Product details:
| ISBN13: | 9780443136191 |
| ISBN10: | 044313619X |
| Binding: | Paperback |
| No. of pages: | 338 pages |
| Size: | 235x191 mm |
| Weight: | 450 g |
| Language: | English |
| 700 |
Category:
Next Generation eHealth
Applied Data Science, Machine Learning and Extreme Computational Intelligence
Publisher: Academic Press
Date of Publication: 30 September 2024
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Long description:
Next Generation eHealth: Applied Data Science, Machine Learning and Extreme Computational Intelligence discusses the emergence, the impact, and the potential of sophisticated computational capabilities in healthcare. This book provides useful therapeutic targets to improve diagnosis, therapies, and prognosis of diseases, as well as helping with the establishment of better and more efficient next-generation medicine and medical systems. Machine learning as a field greatly contributes to next-generation medical research with the goal of improving medicine practices and medical Systems. As a contributing factor to better health outcomes, this book highlights the need for advanced training of professionals from various health areas, clinicians, educators, and social professionals who deal with patients. Content illustrates current issues and future promises as they pertain to all stakeholders, including informaticians, professionals in diagnostics, key industry experts in biotech, pharma, administrators, clinicians, patients, educators, students, health professionals, social scientists and legislators, health providers, advocacy groups, and more. With a focus on machine learning, deep learning, and neural networks, this volume communicates in an integrated, fresh, and novel way the impact of data science and computational intelligence to diverse audiences.
. Allows medical scientists, computer science experts, researchers, and health professionals to better educate themselves on machine Learning practices and applications and to benefit from the improvement of their knowledge skills.
. Provides various tested and current techniques of health literacy as a determinant of health and well-being.
. Provides insight into international research successfully implemented in patient care and education through the proper training of health professionals.
. Offers detailed guidance for diverse communities on their need to get timely, trusted, and integrated knowledge for the adoption of ML in healthcare processes and decisions. professionals involved with healthcare to leverage productive partnerships with technology developers
. Allows medical scientists, computer science experts, researchers, and health professionals to better educate themselves on machine Learning practices and applications and to benefit from the improvement of their knowledge skills.
. Provides various tested and current techniques of health literacy as a determinant of health and well-being.
. Provides insight into international research successfully implemented in patient care and education through the proper training of health professionals.
. Offers detailed guidance for diverse communities on their need to get timely, trusted, and integrated knowledge for the adoption of ML in healthcare processes and decisions. professionals involved with healthcare to leverage productive partnerships with technology developers
Table of Contents:
Contributors
About the editors
Other titles by this editor
Acknowledgments
Introduction
Chapter 1: The challenges for the next generation digital health: The disruptive character of Artificial Intelligence
Miltiadis D. Lytras, Abdulrahman Housawi, Basim S. Alsaywid and Naif Radi Aljohani
