Pub Date : 2021-10-01DOI: 10.4018/IJHISI.20211001.OA22
Turki Alzahrani, M. Hunt, R. J. Whiddett
This research explores the principle barriers to and facilitators of the use of Smart Home Technology, telemonitoring and telemedicine systems to support healthcare and enable older adults to maintain their independence. The research focuses on organizational rather than technological issues. Semi-structured interviews explored the perspectives of three populations of stakeholders (N = 17): managers of rest homes/retirement villages, technology developers in a university setting and older adults (age 65 years and older). Key barriers to and facilitators of adoption are identified for the stakeholder groups. The results indicate that a lack of information about the capabilities and availability of the technologies is a key barrier to adoption. Other issues identified in previous studies are also found to be relevant, such as costs, platform management and infrastructure, and human issues such as privacy. The research provides practical recommendations for directions to be explored by developers and researchers in New Zealand and elsewhere.
{"title":"Barriers and Facilitators to Using Smart Home Technologies to Support Older Adults: Perspectives of Three Stakeholder Groups","authors":"Turki Alzahrani, M. Hunt, R. J. Whiddett","doi":"10.4018/IJHISI.20211001.OA22","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA22","url":null,"abstract":"This research explores the principle barriers to and facilitators of the use of Smart Home Technology, telemonitoring and telemedicine systems to support healthcare and enable older adults to maintain their independence. The research focuses on organizational rather than technological issues. Semi-structured interviews explored the perspectives of three populations of stakeholders (N = 17): managers of rest homes/retirement villages, technology developers in a university setting and older adults (age 65 years and older). Key barriers to and facilitators of adoption are identified for the stakeholder groups. The results indicate that a lack of information about the capabilities and availability of the technologies is a key barrier to adoption. Other issues identified in previous studies are also found to be relevant, such as costs, platform management and infrastructure, and human issues such as privacy. The research provides practical recommendations for directions to be explored by developers and researchers in New Zealand and elsewhere.","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":"132963000","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.oa15
Sachin Kuberkar, T. Singhal
Owing chiefly to the lack of suitable technology solutions, India is experiencing both shortage and wastage of blood units. In addressing such a challenge, we explore the unique role of Blockchain and Internet-of-things technologies in the overall blood supply chain management as an appropriate technology solution. Our study employs an integrated Task-Technology Fit and Technology Acceptance Model to empirically test and identify key factors influencing the adoption intention of the Blockchain and Internet-of-things enabled system. With the need to preserve donor and recipient data integrity and data privacy, the respective state and national health departments strictly regulate blood banks. Accordingly, our study also explores the role of government in supporting and overseeing security concerns in the future adoption of the Blockchain and Internet-of-things technologies. Finally, a solution based on the Blockchain and Internet-of-things technologies to ensure the sufficient availability of blood units at the national level is envisioned.
{"title":"Factors Influencing the Adoption Intention of Blockchain and Internet-of-Things Technologies for Sustainable Blood Bank Management","authors":"Sachin Kuberkar, T. Singhal","doi":"10.4018/ijhisi.20211001.oa15","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa15","url":null,"abstract":"Owing chiefly to the lack of suitable technology solutions, India is experiencing both shortage and wastage of blood units. In addressing such a challenge, we explore the unique role of Blockchain and Internet-of-things technologies in the overall blood supply chain management as an appropriate technology solution. Our study employs an integrated Task-Technology Fit and Technology Acceptance Model to empirically test and identify key factors influencing the adoption intention of the Blockchain and Internet-of-things enabled system. With the need to preserve donor and recipient data integrity and data privacy, the respective state and national health departments strictly regulate blood banks. Accordingly, our study also explores the role of government in supporting and overseeing security concerns in the future adoption of the Blockchain and Internet-of-things technologies. Finally, a solution based on the Blockchain and Internet-of-things technologies to ensure the sufficient availability of blood units at the national level is envisioned.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"4 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":"123662553","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.oa34
Vanita Singh, Vedant Dev
COVID-19 pandemic mandates the transformation of the traditional healthcare delivery model from facility-based to virtual care worldwide. The use of technology in delivering healthcare has always been debated and faces challenges as patients as well as providers are often resistive to change. To date, studies focusing on one's intention to use technology have gained significant research attention. Using the technology adoption model as a research framework, a sample of 336 individuals within the age group of 18-70 years were surveyed via online to understand their intention to use telemedicine. Data were analyzed using structural equation modeling. The findings suggest that perceived ease of use is a significant determinant of one's intention to use telemedicine vis-a-vis its effect on perceived usefulness and attitude towards telemedicine use. The attitude towards telemedicine is significantly affected by privacy concerns and outcome beliefs. The study results have implications for health policymakers and others when implementing telemedicine for today's healthcare delivery.
