Pub Date : 2023-11-12DOI: 10.1007/s12553-023-00790-y
Manju Lata Sahu, Mithilesh Atulkar, Mitul Kumar Ahirwal, Afsar Ahamad
{"title":"Internet-of-things based machine learning enabled medical decision support system for prediction of health issues","authors":"Manju Lata Sahu, Mithilesh Atulkar, Mitul Kumar Ahirwal, Afsar Ahamad","doi":"10.1007/s12553-023-00790-y","DOIUrl":"https://doi.org/10.1007/s12553-023-00790-y","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"75 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135037525","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 : 2023-11-10DOI: 10.1007/s12553-023-00788-6
Jingsong Chen, Bráulio Alturas
Abstract Purpose This work presents a case study of the University of Hong Kong-Shenzhen Hospital (HKU-SZH), which was the first to implement an outpatient appointments registration system. The research question is to determine which factors influence patient satisfaction most. Methods The study provides an anatomy of the hospital outpatient process through various methods and theories, including a literature review, field research, expert consultation, business process improvement (BPI) theory and information technology, with the aim of identifying the objectives and strategies of the hospital for improving its outpatient process. A quantitative analysis was performed using a questionnaire survey to identify the defects and weaknesses of the current model. The principles, methods and techniques of BPI theory are used to analyse various problems existing in the outpatient process and the extent of their influence. A structural equation model has been established for scientific and quantitative analysis, which can help identify the goals of optimization and measure improvement in the outpatient process and patient satisfaction. Results It was determined the source of inefficiency of the current outpatient service process. By means of outpatient process improvement, the study aims to increase the hospital’s efficiency and raise the level of patient satisfaction so that it may enhance its comprehensive competence. In addition, an effective and operable methodology will be generated, which is expected to serve as a reference for other hospitals to improve their operation and management. Conclusions It was found that service attitude, service value and waiting time have a significant influence on patient satisfaction.
{"title":"Improvement of outpatient service processes: a case study of the university of Hong Kong-Shenzhen hospital","authors":"Jingsong Chen, Bráulio Alturas","doi":"10.1007/s12553-023-00788-6","DOIUrl":"https://doi.org/10.1007/s12553-023-00788-6","url":null,"abstract":"Abstract Purpose This work presents a case study of the University of Hong Kong-Shenzhen Hospital (HKU-SZH), which was the first to implement an outpatient appointments registration system. The research question is to determine which factors influence patient satisfaction most. Methods The study provides an anatomy of the hospital outpatient process through various methods and theories, including a literature review, field research, expert consultation, business process improvement (BPI) theory and information technology, with the aim of identifying the objectives and strategies of the hospital for improving its outpatient process. A quantitative analysis was performed using a questionnaire survey to identify the defects and weaknesses of the current model. The principles, methods and techniques of BPI theory are used to analyse various problems existing in the outpatient process and the extent of their influence. A structural equation model has been established for scientific and quantitative analysis, which can help identify the goals of optimization and measure improvement in the outpatient process and patient satisfaction. Results It was determined the source of inefficiency of the current outpatient service process. By means of outpatient process improvement, the study aims to increase the hospital’s efficiency and raise the level of patient satisfaction so that it may enhance its comprehensive competence. In addition, an effective and operable methodology will be generated, which is expected to serve as a reference for other hospitals to improve their operation and management. Conclusions It was found that service attitude, service value and waiting time have a significant influence on patient satisfaction.","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"81 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135091918","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 : 2023-11-06DOI: 10.1007/s12553-023-00786-8
Basile Njei, Ulrick Sidney Kanmounye, Mouhand F. Mohamed, Anim Forjindam, Nkafu Bechem Ndemazie, Adedeji Adenusi, Stella-Maris C. Egboh, Evaristus S. Chukwudike, Joao Filipe G. Monteiro, Tyler M. Berzin, Akwi W. Asombang
