M. C. Caccia-Bava, Sarah L. Smith, J. Mabry, T. Guimaraes
Hospital administrators are motivated to improve hospital quality in the eyes of their patients, healthcare quality regulators, and accrediting organizations. This study empirically tests the importance of some strategic determinants of hospital quality by collecting data using an emailed questionnaire filled by 258 chief quality officers. The results supported the importance of competitive intelligence, strategic leadership, management of technology, specific characteristics of the change process, and organization culture as major determinants of hospital quality. Based on the results, the report makes recommendations on where to direct their efforts, including understanding how to measure these important factors. The important model tested here has not been proposed before and provides several research opportunities for perhaps expanding the model and account for unexplained variance in hospital quality, including other constructs potentially being moderators and mediators for the hypothesized relationships.
{"title":"Testing Hospital Quality Strategic Determinants","authors":"M. C. Caccia-Bava, Sarah L. Smith, J. Mabry, T. Guimaraes","doi":"10.4018/ijhisi.314221","DOIUrl":"https://doi.org/10.4018/ijhisi.314221","url":null,"abstract":"Hospital administrators are motivated to improve hospital quality in the eyes of their patients, healthcare quality regulators, and accrediting organizations. This study empirically tests the importance of some strategic determinants of hospital quality by collecting data using an emailed questionnaire filled by 258 chief quality officers. The results supported the importance of competitive intelligence, strategic leadership, management of technology, specific characteristics of the change process, and organization culture as major determinants of hospital quality. Based on the results, the report makes recommendations on where to direct their efforts, including understanding how to measure these important factors. The important model tested here has not been proposed before and provides several research opportunities for perhaps expanding the model and account for unexplained variance in hospital quality, including other constructs potentially being moderators and mediators for the hypothesized relationships.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124806541","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":"Book Review: Adaptive Health Management Information Systems 4th Edition","authors":"M. Hall","doi":"10.4018/ijhisi.313605","DOIUrl":"https://doi.org/10.4018/ijhisi.313605","url":null,"abstract":"<p />","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129697749","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 : 2021-10-01DOI: 10.4018/ijhisi.20211001.oa16
L. Fetjah, K. Azbeg, O. Ouchetto, Said Jai-Andaloussi
With the rapid development in smart medical devices, Internet of things has a large applicability in healthcare sector. The current system is based on a centralized communication with cloud servers. However, this architecture increases security and privacy risks. This paper describes an architecture of a smart healthcare system for remote patient monitoring. To ensure security and privacy, the architecture uses the Blockchain technology. For data analysis, smart contracts and artificial intelligence are used. The architecture is divided into three layers: smart medical devices layer, fog layer and cloud layer. To validate the proposed approach, a scenario based on diabetes management system is described. The architecture is applied to provide remote diabetic patients monitoring. The system could suggest treatments, generate proactive predictions and predict future complications as well as alerting physicians in case of emergency.
{"title":"Towards a Smart Healthcare System: An Architecture Based on IoT, Blockchain, and Fog Computing","authors":"L. Fetjah, K. Azbeg, O. Ouchetto, Said Jai-Andaloussi","doi":"10.4018/ijhisi.20211001.oa16","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa16","url":null,"abstract":"With the rapid development in smart medical devices, Internet of things has a large applicability in healthcare sector. The current system is based on a centralized communication with cloud servers. However, this architecture increases security and privacy risks. This paper describes an architecture of a smart healthcare system for remote patient monitoring. To ensure security and privacy, the architecture uses the Blockchain technology. For data analysis, smart contracts and artificial intelligence are used. The architecture is divided into three layers: smart medical devices layer, fog layer and cloud layer. To validate the proposed approach, a scenario based on diabetes management system is described. The architecture is applied to provide remote diabetic patients monitoring. The system could suggest treatments, generate proactive predictions and predict future complications as well as alerting physicians in case of emergency.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125532520","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 : 2021-10-01DOI: 10.4018/ijhisi.20211001.oa25
P. Nagaraj, P. Deepalakshmi
Diabetes, caused by the rise in level of glucose in blood, has many latest devices to identify from blood samples. Diabetes, when unnoticed, may bring many serious diseases like heart attack, kidney disease. In this way, there is a requirement for solid research and learning model’s enhancement in the field of gestational diabetes identification and analysis. SVM is one of the powerful classification models in machine learning, and similarly, Deep Neural Network is powerful under deep learning models. In this work, we applied Enhanced Support Vector Machine and Deep Learning model Deep Neural Network for diabetes prediction and screening. The proposed method uses Deep Neural Network obtaining its input from the output of Enhanced Support Vector Machine, thus having a combined efficacy. The dataset we considered includes 768 patients’ data with eight major features and a target column with result “Positive” or “Negative”. Experiment is done with Python and the outcome of our demonstration shows that the deep Learning model gives more efficiency for diabetes prediction.
