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dc.contributor.author
Panagiota, Galetsi
en
dc.date.accessioned
2021-02-08T08:35:59Z
dc.date.available
2021-02-08T08:35:59Z
dc.date.issued
2021-02-08
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29695
dc.rights
Default License
dc.subject
Big Data Analytics
en
dc.subject
Bibliometric Analysis
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dc.subject
Mhealth applications
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dc.subject
Consumer Behavior Analysis
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dc.subject
Healthcare analytics
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dc.title
Business analytics in healthcare operations and the use of mobile applications for decision making by health professionals
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heal.type
doctoralThesis
en_US
heal.creatorID.email
p.galetsi@ihu.edu.gr
heal.generalDescription
Doctoral thesis that contributes to the information 5 systems and operations management research, while empowers mobile health literature providing a better understanding of the matter. This study also provides a multi-layered analysis and aims to assist health professionals and health policy makers with a better understanding of how the development of an innovative data driven strategy can improve public health and the functioning of healthcare organizations but also how such a strategy creates challenges that need to be addressed in the near future to avoid societal malfunctions.
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heal.contributorName
Panagiota, Galetsi
en
heal.contributorID.email
p.galetsi@ihu.edu.gr
heal.dateAvailable
2021-02-08
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Economics, Business Administration and Legal Studies, PhD
en_US
heal.publicationDate
2021-02-06
heal.bibliographicCitation
"Galetsi, Panagiota"," Business analytics in healthcare operations and the use of mobile applications for decision making by health professionals ", "International Hellenic University", " School of Humanities, Social Sciences and Economics","2020"
en
heal.abstract
The emergence of powerful software in healthcare has created conditions and approaches for large datasets to be collected and analyzed which has led to informed decision-making towards tackling health issues. Big Data Analytics (BDA) in Healthcare, otherwise Health Analytics, express the analysis methods of the wide amount of electronic data related to patient healthcare and well-being that are very diverse and difficult to be measured by traditional software or hardware. This PhD Thesis includes two parts. The first part presents a systematic review using PRISMA of the research activity in Big Data Analytics (BDA) in the field of health and demonstrates the existing knowledge. The objective of this profiling study is to discuss this scientific field through related examples and to inform researchers about the nature and magnitude of the technological innovations in health information analysis tools, its influence, and where and how further material could be searched. With reference to the resource-based view theory this Doctoral Thesis has focused on how big data resources are utilised to create organization and social values, discussing the classification of big data types related to healthcare, the associate analysis techniques, the platforms and tools for handling big health data and the future aspects in the field. In recent years a large number of mobile health applications (mHealth) have been developed for medical practitioners and students that use apps and other digital technologies as part of their practice training and education. These trends have created a new social context in clinical diagnosis process based on technology innovation in the field. Inspired by this, the Second Part of the Doctoral Thesis aims to review available mHealth apps addressed to medical professionals and students designed to assist in the diagnosis process and explore the multiple dimensions of the research subject. Based on three conceptual frameworks, different approaches have been taken intending to investigate the social dimension of the intention of integration of mHealth innovation in the diagnosis process, explore the ethical challenges related to their data governance and reliability and explain how the specific consumers’ behaviour is affected by certain app characteristics and attributes. A special emphasis is placed on mHealth apps that use artificial intelligence and a future agenda is provided for the development of new apps for medical professionals with the use of responsible innovative methods. To investigate the relationships between app quality, downloads, features and users’ ratings multiple linear regression statistical analysis was used. The Thesis contributes to the information 5 systems and operations management research, while empowers mobile health literature providing a better understanding of the matter. This study also provides a multi-layered analysis and aims to assist health professionals and health policy makers with a better understanding of how the development of an innovative data driven strategy can improve public health and the functioning of healthcare organizations but also how such a strategy creates challenges that need to be addressed in the near future to avoid societal malfunctions.