{"title":"Artificial intelligence for healthcare in Africa: a scientometric analysis","authors":"Basile Njei, Ulrick Sidney Kanmounye, Mouhand F. Mohamed, Anim Forjindam, Nkafu Bechem Ndemazie, Adedeji Adenusi, Stella-Maris C. Egboh, Evaristus S. Chukwudike, Joao Filipe G. Monteiro, Tyler M. Berzin, Akwi W. Asombang","doi":"10.1007/s12553-023-00786-8","DOIUrl":"https://doi.org/10.1007/s12553-023-00786-8","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135634428","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 : 2023-11-06DOI: 10.1007/s12553-023-00787-7
Rakesh Kumar Patnaik, Yu-Chen Lin, Ming Chih Ho, J. Andrew Yeh
Abstract Purpose Breath profiling has gained importance in recent years as it is a non-invasive technique to identify biomarkers for various diseases. Breath profiling of abnormal liver function in individuals for identifying potential biomarkers in exhaled breath could be a useful diagnostic tool. The objective of this study was to identify potential biomarkers in exhaled breath that remain stable and consistent during different physiological states, including rest and brief workouts, intending to develop a non-invasive diagnostic tool for detecting abnormal liver function. Method Our study employed a gas chromatography and mass-spectrometer quantified dataset for analysis. Machine learning techniques, including feature selection and model training, were used to rank and evaluate potential biomarkers' contributions to the model's performance. Statistical methods were applied to filter significant and consistent biomarkers. The final selected biomarkers were iterated for all possible combinations using machine learning algorithms to determine their accuracy range. Furthermore, classification models were used to evaluate the performance metrics of the biomarkers and compare models. Result The final selected biomarkers, including 2-Myristynoyl Pantetheine, Pterin-6 Carboxylic Acid, Methyl Mercaptan, N-Acetyl Cysteine, and Butyric Acid, exhibited stable levels in exhaled breath during different physiological states. They showed high accuracy and precision in detecting abnormal liver function. Our machine learning models achieved an accuracy rate ranging from 0.7 to 0.95 in all conditions, with precision, recall, prediction probability, and a 95% confidence interval ranging from 0.84 to 0.94, using various combinations of these biomarkers. Conclusion Our statistical and machine learning analysis identified significant and potential biomarkers that contribute to the detection of abnormal liver function. These biomarkers were consistent across different physiological states of the body in both patient and healthy groups. The use of breath samples and feature selection machine learning methods proved to be an accurate and reliable approach for identifying these biomarkers. Our findings provide valuable insights for future research in this field and can inform the development of non-invasive and cost-effective diagnostic tests for liver disease.
{"title":"Selection of consistent breath biomarkers of abnormal liver function using feature selection: a pilot study","authors":"Rakesh Kumar Patnaik, Yu-Chen Lin, Ming Chih Ho, J. Andrew Yeh","doi":"10.1007/s12553-023-00787-7","DOIUrl":"https://doi.org/10.1007/s12553-023-00787-7","url":null,"abstract":"Abstract Purpose Breath profiling has gained importance in recent years as it is a non-invasive technique to identify biomarkers for various diseases. Breath profiling of abnormal liver function in individuals for identifying potential biomarkers in exhaled breath could be a useful diagnostic tool. The objective of this study was to identify potential biomarkers in exhaled breath that remain stable and consistent during different physiological states, including rest and brief workouts, intending to develop a non-invasive diagnostic tool for detecting abnormal liver function. Method Our study employed a gas chromatography and mass-spectrometer quantified dataset for analysis. Machine learning techniques, including feature selection and model training, were used to rank and evaluate potential biomarkers' contributions to the model's performance. Statistical methods were applied to filter significant and consistent biomarkers. The final selected biomarkers were iterated for all possible combinations using machine learning algorithms to determine their accuracy range. Furthermore, classification models were used to evaluate the performance metrics of the biomarkers and compare models. Result The final selected biomarkers, including 2-Myristynoyl Pantetheine, Pterin-6 Carboxylic Acid, Methyl Mercaptan, N-Acetyl Cysteine, and Butyric Acid, exhibited stable levels in exhaled breath during different physiological states. They showed high accuracy and precision in detecting abnormal liver function. Our machine learning models achieved an