{"title":"Telemedicine Adoption in India: Identifying Factors Affecting Intention to Use","authors":"Vanita Singh, Vedant Dev","doi":"10.4018/IJHISI.20211001.oa34","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.oa34","url":null,"abstract":"COVID-19 pandemic mandates the transformation of the traditional healthcare delivery model from facility-based to virtual care worldwide. The use of technology in delivering healthcare has always been debated and faces challenges as patients as well as providers are often resistive to change. To date, studies focusing on one's intention to use technology have gained significant research attention. Using the technology adoption model as a research framework, a sample of 336 individuals within the age group of 18-70 years were surveyed via online to understand their intention to use telemedicine. Data were analyzed using structural equation modeling. The findings suggest that perceived ease of use is a significant determinant of one's intention to use telemedicine vis-a-vis its effect on perceived usefulness and attitude towards telemedicine use. The attitude towards telemedicine is significantly affected by privacy concerns and outcome beliefs. The study results have implications for health policymakers and others when implementing telemedicine for today's healthcare delivery.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"9 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":"121967368","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}
There is an accelerated migration from on-premise ERP to Cloud ERP systems in many industries, but this transition is relatively slow in the healthcare industry. To address this concern, we developed a research model based on Technology-Organization-Environment (TOE) framework, and explored it in the healthcare industry through semi-structured interviews with IT managers and finance managers. We found noticeable differences between small-sized and large-sized healthcare organizations, as well as the perceptions of IT managers and finance managers in Cloud ERP adoption decisions. We discussed these findings, and proposed future research questions on Cloud ERP adoption in the healthcare industry.
{"title":"Investigation of Cloud ERP Adoption in the Healthcare Industry Through Technology-Organization-Environment (TOE) Framework: Qualitative Study","authors":"Uzay Damali, M. Kocakulah, A. Ozkul","doi":"10.4018/ijhisi.289463","DOIUrl":"https://doi.org/10.4018/ijhisi.289463","url":null,"abstract":"There is an accelerated migration from on-premise ERP to Cloud ERP systems in many industries, but this transition is relatively slow in the healthcare industry. To address this concern, we developed a research model based on Technology-Organization-Environment (TOE) framework, and explored it in the healthcare industry through semi-structured interviews with IT managers and finance managers. We found noticeable differences between small-sized and large-sized healthcare organizations, as well as the perceptions of IT managers and finance managers in Cloud ERP adoption decisions. We discussed these findings, and proposed future research questions on Cloud ERP adoption in the healthcare industry.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"32 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":"125458904","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}
Blockchain technology has garnered attention from stakeholders in many domains, including healthcare, governance and supply chain management. In the context of healthcare, traceability of pharmaceutical drugs in a transparent yet secure manner can be made faster and efficient with blockchain. This paper presents a blockchain based solution for traceability known as PharmaChain. The traceability is achieved with application design and algorithms which are proposed in the work. The proposed application can be developed using hyperledger fabric deployed on dockers. The chain codes are written in javascript. The pharmaceutical blockchain proposed in this work consists of manufacturer, wholesaler, retailer and consumer. The right for registering a drug into the blockchain is granted to the manufacturers only and the ownership transfer of the drug is stored. This paper highlights the traceability of ownership transfer of the drug and validates its origin.