{"title":"Diabetes Prediction Using Enhanced SVM and Deep Neural Network Learning Techniques: An Algorithmic Approach for Early Screening of Diabetes","authors":"P. Nagaraj, P. Deepalakshmi","doi":"10.4018/ijhisi.20211001.oa25","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa25","url":null,"abstract":"Diabetes, caused by the rise in level of glucose in blood, has many latest devices to identify from blood samples. Diabetes, when unnoticed, may bring many serious diseases like heart attack, kidney disease. In this way, there is a requirement for solid research and learning model’s enhancement in the field of gestational diabetes identification and analysis. SVM is one of the powerful classification models in machine learning, and similarly, Deep Neural Network is powerful under deep learning models. In this work, we applied Enhanced Support Vector Machine and Deep Learning model Deep Neural Network for diabetes prediction and screening. The proposed method uses Deep Neural Network obtaining its input from the output of Enhanced Support Vector Machine, thus having a combined efficacy. The dataset we considered includes 768 patients’ data with eight major features and a target column with result “Positive” or “Negative”. Experiment is done with Python and the outcome of our demonstration shows that the deep Learning model gives more efficiency for diabetes prediction.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129496724","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}
Hafid Kadi, M. Rebbah, Boudjelal Meftah, O. Lézoray
Personalized medicine exploits the patient data, for example, genetic compositions, and key biomarkers. During the data mining process, the key challenges are the information loss, the data types heterogeneity and the time series representation. In this paper, a novel data representation model for personalized medicine is proposed in light of these challenges. The proposed model will account for the structured, temporal and non-temporal data and their types, namely, numeric, nominal, date, and Boolean. After the "Date and Boolean" data transformation, the nominal data are treated by dispersion while several clustering techniques are deployed to control the numeric data distribution. Ultimately, the transformation process results in three homogeneous representations with these representations having only two dimensions to ease the exploration of the represented dataset. Compared to the Symbolic Aggregate Approximation technique, the proposed model preserves the time-series information, conserves as much data as possible and offers multiple simple representations to be explored.
{"title":"A Data Representation Model for Personalized Medicine","authors":"Hafid Kadi, M. Rebbah, Boudjelal Meftah, O. Lézoray","doi":"10.4018/ijhisi.295822","DOIUrl":"https://doi.org/10.4018/ijhisi.295822","url":null,"abstract":"Personalized medicine exploits the patient data, for example, genetic compositions, and key biomarkers. During the data mining process, the key challenges are the information loss, the data types heterogeneity and the time series representation. In this paper, a novel data representation model for personalized medicine is proposed in light of these challenges. The proposed model will account for the structured, temporal and non-temporal data and their types, namely, numeric, nominal, date, and Boolean. After the \"Date and Boolean\" data transformation, the nominal data are treated by dispersion while several clustering techniques are deployed to control the numeric data distribution. Ultimately, the transformation process results in three homogeneous representations with these representations having only two dimensions to ease the exploration of the represented dataset. Compared to the Symbolic Aggregate Approximation technique, the proposed model preserves the time-series information, conserves as much data as possible and offers multiple simple representations to be explored.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129165318","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 : 2021-10-01DOI: 10.4018/ijhisi.20211001.oa19
Suwat Janyapoon, Jirapan Liangrokapart, Albert Tan
Business intelligence (BI) has become a popular among management executives of different industries. Many publications have mentioned Big Data and how to use data intelligently. However, little is known about how to successfully implement BI in the healthcare industry. The unique characteristic of this business, which focuses only on quality of care and patient safety, has a big impact on decision-making. This research is based on a literature review and empirical evidence collected from interviews with professionals involved in the healthcare industry. Twenty-four hospital executives and Information Technology staff who have direct or indirect experience with BI were interviewed. It investigates critical success factors for BI implementation in hospitals and provides insight into the healthcare industry in Thailand. The concept of grounded theory was applied for content analysis. Insights from this research contribute to academia and the healthcare industry by providing first-time evidence of specific factors for BI implementation and guidelines in hospitals.