accuracy rate ranging from 0.7 to 0.95 in all conditions, with precision, recall, prediction probability, and a 95% confidence interval ranging from 0.84 to 0.94, using various combinations of these biomarkers. Conclusion Our statistical and machine learning analysis identified significant and potential biomarkers that contribute to the detection of abnormal liver function. These biomarkers were consistent across different physiological states of the body in both patient and healthy groups. The use of breath samples and feature selection machine learning methods proved to be an accurate and reliable approach for identifying these biomarkers. Our findings provide valuable insights for future research in this field and can inform the development of non-invasive and cost-effective diagnostic tests for liver disease.","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"10 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135590141","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 : 2023-11-01DOI: 10.1007/s12553-023-00799-3
Emmanuel Fiagbedzi, Francis Hasford, S. Tagoe, Andrew Nisbet
{"title":"Radiotherapy infrastructure for brain metastasis treatment in Africa: practical guildelines for implementation of a stereotactic radiosurgery (SRS) program","authors":"Emmanuel Fiagbedzi, Francis Hasford, S. Tagoe, Andrew Nisbet","doi":"10.1007/s12553-023-00799-3","DOIUrl":"https://doi.org/10.1007/s12553-023-00799-3","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"63 1","pages":"893 - 904"},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139292296","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 : 2023-11-01DOI: 10.1007/s12553-023-00792-w
Muhammad Aizaz, Faisal Khan, Babar Ali, Shahbaz Ahmad, Khansa Naseem, Smriti Mishra, Farrakh Ali Abbas, Guiwen Yang
{"title":"Significance of Digital Health Technologies (DHTs) to manage communicable and non-communicable diseases in Low and Middle-Income Countries (LMICs)","authors":"Muhammad Aizaz, Faisal Khan, Babar Ali, Shahbaz Ahmad, Khansa Naseem, Smriti Mishra, Farrakh Ali Abbas, Guiwen Yang","doi":"10.1007/s12553-023-00792-w","DOIUrl":"https://doi.org/10.1007/s12553-023-00792-w","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"13 1","pages":"883 - 892"},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139301681","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 : 2023-11-01DOI: 10.1007/s12553-023-00791-x
M. Abuzaid, W. Elshami, Ali Abdelrazig, Sonyia McFadden
{"title":"Direct digital radiography: Exploring applications, misuse, and training needs in medical imaging","authors":"M. Abuzaid, W. Elshami, Ali Abdelrazig, Sonyia McFadden","doi":"10.1007/s12553-023-00791-x","DOIUrl":"https://doi.org/10.1007/s12553-023-00791-x","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"71 1","pages":"1025 - 1032"},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139291771","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 : 2023-11-01DOI: 10.1007/s12553-023-00789-5
Paul Guillot, M. Bøgsted, C. Vesteghem
{"title":"FAIR sharing of health data: a systematic review of applicable solutions","authors":"Paul Guillot, M. Bøgsted, C. Vesteghem","doi":"10.1007/s12553-023-00789-5","DOIUrl":"https://doi.org/10.1007/s12553-023-00789-5","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"43 1","pages":"869 - 882"},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139296312","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 : 2023-10-31DOI: 10.1007/s12553-023-00774-y
Francisco Reyes-Santias, Octavio Cordova-Arevalo, Ivan Busto Dominguez, Manel Antelo
Abstract Purpose This article analyses the factors influencing the uptake of computed tomography (CT) and magnetic resonance imaging (MRI) technologies by a sample of private hospitals located in Galicia-North of Portugal European Region. Methods Regarding adoption, associations with the different variables were analysed by means of binary logistic regression for CT and MRI of data from 24 private hospitals for the period 2006–2019. The sample data used to perform the regression analyses were panel data (Wooldridge in Econometric Analysis of Cross Section and Panel Data, Cambridge, Massachusetts, 1) and statistical significance was established at p ≤ 0.05. Results We find that hospital size, proxied by the number of beds, best explains the decision to adopt CT technology, while the only sociodemographic variable that affects the adoption decision is age above 64 years. Hospital size is also the main explanatory variable for MRI technology adoption, and in this case, all sociodemographic variables, except for population density, affect the adoption decision. Conclusions The availability of a CT scanner reduces the probability of a private hospital adopting MRI technology. Contracts with Public Sector have a counterfactual effect on CT uptake and a negative influence on MRI uptake.