1. Introduction
2. Artificial Intelligence as a value-based ecosystem for digital health
2.1. The unique value proposition of Artificial Intelligence
2.2. A proposed value-based ecosystem for AI-enabled Digital Health
3. Disruptive scenarios and use case for AI-Enabled Next Generation Digital Health Services and Solutions
3.1. Disruptive NextGen AI-enabled digital health use cases
3.2. Use case scenarios for the integration of AI in diverse eHealth settings
4. Discussing the early-adoption era of next generation digital health
5. Conclusions
References
Chapter 2: Data governance in healthcare organizations
Abdulrahman Housawi and Miltiadis D. Lytras
1. Introduction
1.1. The methodological approach
2. Data governance in healthcare
2.1. The importance of data governance in eHealth
2.2. The data governance framework as a robust background and enabler of unified e-health service
2.3. Emerging technologies and data governance
2.4. Impact assessment
2.5. Challenges and considerations
3. Case study: Implementing data governance in a Saudi Arabian healthcare organization
3.1. Contextual analysis
3.2. Adopting DAMA DMBOK for maturity assessment
3.3. Data strategy and governance development
3.4. Approach to the assessment
3.5. Executive summary of key findings
3.6. Strategic recommendations of the maturity assessment
3.7. Strategic initiatives
3.8. Learning and evolution
3.9. Anticipated challenges and mitigation strategies
3.10. Conclusion and future directions
4. Conclusions
References
Chapter 3: Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece
Paraskevi Papadopoulou and Miltiadis D. Lytras
1. Introduction
2. Methodology
2.1. Research questions
2.2. Literature review
3. Findingsediscussion
3.1. Top prioritiesetop questions to address
3.2. Examples of recent advances in precision medicine
3.3. Ethical considerations of AI-generated healthcare innovation recommendations
3.4. Applications of AI in enhancing medical education
3.5. How AI contributes to climate change impacts on public health
3.6. Progress made in digital strategies in OECD countries and in Greece (related to research question 3)
4. Recommendationseconclusion
4.1. AI risks to consider
4.2. How can we live in harmony with nature and in full health?
References
Chapter 4: The economic feasibility of digital health and telerehabilitation
Priya Sharma, Meena Gupta and Ruchika Kalra
1. Introduction
2. Factor responsible for feasibility of digital health and telerehabilitation
2.1. Cost effectiveness
2.2. User adoption
3. Ways to deliver digital health
3.1. Remote patient monitoring
3.2. Video conferencing
3.3. Mobile health (mHealth)
3.4. Wearable activity monitor
3.5. Virtual assistant
4. Acceptability of digital health among care workers
4.1. Positive reinforcement of digital health
4.2. Negative reinforcement of digital health
5. Feasibility of digital health among patients
6. Discussion
7. Conclusion
References
Chapter 5: Intelligent digital twins: Scenarios, promises, and challenges in medicine and public health
Maged N. Kamel Boulos
1. Introduction
2. An overview of intelligent digital twins
3. Select IDT scenarios and applications in health and healthcare
3.1. Drug discovery and development
3.2. Medical device lifecycle management and innovation
3.3. Personalized treatment decision support
3.4. Public health applications
3.5. IDT banks for clinical trial matching and large-scale population studies
4. IDT issues and challenges
4.1. Data privacy and ownership
4.2. Technology maturity and adoption
4.3. Equity
4.4. Regulatory issues
5. Conclusions
References
Chapter 6: Digital twin in cardiology: Navigating the digital landscape for education, global health, and preventive medicine
Yara Alkhalifah and Dimitrios Lytras
1. Introduction
1.1. What is a digital twin?
1.2. How is a digital twin constructed?
1.3. Why are digital twins far from being realized?
2. Digital twin applications in cardiology: Enhancing precision medicine, clinical research, and medical education
2.1. Transformative potential and challenges
2.2. Federated learning as a solution
2.3. Dynamic bidirectional links in digital twins
2.4. Precision medicine, digital twins, and federated learning
2.5. Role in clinical trials and research methodologies
2.6. Role in medical education and clinical exams
2.7. Future implications and commitment of digital twin
3. Global cardiovascular health: Digital twin applications in hypertension and Saudi Arabia case
3.1. Regional prevalence of hypertension
3.2. Global cardiovascular health and digital twins: An overview
3.3. Precision medicine and the role of digital twins
3.4. Digital twin in cardiology: Saudi Arabia case
3.5. Transformative impact on global cardiovascular health
4. Digital twin integration in preventive cardiology
4.1. What is preventive cardiology?
4.2. Prospective applications of digital twins in preventive cardiology
4.3. Challenges arising from digital twins implementation on preventive cardiology
5. Conclusion
References
Chapter 7: Review of data-driven generative AI models for knowledge extraction from scientific literature in healthcare
Leon Kopitar, Primoz Kocbek, Lucija Gosak and Gregor Stiglic
1. Introduction
1.1. Introduction to a brief history of text summarization using NLP
2. Methods
2.1. Extractive and abstractive summarization
2.2. Zero-shot learning
2.3. Few-shot learning
2.4. Search strategy
2.5. Study selection
3. Results
3.1. Search results