{"title":"Critical Success Factors of Business Intelligence Implementation in Thai Hospitals","authors":"Suwat Janyapoon, Jirapan Liangrokapart, Albert Tan","doi":"10.4018/ijhisi.20211001.oa19","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa19","url":null,"abstract":"Business intelligence (BI) has become a popular among management executives of different industries. Many publications have mentioned Big Data and how to use data intelligently. However, little is known about how to successfully implement BI in the healthcare industry. The unique characteristic of this business, which focuses only on quality of care and patient safety, has a big impact on decision-making. This research is based on a literature review and empirical evidence collected from interviews with professionals involved in the healthcare industry. Twenty-four hospital executives and Information Technology staff who have direct or indirect experience with BI were interviewed. It investigates critical success factors for BI implementation in hospitals and provides insight into the healthcare industry in Thailand. The concept of grounded theory was applied for content analysis. Insights from this research contribute to academia and the healthcare industry by providing first-time evidence of specific factors for BI implementation and guidelines in hospitals.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124623194","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}
Epidemic spread poses a new challenge to the public health community. Given its very rapid spread, public health decision makers are mobilized to fight and stop it by setting disposal several tools. This ongoing research aims to design and develop a new system based on Multi-Agent System, Suscpetible-Infected-Removed (SIR) model and Geographic Information System (GIS) for public health officials. The proposed system aimed to find out the real and responsible factors for the epidemic spread and explaining its emergence in human population. Moreover, it allows to monitor the disease spread in space and time and provides rapid early warning alert of disease outbreaks. In this paper, a multi-agent epidemic spread simulation system is proposed, discussed and implemented. Simulation result shows that the proposed multi-agent disease spread system performs well in reflecting the evolution of dynamic disease spread system's behavior
{"title":"Dynamic Contact Network Simulation Model Based on Multi-Agent Systems","authors":"Fatima-Zohra Younsi, D. Hamdadou","doi":"10.4018/ijhisi.289462","DOIUrl":"https://doi.org/10.4018/ijhisi.289462","url":null,"abstract":"Epidemic spread poses a new challenge to the public health community. Given its very rapid spread, public health decision makers are mobilized to fight and stop it by setting disposal several tools. This ongoing research aims to design and develop a new system based on Multi-Agent System, Suscpetible-Infected-Removed (SIR) model and Geographic Information System (GIS) for public health officials. The proposed system aimed to find out the real and responsible factors for the epidemic spread and explaining its emergence in human population. Moreover, it allows to monitor the disease spread in space and time and provides rapid early warning alert of disease outbreaks. In this paper, a multi-agent epidemic spread simulation system is proposed, discussed and implemented. Simulation result shows that the proposed multi-agent disease spread system performs well in reflecting the evolution of dynamic disease spread system's behavior","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131959691","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 : 2021-10-01DOI: 10.4018/IJHISI.20211001.OA14
Itisha Gupta, Nisheeth Joshi
Negation is an important linguistic phenomenon that needs to be considered for identifying correct sentiments from the opinionated data available in digital form. It has the power to alter the polarity or strength of the polarity of affected words. In this paper, the authors present a survey on the negation role that has been done until now in sentiment analysis, specifically Twitter sentiment analysis. The authors discuss the various approaches of modelling negation in Twitter sentiment analysis. In particular, their focus is on negation scope detection and negation handling methods. This article also presents some of the challenges and limits of negation accounting in the field of Twitter sentiment analysis.