摘要目的分析影响葡萄牙北部加利西亚地区私立医院CT和MRI技术应用的因素。方法对2006-2019年24家民营医院的CT和MRI数据进行二元logistic回归分析，分析采用率与不同变量的相关性。进行回归分析的样本数据为面板数据(Wooldridge in Econometric Analysis of Cross Section and panel data, Cambridge, Massachusetts, 1)， p≤0.05具有统计学显著性。结果我们发现医院规模(以床位数量为代表)最能解释采用CT技术的决定，而影响采用决策的唯一社会人口变量是64岁以上的年龄。医院规模也是MRI技术采用的主要解释变量，在这种情况下，除人口密度外，所有社会人口统计学变量都会影响采用决策。结论CT扫描仪的可用性降低了私立医院采用MRI技术的可能性。与公共部门的合同对CT吸收有反事实效应，对MRI吸收有负面影响。
{"title":"Factors influencing medical imaging technology uptake by private hospitals","authors":"Francisco Reyes-Santias, Octavio Cordova-Arevalo, Ivan Busto Dominguez, Manel Antelo","doi":"10.1007/s12553-023-00774-y","DOIUrl":"https://doi.org/10.1007/s12553-023-00774-y","url":null,"abstract":"Abstract Purpose This article analyses the factors influencing the uptake of computed tomography (CT) and magnetic resonance imaging (MRI) technologies by a sample of private hospitals located in Galicia-North of Portugal European Region. Methods Regarding adoption, associations with the different variables were analysed by means of binary logistic regression for CT and MRI of data from 24 private hospitals for the period 2006–2019. The sample data used to perform the regression analyses were panel data (Wooldridge in Econometric Analysis of Cross Section and Panel Data, Cambridge, Massachusetts, 1) and statistical significance was established at p ≤ 0.05. Results We find that hospital size, proxied by the number of beds, best explains the decision to adopt CT technology, while the only sociodemographic variable that affects the adoption decision is age above 64 years. Hospital size is also the main explanatory variable for MRI technology adoption, and in this case, all sociodemographic variables, except for population density, affect the adoption decision. Conclusions The availability of a CT scanner reduces the probability of a private hospital adopting MRI technology. Contracts with Public Sector have a counterfactual effect on CT uptake and a negative influence on MRI uptake.","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"77 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135870235","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 : 2023-10-23DOI: 10.1007/s12553-023-00785-9
Aisyah Rahimi, Azira Khalil, Shahrina Ismail, Aminatul Saadiah Abdul Jamil, Muhammad Mokhzaini Azizan, Khin Wee Lai, Amir Faisal
{"title":"Trimodality image registration of ultrasound, cardiac computed tomography, and magnetic resonance imaging for transcatheter aortic valve implantation and replacement image guidance","authors":"Aisyah Rahimi, Azira Khalil, Shahrina Ismail, Aminatul Saadiah Abdul Jamil, Muhammad Mokhzaini Azizan, Khin Wee Lai, Amir Faisal","doi":"10.1007/s12553-023-00785-9","DOIUrl":"https://doi.org/10.1007/s12553-023-00785-9","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135405163","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}
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