3.2. Applications in the healthcare domain
3.3. Evaluation of generated short summaries
3.4. Examples of evaluations of summaries generated by SOTA models
3.5. Limitations and challenges of existing SOTA models
4. Discussion
Acknowledgments
References
Chapter 8: Approximate computing for energy-efficient processing of biosignals in ehealth care systems
Mahmoud Masadeh, Aya Masadeh and Abdullah Muaad
1. Introduction
1.1. Wireless body area networks
1.2. Approximate computing
1.3. Approximate squaring
2. IOT-based/e-healthcare systems
2.1. Internet of Things and e-healthcare
2.2. Pan-Tompkins algorithm
3. Edge computing and near-sensor computing
4. Wearable sensors
5. Approximate computing in Pan-Tompkins algorithm
5.1. Data description
5.2. ECG signals classification using PaneTompkins algorithm
6. Conclusions
References
Chapter 9: Linked open research information on semantic web: Challenges and opportunities for Research information management (RIM) User’s
Otmane Azeroual
1. Introduction
2. Semantic web and linked datadPotential of use
3. Reasons for LORI
4. Status quo of German National Library
5. Opportunities and challenges
6. Conclusion
References
Chapter 10: The need of E-health and literacy of cancer patients for Healthcare providers
Ruchika Kalra, Meena Gupta and Priya Sharma
List of notations and abbreviations
1. Introduction
2. eHealth and cancer screening
2.1. Need of eHealth technology in cancer diagnosis
2.2. eHealth can be as a new technology to cancer screening
2.3. Artificial intelligence a part to cancer screening in eHealth
2.4. Advantages and disadvantages of eHealth
2.5. Future challenges in eHealth cancer screening
3. Discussion
4. Conclusion
References
Chapter 11: eHealth concern over fine particulate matter air pollution and brain tumors
Prisilla Jayanthi Gandam, Iyyanki Krishna and Utku Ko¨se
1. Introduction
1.1. eHealth paradigmdMachine learning modeldAnalyzing of particulate matter
1.2. eHealth modeldResNET 50 model for brain tumor detection
1.3. ResNet 50 architecture
2. eHealthdSmart diagnosis of air pollution and brain tumors
3. Global industrial revolutiondA cause for air pollution
4. Conclusion
References
Chapter 12: Wearable devices developed to support dementia detection, monitoring, and intervention
Eaman Alharbi, Somayah Albaradei, Magbubah Essack, Janelle M. Jones and Akram Alomainy
1. Introduction
2. Method
2.1. Literature search strategy
2.2. Eligibility criteria
3. Wearables that assess and monitor symptoms of dementia
4. Wearables for the detection and monitoring of people with BPSD
5. Wearables used in assisting individuals with dementia in daily life
5.1. Wearables for the monitoring of physical and physiological activity
5.2. Wearable technology for localization and navigation
6. Wearables that support cognitive intervention
7. Limitations in dementia assessments
8. Discussion
9. Future work
10. Conclusion
References
Chapter 13: How artificial intelligence affects the future of pharmacy practice?
Sarah Alajlan and Miltiadis D. Lytras
1. Introduction
2. Artificial intelligence in pharmacy practice
2.1. Medication reminder
2.2. Drug interactions
2.3. Personalized medication management
2.4. Adverse drug reaction monitoring
2.5. Electronic health record integration
2.6. Chronic disease management
2.7. Patient education
2.8. Prescription refill management
2.9. Prescription transfer
2.10. Medication dosage adjustments
2.11. Medication therapy management
2.12. Patient screening
2.13. Patient triage
2.14. Medication reconciliation
2.15. Medication adherence tracking
2.16. Online prescription services
2.17. Pharmacist training and education
3. Challenges and barriers to artificial intelligence integration in pharmacy practice
4. Conclusion
References
Chapter 14: Designing robust and resilient data strategy in health clusters (HCs): Use case identification for efficiency and performance enhancement
Abdulrahman Housawi and Miltiadis D. Lytras
1. Introduction
2. Understanding data strategy in healthcare
2.1. The role of data strategy in healthcare organizations
2.2. Key components of data strategy in healthcare
2.3. Challenges and opportunities in healthcare data management
3. Data use cases in healthcare strategy
3.1. Introduction to data use cases in healthcare
3.2. Identifying and prioritizing approach for data use cases
3.3. Developing a comprehensive register of strategic use cases
3.4. Core data use cases in healthcare strategy
3.5. Emerging data use cases and trends
3.6. Implementing data use cases: Challenges and solutions
3.7. Impact of data use cases on healthcare strategy
3.8. Best practices and recommendations
3.9. Key messages and takeaways
3.10. Examples of data use cases in healthcare
3.11. The role of artificial intelligence and big data in healthcare
3.12. Challenges in data strategy implementation
3.13. Conclusion
4. Framework for developing a data strategy in healthcare organizations
4.1. Core components (foundational elements) of the framework
4.2. Strategic objectives
4.3. People and culture
4.4. Steps in developing a data strategy
5. Implementation plan for data strategy in healthcare
5.1. Key components of an effective implementation plan
5.2. Leadership and stakeholder engagement
5.3. Technology and infrastructure requirements
5.4. Resource allocation and budgeting
5.5. Training and capacity building
5.6. Project management methodologies for quick wins
5.7. Monitoring, milestones, evaluation, and continuous improvement