en
heal.tableOfContents
Contents Acknowledgments……………………………3 Abstract……………………………………… 4 Declaration…………………………………….6 Copyright Statement ………………………………………………….6 PARTA………………………………………....12 CHAPTER A. 1 1. Introduction ........................................................................12 CΗΑPTER A. 2 2. Literature Review 2.1 Previous Literature ........................................................................16 2.2. Research Framework ........................................................................17 CHAPTER A. 3 3. Materials and Methods……………………………………....21 CHAPTER A.4………………………………26 4.Results……………………………………26 4.1. Bibliometric Analysis and Descriptive Results . 26 4.1a. Years of Publication, Country of Origin, Source of Publication, Subject Areas and Authors’ multi-disciplinarity 26 4.1b. Popular authors and co-cited authors, affiliations and departments 30 4.1c. Citation and Co-citation analysis based on the bibliographic data and the most popular keywords found in the 804 articles 32 4.2. Content Analysis Results 36 4.2a. Medical Specialties ...................... 37 4.2b. Stakeholders of Big Data Analytics in HealthCare 39 4.2c.Research approach ........................................ 42 4.2d. Nature of Analytics ....................................... 43 4.2e. Types of data ................................................. 44 4.2f Big Data Techniques ...................................... 45 4.2g.Big Data Capabilities ..................................... 47 4.3. The association of the selected analytical techniques with the data types and capabilities ........................................................... 50 4.4. Gained values/capabilities from the use of BDA in the health sector 51 4.5 Types of social value creation in healthcare 54 4.6 Challenges from the implementation of BDA in healthcare industry 60 4.7. The use of Machine Learning in the health field 64 4. 8. Future perspectives as derived through the article content analysis 65 4.9 Big Data Analytics .................................................68 4.10. Recent Examples of Big Data Analytics Tools for use in Healthcare 71 CHAPTER A. 5 .................................................. 75 5. Discussion and Conclusions……............... 75 CHAPTER A. 6 .................................................. 82 6. Limitations ................................................... 82 CHAPTER A. 7 .................................................. 84 7. Future research ............................................ 84 PART B .............................................................. 86 CHAPTER B. 1 .................................................. 86 1. Introduction .......................................................................................................... 86 CHAPTER B. 2 ...................................................... 92 2. Literature Review ..............................................92 CHAPTER B. 3 ...................................................... 96 3. Methodology .................................................... 96 CHAPTER B. 4 .......................................................99 4. Conceptual frameworks and results .............. 99 4. 1a. 1st Conceptual framework. “Communication Privacy Management” 99 4.1b. Results ................................................ 102 4.1c. App features and related challenges of digital healthcare 102 4.1d. Health app types ........................................... 106 4.1e. mHealth apps using Artificial intelligence ... 112 4. 2a. Conceptual Framework. “Normalization Process Theory” (NPT) 116 4.2b. Results ............................................... 119 A. Implementation stage: Health apps’ categories 119 B. Embedding stage: App features analysis indicating app trustworthiness 123 C. Integration stage: Quality evaluation ........... 127 C. Integration stage. Health apps popularity and engagement 128 4. 2c. Results from statistical tests: Comparison of Means 129 4.2d. Results from statistical tests: Correlations 131 4.3a. 3rd Conceptual framework .......................... 133 4.3b Hypothesis testing ........................................ 136 4.3c. Regression model variables .........................138 A. Depended variable ............................................ 139 B. Independent Variables ...................................... 139 4.3d. Regression analysis results ......................... 145 A. Descriptive statistics of variables ................... 145 B. Regression analysis results ............................. 146 CHAPTER B. 5 ....................................................... 148 5. Agenda with valuable insights about mHealth apps for professionals 148 5.1. mHealth apps: What exists .........................148 5.2 Suggestions for new mHealth apps with increased usefulness 150 5.3 Mitigation of Privacy and Security Challenges 151 5.4 Mitigation of Reliability Challenges .................. 155 CHAPTER B. 6 .......................................................... 157 6. mHealth apps: What is needed .......................... 158 6.1 Building Useful and Trustworthy, Quality mHealth apps 158 6.2 Managerial Implications ................................ 162 CHAPTER B. 7 ...... …………………….........……..163 7. Discussion and Conclusions……......................164 CHAPTER B. 8 ............................………………….168 8. Limitations and Future Research…………………………………………....168 CONCLUSIONS OF THE TWO PARTS OF THE Ph.D THESIS 169 REFERENCES…………...........................................171 APPENDIX……………………………………......... 205
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heal.advisorName
Katsaliaki, Korina
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heal.committeeMemberName
Kumar, Sameer
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heal.committeeMemberName
Stergioulas, Lampros
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heal.committeeMemberName
Katsaliaki, Korina
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heal.committeeMemberName
Kostagiolas, Petros
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heal.committeeMemberName
Tjortjis, Chris
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heal.committeeMemberName
Manolitzas, Panayiotis
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heal.committeeMemberName
Archontakis, Fragiskos
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heal.academicPublisher
International Hellenic University
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heal.academicPublisherID
ihu
en_US
heal.numberOfPages
254
en_US


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