{"title":"A Review on Negation Role in Twitter Sentiment Analysis","authors":"Itisha Gupta, Nisheeth Joshi","doi":"10.4018/IJHISI.20211001.OA14","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA14","url":null,"abstract":"Negation is an important linguistic phenomenon that needs to be considered for identifying correct sentiments from the opinionated data available in digital form. It has the power to alter the polarity or strength of the polarity of affected words. In this paper, the authors present a survey on the negation role that has been done until now in sentiment analysis, specifically Twitter sentiment analysis. The authors discuss the various approaches of modelling negation in Twitter sentiment analysis. In particular, their focus is on negation scope detection and negation handling methods. This article also presents some of the challenges and limits of negation accounting in the field of Twitter sentiment analysis.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132786114","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}
To model the trajectory of the pandemic in Kuwait from February 24, 2020 to February 28, 2021, we used two modeling procedures: Auto Regressive Integrated Moving Average (ARIMA) with structural breaks and Multivariate Adaptive Regression Splines (MARS), and then mapped the key breakpoints of the models to the set of government-enforced interventions. The MARS model, as opposed to the ARIMA model, provides a more precise interpretation of the intervention's effects. It demonstrates that partial and total lockdown interventions were highly effective in reducing the number of confirmed cases. When some interventions, such as enforcing regional curfews, closing workplaces, and imposing travel restrictions, were combined, their impact became significant. MARS method is recommended to be applied when exploring the impact of interventions on the spread of a disease. It does not require any prior assumptions about the statistical distribution of data, does not affect data collinearity, has simple and transparent functions, and allows for a more accurate analysis of intervention results.
{"title":"Exploring the Impact of Government Interventions on COVID-19 Pandemic Spread in Kuwait","authors":"S. BuHamra, Jehad Al Dallal","doi":"10.4018/ijhisi.288893","DOIUrl":"https://doi.org/10.4018/ijhisi.288893","url":null,"abstract":"To model the trajectory of the pandemic in Kuwait from February 24, 2020 to February 28, 2021, we used two modeling procedures: Auto Regressive Integrated Moving Average (ARIMA) with structural breaks and Multivariate Adaptive Regression Splines (MARS), and then mapped the key breakpoints of the models to the set of government-enforced interventions. The MARS model, as opposed to the ARIMA model, provides a more precise interpretation of the intervention's effects. It demonstrates that partial and total lockdown interventions were highly effective in reducing the number of confirmed cases. When some interventions, such as enforcing regional curfews, closing workplaces, and imposing travel restrictions, were combined, their impact became significant. MARS method is recommended to be applied when exploring the impact of interventions on the spread of a disease. It does not require any prior assumptions about the statistical distribution of data, does not affect data collinearity, has simple and transparent functions, and allows for a more accurate analysis of intervention results.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121605327","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 : 2021-10-01DOI: 10.4018/ijhisi.20211001.oa33
A. Inthiran
Many health organizations use Facebook as a platform to engage with users. This has resulted in many research studies conducted on this platform. One popular type of research study is to characterize posts and measure user engagement levels . In this study, 100 post on the Mental Health Foundation of New Zealand Facebook page was analyzed this purpose. A mixed method approach was used. Quantitative descriptive statistics was used to analyze levels of engagement whilst qualitative content analysis was used to characterize posts into themes. Preliminary results indicate most posts fit in the awareness theme followed by the campaign theme. High levels of user engagements was observed for posts related to the awareness and others theme. Results of this study makes the suggestion for the implementation of intervention type awareness posts. A recommendation is also to made that the awareness posts promote mental health education and communication. This research study adds new knowledge to the area of posts characterization and user engagement levels on a mental health Facebook page.
{"title":"Posts Characterization and User Engagement: A Preliminary Study on a Mental Health Facebook Page in New Zealand","authors":"A. Inthiran","doi":"10.4018/ijhisi.20211001.oa33","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa33","url":null,"abstract":"Many health organizations use Facebook as a platform to engage with users. This has resulted in many research studies conducted on this platform. One popular type of research study is to characterize posts and measure user engagement levels . In this study, 100 post on the Mental Health Foundation of New Zealand Facebook page was analyzed this purpose. A mixed method approach was used. Quantitative descriptive statistics was used to analyze levels of engagement whilst qualitative content analysis was used to characterize posts into themes. Preliminary results indicate most posts fit in the awareness theme followed by the campaign theme. High levels of user engagements was observed for posts related to the awareness and others theme. Results of this study makes the suggestion for the implementation of intervention type awareness posts. A recommendation is also to made that the awareness posts promote mental health education and communication. This research study adds new knowledge to the area of posts characterization and user engagement levels on a mental health Facebook page.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127740295","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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