Pub Date : 2021-12-01DOI: 10.1016/j.ceh.2021.11.001
Jianan Hui, Hongju Mao
{"title":"Role of Portable and Wearable Sensors in Era of Electronic Healthcare and Medical Internet of Things","authors":"Jianan Hui, Hongju Mao","doi":"10.1016/j.ceh.2021.11.001","DOIUrl":"https://doi.org/10.1016/j.ceh.2021.11.001","url":null,"abstract":"","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73949744","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}
Pub Date : 2021-12-01DOI: 10.1016/j.ceh.2021.09.002
S. Bhat, Gurjinder Singh, W. Bhat, Kumudini Borole, Ashraf Ali Khan
{"title":"Coronavirus disease-2019 and its current scenario – A review","authors":"S. Bhat, Gurjinder Singh, W. Bhat, Kumudini Borole, Ashraf Ali Khan","doi":"10.1016/j.ceh.2021.09.002","DOIUrl":"https://doi.org/10.1016/j.ceh.2021.09.002","url":null,"abstract":"","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"13 1","pages":"67 - 73"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89707445","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}
Pub Date : 2021-11-01DOI: 10.1016/j.ceh.2021.11.002
F. Syed, Muhammad Hassan, A. Shehzad, Salman Shafi Koul, M. Arif, R. S. Dewey, T. Khaliq
{"title":"The establishment of a telemedicine center during the COVID-19 pandemic at a tertiary care hospital in Pakistan","authors":"F. Syed, Muhammad Hassan, A. Shehzad, Salman Shafi Koul, M. Arif, R. S. Dewey, T. Khaliq","doi":"10.1016/j.ceh.2021.11.002","DOIUrl":"https://doi.org/10.1016/j.ceh.2021.11.002","url":null,"abstract":"","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"22 1","pages":"50 - 53"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88132212","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}
Pub Date : 2021-04-06DOI: 10.1016/J.CEH.2021.02.001
E. Talboom-Kamp, P. Ketelaar, Anke Versluis
{"title":"A national program to support self-management for patients with a chronic condition in primary care: A social return on investment analysis","authors":"E. Talboom-Kamp, P. Ketelaar, Anke Versluis","doi":"10.1016/J.CEH.2021.02.001","DOIUrl":"https://doi.org/10.1016/J.CEH.2021.02.001","url":null,"abstract":"","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75145237","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}
Pub Date : 2021-03-19DOI: 10.1016/J.CEH.2020.12.002
Anneloek Rauwerdink, M. Kasteleyn, N. Chavannes, M. Schijven
{"title":"The successes and lessons of a Dutch University Hospitals’ eHealth program: An evaluation study protocol","authors":"Anneloek Rauwerdink, M. Kasteleyn, N. Chavannes, M. Schijven","doi":"10.1016/J.CEH.2020.12.002","DOIUrl":"https://doi.org/10.1016/J.CEH.2020.12.002","url":null,"abstract":"","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79522606","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}
Pub Date : 2021-03-19DOI: 10.1016/J.CEH.2020.11.004
M. Ebuenyi, Kyma Schnoor, Anke Versluis, E. Meijer, N. Chavannes
{"title":"Short message services interventions for chronic disease management: A systematic review","authors":"M. Ebuenyi, Kyma Schnoor, Anke Versluis, E. Meijer, N. Chavannes","doi":"10.1016/J.CEH.2020.11.004","DOIUrl":"https://doi.org/10.1016/J.CEH.2020.11.004","url":null,"abstract":"","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"37 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83586634","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}
Pub Date : 2021-03-02DOI: 10.1101/2021.02.28.21252122
R. Bentvelsen, R. van der Vaart, K. Veldkamp, N. Chavannes
Introduction In hospital care, urinary catheters are frequently used, causing a substantial risk for catheter-associated urinary tract infections (CAUTI). Patient awareness and evaluation of appropriateness of their catheter through mHealth could decrease these healthcare-associated infections. However, patient engagement via mHealth in infection prevention is still limited. Therefore, we describe the systematic development and usability evaluation of the mHealth intervention Participatient, to prevent CAUTI, aiming for optimal adoption of the app in the clinical setting. Method The CeHRes roadmap was used as development guideline, operationalizing phases for (1) contextual inquiry (observations and interviews), (2) value specification (interviews with probing) and (3) design in multiple steps and in co-creation with end-users. During phases 1 and 2, semi-structured interviews were conducted with fifteen patients and three nurses. The design phase was combined with the minimum viable product development strategy, with a focus on early cyclic steps of prototyping. Results In phase 1, patients acknowledged the risks of catheter use. Patients in phase 2 valued endorsement of a mHealth application by healthcare workers and reported to own a smartphone. Both patients and nurses recognized the need for useful modules in the app besides catheter care. Based on the needs and values as found in phase 2, the Participation app was developed. Based on usability tests in phase 3, content, text size, plain language, and navigation structures were further amended, and images were added. Conclusion This study provides real-world insight in the developmental strategy for mHealth interventions by involving both patients and care providers. Development of an app using thorough needs-assessment provided understanding for its content and design. By developing an app providing patients with reliable information and daily checklists, we aim to provide a tailored tool for communication and awareness on catheter use for the whole ward, and a potential blueprint for mHealth development.