5.8. Key considerations for implementation
5.9. Potential challenges and solutions
5.10. Risk management and mitigation strategies
6. Case study: Implementing data strategy in a healthcare organization in Saudi Arabia
6.1. Overview of healthcare transformation in Saudi Arabia
6.2. The role of data strategy in healthcare transformation
6.3. Challenges and opportunities
7. Knowledge management and continuous improvement
7.1. Role of knowledge management in sustaining data strategy
7.2. Continuous improvement and adaptation of the data strategy
7.3. Leveraging lessons learned for ongoing success
7.4. Conclusion
8. Future directions and conclusion
8.1. Emerging trends in healthcare data strategy
8.2. The importance of scalability and flexibility in data strategies
8.3. The role of innovation and technology in shaping future data strategies
8.4. Conclusion
References
Chapter 15: Digital health as a bold contribution to sustainable and social inclusive development
Miltiadis D. Lytras, Abdulrahman Housawi, Basim S. Alsaywid, Dimitrios Lytras and Naif Radi Aljohani
1. The sustainable health ecosystem
1.1. The enabling technologies
1.2. The enabling stakeholders
1.3. The emerging agora of digital health services
2. Digital health as a pivotal pillar of social inclusive development
2.1. The context of social inclusive economic development
2.2. The areas of innovation and digital disruption
3. Conclusions
References
Index
About the editors
Other titles by this editor
Acknowledgments
Introduction
Chapter 1: The challenges for the next generation digital health: The disruptive character of Artificial Intelligence
Miltiadis D. Lytras, Abdulrahman Housawi, Basim S. Alsaywid and Naif Radi Aljohani
1. Introduction
2. Artificial Intelligence as a value-based ecosystem for digital health
2.1. The unique value proposition of Artificial Intelligence
2.2. A proposed value-based ecosystem for AI-enabled Digital Health
3. Disruptive scenarios and use case for AI-Enabled Next Generation Digital Health Services and Solutions
3.1. Disruptive NextGen AI-enabled digital health use cases
3.2. Use case scenarios for the integration of AI in diverse eHealth settings
4. Discussing the early-adoption era of next generation digital health
5. Conclusions
References
Chapter 2: Data governance in healthcare organizations
Abdulrahman Housawi and Miltiadis D. Lytras
1. Introduction
1.1. The methodological approach
2. Data governance in healthcare
2.1. The importance of data governance in eHealth
2.2. The data governance framework as a robust background and enabler of unified e-health service
2.3. Emerging technologies and data governance
2.4. Impact assessment
2.5. Challenges and considerations
3. Case study: Implementing data governance in a Saudi Arabian healthcare organization
3.1. Contextual analysis
3.2. Adopting DAMA DMBOK for maturity assessment
3.3. Data strategy and governance development
3.4. Approach to the assessment
3.5. Executive summary of key findings
3.6. Strategic recommendations of the maturity assessment
3.7. Strategic initiatives
3.8. Learning and evolution
3.9. Anticipated challenges and mitigation strategies
3.10. Conclusion and future directions
4. Conclusions
References
Chapter 3: Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece
Paraskevi Papadopoulou and Miltiadis D. Lytras
1. Introduction
2. Methodology
2.1. Research questions
2.2. Literature review
3. Findingsediscussion
3.1. Top prioritiesetop questions to address
3.2. Examples of recent advances in precision medicine
3.3. Ethical considerations of AI-generated healthcare innovation recommendations
3.4. Applications of AI in enhancing medical education
3.5. How AI contributes to climate change impacts on public health
3.6. Progress made in digital strategies in OECD countries and in Greece (related to research question 3)
4. Recommendationseconclusion
4.1. AI risks to consider
4.2. How can we live in harmony with nature and in full health?
References
Chapter 4: The economic feasibility of digital health and telerehabilitation
Priya Sharma, Meena Gupta and Ruchika Kalra
1. Introduction
2. Factor responsible for feasibility of digital health and telerehabilitation
2.1. Cost effectiveness
2.2. User adoption
3. Ways to deliver digital health
3.1. Remote patient monitoring
3.2. Video conferencing
3.3. Mobile health (mHealth)
3.4. Wearable activity monitor
3.5. Virtual assistant
4. Acceptability of digital health among care workers
4.1. Positive reinforcement of digital health
4.2. Negative reinforcement of digital health
5. Feasibility of digital health among patients
6. Discussion
7. Conclusion
References
Chapter 5: Intelligent digital twins: Scenarios, promises, and challenges in medicine and public health
Maged N. Kamel Boulos
1. Introduction
2. An overview of intelligent digital twins
3. Select IDT scenarios and applications in health and healthcare
3.1. Drug discovery and development
3.2. Medical device lifecycle management and innovation
3.3. Personalized treatment decision support
3.4. Public health applications
3.5. IDT banks for clinical trial matching and large-scale population studies
4. IDT issues and challenges
4.1. Data privacy and ownership
4.2. Technology maturity and adoption
4.3. Equity
4.4. Regulatory issues
5. Conclusions
References
Chapter 6: Digital twin in cardiology: Navigating the digital landscape for education, global health, and preventive medicine