{"title":"Blockchain Application Design and Algorithms for Traceability in Pharmaceutical Supply Chain","authors":"V. Bali, Pawan Soni, Tejaswi Khanna, Shivam Gupta, Shivi Chauhan, Shivani Gupta","doi":"10.4018/ijhisi.289460","DOIUrl":"https://doi.org/10.4018/ijhisi.289460","url":null,"abstract":"Blockchain technology has garnered attention from stakeholders in many domains, including healthcare, governance and supply chain management. In the context of healthcare, traceability of pharmaceutical drugs in a transparent yet secure manner can be made faster and efficient with blockchain. This paper presents a blockchain based solution for traceability known as PharmaChain. The traceability is achieved with application design and algorithms which are proposed in the work. The proposed application can be developed using hyperledger fabric deployed on dockers. The chain codes are written in javascript. The pharmaceutical blockchain proposed in this work consists of manufacturer, wholesaler, retailer and consumer. The right for registering a drug into the blockchain is granted to the manufacturers only and the ownership transfer of the drug is stored. This paper highlights the traceability of ownership transfer of the drug and validates its origin.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"1 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":"116865997","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.OA11
Adnan Muhammad Shah, Xiangbin Yan, Syed Asad Ali Shah, R. Ullah
Online reviews generated by patients on physician rating Websites (PRWs) have recently received much attention from physicians and their patients. In these reviews, patients exchange opinions as a diverse set of topics regarding different aspects of healthcare quality. This study aimed to propose a novel service quality-based text analytics (SQTA) model with other qualitative methods to mine different aspects of physicians and their clinical relevance in choosing a good doctor. Data included 45,560 online reviews that the authors scraped from a U.S.-based PRW (Healthgrades.com). The resulting topics demonstrate excellent classification results across different disease ranks, with overall accuracy and recall of 98%. The proposed classifier’s performance was 3% better than the existing topic classification methods applied in previous studies. The resulting clinically informative topics could help patients and physicians to maximize the usefulness of online reviews for efficient clinical decisions and improving the quality of care.
{"title":"Exploring Important Aspects of Service Quality While Choosing a Good Doctor: A Mixed-Methods Approach","authors":"Adnan Muhammad Shah, Xiangbin Yan, Syed Asad Ali Shah, R. Ullah","doi":"10.4018/IJHISI.20211001.OA11","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA11","url":null,"abstract":"Online reviews generated by patients on physician rating Websites (PRWs) have recently received much attention from physicians and their patients. In these reviews, patients exchange opinions as a diverse set of topics regarding different aspects of healthcare quality. This study aimed to propose a novel service quality-based text analytics (SQTA) model with other qualitative methods to mine different aspects of physicians and their clinical relevance in choosing a good doctor. Data included 45,560 online reviews that the authors scraped from a U.S.-based PRW (Healthgrades.com). The resulting topics demonstrate excellent classification results across different disease ranks, with overall accuracy and recall of 98%. The proposed classifier’s performance was 3% better than the existing topic classification methods applied in previous studies. The resulting clinically informative topics could help patients and physicians to maximize the usefulness of online reviews for efficient clinical decisions and improving the quality of care.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"10 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":"125524579","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}
In the medical field, the diagnostic phase is the most important, as the entire treatment process will be based on this step. Oncological diseases such as breast cancer require a precise anatomopathological study accompanied most of the time by an immunohistochemical study whose goal is to know the sensitivity of tumor tissues to hormone therapy and targeted therapy. This study relies on antibodies and their interpretation requires significant time and as it can suffer from poor reproducibility which negatively influences the treatment stage. In this work, the objective is to classify histopathological images stained with E-cadherin antibody to help pathologists in their work in order to facilitate oncologists in the choice of the most appropriate therapeutic protocol. The realization of this task is based on the choice of transfer learning as techniques and data augmentation due to the minimal number of images gathered. The results obtained are very satisfying both on accuracy where we reached a rate of 97.27% with a reduced number of parameters and very close to our basic model.