{"title":"Systematic development of an mHealth app to prevent healthcare-associated infections by involving patients: Participatient","authors":"R. Bentvelsen, R. van der Vaart, K. Veldkamp, N. Chavannes","doi":"10.1101/2021.02.28.21252122","DOIUrl":"https://doi.org/10.1101/2021.02.28.21252122","url":null,"abstract":"Introduction In hospital care, urinary catheters are frequently used, causing a substantial risk for catheter-associated urinary tract infections (CAUTI). Patient awareness and evaluation of appropriateness of their catheter through mHealth could decrease these healthcare-associated infections. However, patient engagement via mHealth in infection prevention is still limited. Therefore, we describe the systematic development and usability evaluation of the mHealth intervention Participatient, to prevent CAUTI, aiming for optimal adoption of the app in the clinical setting. Method The CeHRes roadmap was used as development guideline, operationalizing phases for (1) contextual inquiry (observations and interviews), (2) value specification (interviews with probing) and (3) design in multiple steps and in co-creation with end-users. During phases 1 and 2, semi-structured interviews were conducted with fifteen patients and three nurses. The design phase was combined with the minimum viable product development strategy, with a focus on early cyclic steps of prototyping. Results In phase 1, patients acknowledged the risks of catheter use. Patients in phase 2 valued endorsement of a mHealth application by healthcare workers and reported to own a smartphone. Both patients and nurses recognized the need for useful modules in the app besides catheter care. Based on the needs and values as found in phase 2, the Participation app was developed. Based on usability tests in phase 3, content, text size, plain language, and navigation structures were further amended, and images were added. Conclusion This study provides real-world insight in the developmental strategy for mHealth interventions by involving both patients and care providers. Development of an app using thorough needs-assessment provided understanding for its content and design. By developing an app providing patients with reliable information and daily checklists, we aim to provide a tailored tool for communication and awareness on catheter use for the whole ward, and a potential blueprint for mHealth development.","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87159683","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}
Pub Date : 2021-01-01DOI: 10.1016/j.ceh.2020.11.001
Harshil Thakkar , Vaishnavi Shah , Hiteshri Yagnik , Manan Shah
Diabetes is an ailment in which glucose level increase in at high rates in blood due to body’s inability to metabolize it. This happens when body does not produce sufficient amount of insulin or it does not respond to it properly. Critical and long-term health issues arise if diabetes is not handled or properly treated which includes: heart problems, disorders of the lungs, skin and liver complications, nerve damage, etc. With increasing number of diabetic patients, its early detection becomes essential. In this paper, our major focus areas are data mining and fuzzy logic techniques used in diabetes diagnosis. Data mining is used for locating patterns in huge datasets using a composition of different methods of machine learning, database manipulations and statistics. Data mining offers a lot of methods to inspect large data considering the expected outcome to find the hidden knowledge. Fuzzy logic is similar to human reasoning system and hence it can handle the uncertainties found in the data of medical diagnosis. These systems are called expert systems. The fuzzy expert systems (FES) analyze the knowledge from the available data which might be vague and suggests linguistic concept with huge approximation as its core to medical texts. In this paper, the methodology section delivers the pipeline of various tasks such as selecting the dataset, preprocessing the data by applying numerous methods such as standardization, normalization etc. After that, feature extraction technique is implemented on the dataset for improving the accuracy and finally dataset worked on data mining and fuzzy logic various classification algorithms. While analyzing different data mining methods, the accuracy computed through random forest classifiers as high as 99.7% and in case of numerous fuzzy logic approaches, high precision and low complexity was found to contribute a fairly high accuracy of 96%.
{"title":"Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis","authors":"Harshil Thakkar , Vaishnavi Shah , Hiteshri Yagnik , Manan Shah","doi":"10.1016/j.ceh.2020.11.001","DOIUrl":"https://doi.org/10.1016/j.ceh.2020.11.001","url":null,"abstract":"<div><p>Diabetes is an ailment in which glucose level increase in at high rates in blood due to body’s inability to metabolize it. This happens when body does not produce sufficient amount of insulin or it does not respond to it properly. Critical and long-term health issues arise if diabetes is not handled or properly treated which includes: heart problems, disorders of the lungs, skin and liver complications, nerve damage, etc. With increasing number of diabetic patients, its early detection becomes essential. In this paper, our major focus areas are data mining and fuzzy logic techniques used in diabetes diagnosis. Data mining is used for locating patterns in huge datasets using a composition of different methods of machine learning, database manipulations and statistics. Data mining offers a lot of methods to inspect large data considering the expected outcome to find the hidden knowledge. Fuzzy logic is similar to human reasoning system and hence it can handle the uncertainties found in the data of medical diagnosis. These systems are called expert systems. The fuzzy expert systems (FES) analyze the knowledge from the available data which might be vague and suggests linguistic concept with huge approximation as its core to medical texts. In this paper, the methodology section delivers the pipeline of various tasks such as selecting the dataset, preprocessing the data by applying numerous methods such as standardization, normalization etc. After that, feature extraction technique is implemented on the dataset for improving the accuracy and finally dataset worked on data mining and fuzzy logic various classification algorithms. While analyzing different data mining methods, the accuracy computed through random forest classifiers as high as 99.7% and in case of numerous fuzzy logic approaches, high precision and low complexity was found to contribute a fairly high accuracy of 96%.</p></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"4 ","pages":"Pages 12-23"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ceh.2020.11.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90006305","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}
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