Yara Alkhalifah and Dimitrios Lytras
1. Introduction
1.1. What is a digital twin?
1.2. How is a digital twin constructed?
1.3. Why are digital twins far from being realized?
2. Digital twin applications in cardiology: Enhancing precision medicine, clinical research, and medical education
2.1. Transformative potential and challenges
2.2. Federated learning as a solution
2.3. Dynamic bidirectional links in digital twins
2.4. Precision medicine, digital twins, and federated learning
2.5. Role in clinical trials and research methodologies
2.6. Role in medical education and clinical exams
2.7. Future implications and commitment of digital twin
3. Global cardiovascular health: Digital twin applications in hypertension and Saudi Arabia case
3.1. Regional prevalence of hypertension
3.2. Global cardiovascular health and digital twins: An overview
3.3. Precision medicine and the role of digital twins
3.4. Digital twin in cardiology: Saudi Arabia case
3.5. Transformative impact on global cardiovascular health
4. Digital twin integration in preventive cardiology
4.1. What is preventive cardiology?
4.2. Prospective applications of digital twins in preventive cardiology
4.3. Challenges arising from digital twins implementation on preventive cardiology
5. Conclusion
References
Chapter 7: Review of data-driven generative AI models for knowledge extraction from scientific literature in healthcare
Leon Kopitar, Primoz Kocbek, Lucija Gosak and Gregor Stiglic
1. Introduction
1.1. Introduction to a brief history of text summarization using NLP
2. Methods
2.1. Extractive and abstractive summarization
2.2. Zero-shot learning
2.3. Few-shot learning
2.4. Search strategy
2.5. Study selection
3. Results
3.1. Search results
3.2. Applications in the healthcare domain
3.3. Evaluation of generated short summaries
3.4. Examples of evaluations of summaries generated by SOTA models
3.5. Limitations and challenges of existing SOTA models
4. Discussion
Acknowledgments
References
Chapter 8: Approximate computing for energy-efficient processing of biosignals in ehealth care systems
Mahmoud Masadeh, Aya Masadeh and Abdullah Muaad
1. Introduction
1.1. Wireless body area networks
1.2. Approximate computing
1.3. Approximate squaring
2. IOT-based/e-healthcare systems
2.1. Internet of Things and e-healthcare
2.2. Pan-Tompkins algorithm
3. Edge computing and near-sensor computing
4. Wearable sensors
5. Approximate computing in Pan-Tompkins algorithm
5.1. Data description
5.2. ECG signals classification using PaneTompkins algorithm
6. Conclusions
References
Chapter 9: Linked open research information on semantic web: Challenges and opportunities for Research information management (RIM) User’s
Otmane Azeroual
1. Introduction
2. Semantic web and linked datadPotential of use
3. Reasons for LORI
4. Status quo of German National Library
5. Opportunities and challenges
6. Conclusion
References
Chapter 10: The need of E-health and literacy of cancer patients for Healthcare providers
Ruchika Kalra, Meena Gupta and Priya Sharma
List of notations and abbreviations
1. Introduction
2. eHealth and cancer screening
2.1. Need of eHealth technology in cancer diagnosis
2.2. eHealth can be as a new technology to cancer screening
2.3. Artificial intelligence a part to cancer screening in eHealth
2.4. Advantages and disadvantages of eHealth
2.5. Future challenges in eHealth cancer screening
3. Discussion
4. Conclusion
References
Chapter 11: eHealth concern over fine particulate matter air pollution and brain tumors
Prisilla Jayanthi Gandam, Iyyanki Krishna and Utku Ko¨se
1. Introduction
1.1. eHealth paradigmdMachine learning modeldAnalyzing of particulate matter
1.2. eHealth modeldResNET 50 model for brain tumor detection
1.3. ResNet 50 architecture
2. eHealthdSmart diagnosis of air pollution and brain tumors
3. Global industrial revolutiondA cause for air pollution
4. Conclusion
References
Chapter 12: Wearable devices developed to support dementia detection, monitoring, and intervention
Eaman Alharbi, Somayah Albaradei, Magbubah Essack, Janelle M. Jones and Akram Alomainy
1. Introduction
2. Method
2.1. Literature search strategy
2.2. Eligibility criteria
3. Wearables that assess and monitor symptoms of dementia
4. Wearables for the detection and monitoring of people with BPSD
5. Wearables used in assisting individuals with dementia in daily life
5.1. Wearables for the monitoring of physical and physiological activity
5.2. Wearable technology for localization and navigation
6. Wearables that support cognitive intervention
7. Limitations in dementia assessments
8. Discussion
9. Future work
10. Conclusion
References
Chapter 13: How artificial intelligence affects the future of pharmacy practice?