{"title":"Transfer Learning for Highlighting Diagnosis in Pathological Anatomy Based on Immunohistochemistry","authors":"M. Gasmi, Issam Bendib, Yasmina Benmabrouk","doi":"10.4018/ijhisi.301232","DOIUrl":"https://doi.org/10.4018/ijhisi.301232","url":null,"abstract":"In the medical field, the diagnostic phase is the most important, as the entire treatment process will be based on this step. Oncological diseases such as breast cancer require a precise anatomopathological study accompanied most of the time by an immunohistochemical study whose goal is to know the sensitivity of tumor tissues to hormone therapy and targeted therapy. This study relies on antibodies and their interpretation requires significant time and as it can suffer from poor reproducibility which negatively influences the treatment stage. In this work, the objective is to classify histopathological images stained with E-cadherin antibody to help pathologists in their work in order to facilitate oncologists in the choice of the most appropriate therapeutic protocol. The realization of this task is based on the choice of transfer learning as techniques and data augmentation due to the minimal number of images gathered. The results obtained are very satisfying both on accuracy where we reached a rate of 97.27% with a reduced number of parameters and very close to our basic model.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"818 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":"132116833","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.OA13
M. Sood, Arun Aggarwal, Sahil Gupta, S. Rastogi
{"title":"Identifying Factors of Indian Health System and Their Influence for Providing Good Customer Care","authors":"M. Sood, Arun Aggarwal, Sahil Gupta, S. Rastogi","doi":"10.4018/IJHISI.20211001.OA13","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA13","url":null,"abstract":"","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":"130970545","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.OA10
M. Alghobiri, H. Khan, Ahsan Mahmood
The human liver is one of the major organs in the body and liver disease can cause many problems in human live. Due to the increase in liver disease, various data mining techniques are proposed by the researchers to predict the liver disease. These techniques are improving day by day in order to predict and diagnose the liver disease in human. In this paper, real-world liver disease dataset is incorporated for diagnosing liver disease in human body. For this purpose, feature selection models are used to select a number of features that best are the most important feature to diagnose the liver disease. After selecting features and splitting data for training and testing, different classification algorithms in terms of naive Bayes, supervised vector machine, decision tree, k near neighbor and logistic regression models to diagnose the liver disease in human body. The results are cross-validated by tenfold cross validation methods and achieve an accuracy as good as 93%.
{"title":"An Empirical Comparative Analysis Using Machine Learning Techniques for Liver Disease Prediction","authors":"M. Alghobiri, H. Khan, Ahsan Mahmood","doi":"10.4018/IJHISI.20211001.OA10","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA10","url":null,"abstract":"The human liver is one of the major organs in the body and liver disease can cause many problems in human live. Due to the increase in liver disease, various data mining techniques are proposed by the researchers to predict the liver disease. These techniques are improving day by day in order to predict and diagnose the liver disease in human. In this paper, real-world liver disease dataset is incorporated for diagnosing liver disease in human body. For this purpose, feature selection models are used to select a number of features that best are the most important feature to diagnose the liver disease. After selecting features and splitting data for training and testing, different classification algorithms in terms of naive Bayes, supervised vector machine, decision tree, k near neighbor and logistic regression models to diagnose the liver disease in human body. The results are cross-validated by tenfold cross validation methods and achieve an accuracy as good as 93%.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"15 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":"115346382","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.OA24
Sudeep D. Thepade, Gaurav Ramnani
Melanoma is a mortal type of skin cancer. Early detection of melanoma significantly improves the patient’s chances of survival. Detection of melanoma at an early juncture demands expert doctors. The scarcity of such expert doctors is a major issue with healthcare systems globally. Computer-assisted diagnostics may prove helpful in this case. This paper proposes a health informatics system for melanoma identification using machine learning with dermoscopy skin images. In the proposed method, the features of dermoscopy skin images are extracted using the Haar wavelet pyramid various levels. These features are employed to train machine learning algorithms and ensembles for melanoma identification. The consideration of higher levels of Haar Wavelet Pyramid helps speed up the identification process. It is observed that the performance gradually improves from the Haar wavelet pyramid level 4x4 to 16x16, and shows marginal improvement further. The ensembles of machine learning algorithms have shown a boost in performance metrics compared to the use of individual machine learning algorithms.
{"title":"Haar Wavelet Pyramid-Based Melanoma Skin Cancer Identification With Ensemble of Machine Learning Algorithms","authors":"Sudeep D. Thepade, Gaurav Ramnani","doi":"10.4018/IJHISI.20211001.OA24","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA24","url":null,"abstract":"Melanoma is a mortal type of skin cancer. Early detection of melanoma significantly improves the patient’s chances of survival. Detection of melanoma at an early juncture demands expert doctors. The scarcity of such expert doctors is a major issue with healthcare systems globally. Computer-assisted diagnostics may prove helpful in this case. This paper proposes a health informatics system for melanoma identification using machine learning with dermoscopy skin images. In the proposed method, the features of dermoscopy skin images are extracted using the Haar wavelet pyramid various levels. These features are employed to train machine learning algorithms and ensembles for melanoma identification. The consideration of higher levels of Haar Wavelet Pyramid helps speed up the identification process. It is observed that the performance gradually improves from the Haar wavelet pyramid level 4x4 to 16x16, and shows marginal improvement further. The ensembles of machine learning algorithms have shown a boost in performance metrics compared to the use of individual machine learning algorithms.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"202 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":"116174765","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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