engineering has provided valuable three-dimensional species-specific models of the human myocardium in the form of human Engineered Cardiac Tissues (hECTs) and similar constructs. However, hECT systems are often bottlenecked by a lack of openly available software that can collect data from multiple tissues at a time, even in multi-tissue bioreactors, which limits throughput in phenotypic and therapeutic screening applications.</p></div><div><h3>Methods:</h3><p>We developed Rianú, an open-source web application capable of simultaneously tracking multiple hECTs on flexible end-posts. This software is operating system agnostic and deployable on a remote server, accessible via a web browser with no local hardware or software requirements. The software incorporates object-tracking capabilities for multiple objects simultaneously, an algorithm for twitch tracing analysis and contractile force calculation, and a data compilation system for comparative analysis within and amongst groups. Validation tests were performed using in-silico and in-vitro experiments for comparison with established methods and interventions.</p></div><div><h3>Results:</h3><p>Rianú was able to detect the displacement of the flexible end-posts with a sub-pixel sensitivity of 0.555 px/post (minimum increment in post displacement) and a lower limit of 1.665 px/post (minimum post displacement). Compared to our established reference for contractility assessment, Rianú had a high correlation for all parameters analyzed (ranging from R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>=0.7514 to R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>=0.9695), demonstrating its high accuracy and reliability.</p></div><div><h3>Conclusions:</h3><p>Rianú provides simultaneous tracking of multiple hECTs, expediting the recording and analysis processes, and simplifies time-based intervention studies. It also allows data collection from different formats and has scale-up capabilities proportional to the number of tissues per field of view. These capabilities will enhance throughput of hECTs and similar assays for in-vitro analysis in disease modeling and drug screening applications.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100107"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/75/28/nihms-1909546.PMC10359020.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9848113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of the level of eHealth literacy between patients with COPD and registered nurses with interest in pulmonary diseases","authors":"Marie Knude Palshof ,&nbsp;Freja Katrine Henning Jeppesen ,&nbsp;Anne Dahlgaard Thuesen ,&nbsp;Camilla Steno Holm ,&nbsp;Eva Brøndum ,&nbsp;Lars Kayser","doi":"10.1016/j.cmpbup.2023.100121","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2023.100121","url":null,"abstract":"<div><h3>Background</h3><p>This study examines the level of eHealth literacy (eHL) of COPD patients and registered nurses (RN) prior to the implementation of a new national telehealth service. The objective was to provide the nurses with an understanding of eHL and to provide knowledge about the patients’ eHL level, socio-demographic characteristics, and digital behaviour for the nurses to be better able to support the patients’ adoption and usage of telehealth.</p></div><div><h3>Method</h3><p>The eHealth Literacy Questionnaire (eHLQ) was administered in an outpatient clinic in February and March 2020 (<em>N</em> = 42). The staff-eHLQ was administered by web in November 2019 and at a conference in January 2020 (<em>N</em> = 39). The RNs were asked about workplace and experience with telehealth and the patients about gender, age, and educational level as well as their digital health behaviour.</p><p>A multiple linear regression analysis tested for relations between the socio-demographic and digital behaviour variables and the eHLQ-scores for the COPD patients.</p></div><div><h3>Results</h3><p>The RNs’ eHLQ-scores relating to engagement with information, motivation, and experience with digital services signified an insufficient eHL level which may influence their ability to motivate and promote the usage of telehealth to patients.</p><p>The patients’ scores were higher than the RNs’ with respect to motivation and experience with digital services but seemed to have an insufficient level in relation to using technology to process information and actively engage with digital services.</p></div><div><h3>Conclusion</h3><p>The patients need support in relation to processing information and interacting with services. The RNs’ eHLQ-scores being lower than the patients are problematic as it may influence how well they are able to support the adoption of the new telehealth service.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100121"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49727002","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}