Sarah Alajlan and Miltiadis D. Lytras
1. Introduction
2. Artificial intelligence in pharmacy practice
2.1. Medication reminder
2.2. Drug interactions
2.3. Personalized medication management
2.4. Adverse drug reaction monitoring
2.5. Electronic health record integration
2.6. Chronic disease management
2.7. Patient education
2.8. Prescription refill management
2.9. Prescription transfer
2.10. Medication dosage adjustments
2.11. Medication therapy management
2.12. Patient screening
2.13. Patient triage
2.14. Medication reconciliation
2.15. Medication adherence tracking
2.16. Online prescription services
2.17. Pharmacist training and education
3. Challenges and barriers to artificial intelligence integration in pharmacy practice
4. Conclusion
References
Chapter 14: Designing robust and resilient data strategy in health clusters (HCs): Use case identification for efficiency and performance enhancement
Abdulrahman Housawi and Miltiadis D. Lytras
1. Introduction
2. Understanding data strategy in healthcare
2.1. The role of data strategy in healthcare organizations
2.2. Key components of data strategy in healthcare
2.3. Challenges and opportunities in healthcare data management
3. Data use cases in healthcare strategy
3.1. Introduction to data use cases in healthcare
3.2. Identifying and prioritizing approach for data use cases
3.3. Developing a comprehensive register of strategic use cases
3.4. Core data use cases in healthcare strategy
3.5. Emerging data use cases and trends
3.6. Implementing data use cases: Challenges and solutions
3.7. Impact of data use cases on healthcare strategy
3.8. Best practices and recommendations
3.9. Key messages and takeaways
3.10. Examples of data use cases in healthcare
3.11. The role of artificial intelligence and big data in healthcare
3.12. Challenges in data strategy implementation
3.13. Conclusion
4. Framework for developing a data strategy in healthcare organizations
4.1. Core components (foundational elements) of the framework
4.2. Strategic objectives
4.3. People and culture
4.4. Steps in developing a data strategy
5. Implementation plan for data strategy in healthcare
5.1. Key components of an effective implementation plan
5.2. Leadership and stakeholder engagement
5.3. Technology and infrastructure requirements
5.4. Resource allocation and budgeting
5.5. Training and capacity building
5.6. Project management methodologies for quick wins
5.7. Monitoring, milestones, evaluation, and continuous improvement
5.8. Key considerations for implementation
5.9. Potential challenges and solutions
5.10. Risk management and mitigation strategies
6. Case study: Implementing data strategy in a healthcare organization in Saudi Arabia
6.1. Overview of healthcare transformation in Saudi Arabia
6.2. The role of data strategy in healthcare transformation
6.3. Challenges and opportunities
7. Knowledge management and continuous improvement
7.1. Role of knowledge management in sustaining data strategy
7.2. Continuous improvement and adaptation of the data strategy
7.3. Leveraging lessons learned for ongoing success
7.4. Conclusion
8. Future directions and conclusion
8.1. Emerging trends in healthcare data strategy
8.2. The importance of scalability and flexibility in data strategies
8.3. The role of innovation and technology in shaping future data strategies
8.4. Conclusion
References
Chapter 15: Digital health as a bold contribution to sustainable and social inclusive development
Miltiadis D. Lytras, Abdulrahman Housawi, Basim S. Alsaywid, Dimitrios Lytras and Naif Radi Aljohani
1. The sustainable health ecosystem
1.1. The enabling technologies
1.2. The enabling stakeholders
1.3. The emerging agora of digital health services
2. Digital health as a pivotal pillar of social inclusive development
2.1. The context of social inclusive economic development
2.2. The areas of innovation and digital disruption
3. Conclusions
References
Index