Search

Browse Subject Areas

For Authors

Submit a Proposal

Artificial Intelligence-Based System Models in Healthcare

Edited by A. Jose Anand, K. Kalaiselvi and Jyotir Moy Chatterjee
Series: Machine Learning in Biomedical Science and Healthcare Informatics
Copyright: 2025   |   Expected Pub Date:2024/11/30
ISBN: 9781394242498  |  Hardcover  |  
508 pages

One Line Description
Artificial Intelligence-Based System Models in Healthcare provides a comprehensive and insightful guide to the transformative applications of AI in the healthcare system.

Audience
This book is intended for researchers and postgraduate students in artificial intelligence and the biomedical and healthcare sectors. Medical administrators, policymakers and regulatory specialists will also have an interest.

Description
This book is a groundbreaking exploration of the synergies between artificial intelligence and healthcare innovation. In an era where technological advancements are reshaping the landscape of medical practices, this book provides a comprehensive and insightful guide to the transformative applications of AI in healthcare systems. From conceptual foundations to practical implementations, the book serves as a roadmap for understanding the intricate relationships between AI-based system models and the evolution of healthcare delivery.
The first section delves into the fundamental role of technology in reshaping the healthcare landscape. With a focus on daily life activities, decision support systems, vision-based management, and semantic frameworks, this section lays the groundwork for understanding the pivotal role of AI in revolutionizing traditional healthcare approaches. Each chapter offers a unique perspective, emphasizing the intricate integration of technology into healthcare ecosystems.
The second section takes a deep dive into specific applications of AI, ranging from predictive analysis and machine learning to deep learning, image analysis, and biomedical text processing. With a focus on decision-making support systems, this section aims to demystify the complex world of AI algorithms in healthcare, offering valuable insights into their practical implications and potential impact on patient outcomes.
The final section addresses the modernization of healthcare practices and envisions the future landscape of AI applications. From medical imaging and diagnostics to predicting ventilation needs in intensive care units, modernizing health record maintenance, natural language processing, chatbots for medical inquiries, secured health insurance management, and glimpses into the future, the book concludes by exploring the frontiers of AI-driven healthcare innovations.

Back to Top
Author / Editor Details
A. Jose Anand, PhD, is a professor in the Department of ECE, KCG College of Technology, Chennai, Tamil Nadu, India. He obtained his doctorate in information and communication engineering from Anna University in 2017 and has published several papers in national and international journals. He has also authored books on various engineering topics.

K. Kalaiselvi, PhD, is an associate professor in the Department of Computer Applications, Saveetha College of Liberal Arts and Sciences, Chennai, Tamil Nadu, India. With more than 20 years of teaching and research experience, she has published more than 80 articles in SCI journals, presented more than 20 research papers at international conferences, has been granted a patent based on the Internet of Things applications, and published 10 monographs. In addition, she received the ‘Senior Woman Educator & Scholar Award’ in 2020 from the National Foundation for Entrepreneurship Development.

Jyotir Moy Chatterjee, is an assistant professor in the Department of CSE, Graphic Era University, Dehradun, Uttarakhand, India. Additionally, he is an assistant professor in the Department of IT at the Lord Buddha Education Foundation, Kathmandu, Nepal. His research interests include machine learning and the Internet of Things. He was also the Young Ambassador of the Scientific Research Group of Egypt for 2020-2021. He has edited many books of which several are with the Wiley-Scrivener imprint.

Back to Top

Table of Contents
Preface
Part I: Introduction to Healthcare Systems
1. Role of Technology in Healthcare Systems
A. Hency Juliet and K. Kalaiselvi
1.1 Introduction
1.2 Transformation in Healthcare
1.2.1 Digitalization and Health Tech Integration
1.2.2 Patient-Centric Approach
1.2.3 Telemedicine and Virtual Care
1.2.4 Data-Driven Decision Making
1.2.5 Preventive and Predictive Healthcare
1.2.6 Value-Based Care
1.2.7 Interoperability and Health Information Exchange
1.2.8 Genomics and Personalized Medicine
1.2.9 Population Heal Management
1.2.10 Innovation and Collaboration
1.2.11 Regulatory and Policy Changes
1.2.12 Workforce Transformation
1.3 Technology Transformation in Healthcare Industry
1.4 Patient Care Improvement Using Healthcare Technology
1.5 Importance of Technology in Healthcare
1.6 Technology Impact on Healthcare
1.7 Innovation and Digital Transformation
1.8 Diagnostics’ Role in Combatting Life-Threatening Diseases
1.9 Role of Medical Technology in Healthcare
1.10 Conclusion
References
2. Health Status Estimation based on Daily Life Activities
Josephine Anitha A. and Geetanjali R.
2.1 Introduction
2.2 Intersection of Technology and Healthcare
2.2.1 Historical Context
2.2.2 Milestones in the Integration of Technology into Healthcare Practices
2.2.3 Wearable Devices, IoT Technologies and their Prevalence
2.3 Unveiling the Technologies
2.3.1 Wearable Devices and Real-Time Biometric Data
2.3.2 Smart Homes and IoT in Healthcare
2.4 Machine Learning Marvels: Unravelling Health Insights From Daily Life Activities
2.4.1 The Landscape of Daily Life Activities
2.4.2 Key Machine Learning Algorithms for Health Status Estimation
2.5 Data Collection and Processing in Daily Life Health Monitoring
2.6 Ethical Considerations, Data Privacy, and Regulatory Compliance
2.6.1 The Promise and Risks of Health Status Estimation
2.6.2 Ethical Considerations in Health Data Usage
2.6.3 Protecting the Health Data Usage
2.7 Potential Areas of Improvement
2.8 Challenges and Opportunities
2.9 Conclusion
References
3. Decision Support System in Healthcare Monitoring
V. Suganthi and K. Kalaiselvi
3.1 Introduction
3.1.1 Definition and Overview
3.1.1.1 Define Decision Support System
3.1.1.2 Contextualize DSS in the Healthcare Monitoring System
3.1.2 Importance of Decision Support in Healthcare
3.1.2.1 Enhancing Patient Care
3.1.2.2 Improving Diagnostic Accuracy
3.1.2.3 Streamlining Healthcare Processes
3.2 Components of a Healthcare Monitoring System
3.2.1 Patient Monitoring Devices
3.2.1.1 Overview of Wearable Devices
3.2.1.2 Remote Monitoring Technologies
3.2.2 Data Collection and Storage
3.2.2.1 Electronic Health Records
3.2.2.2 Real-Time Data Streams
3.2.3 Integration of Medical Sensors
3.2.3.1 Overview of Sensor Technologies
3.2.3.2 Interoperability Challenges
3.3 Role of Decision Support System
3.3.1 Data Processing and Analysis
3.3.1.1 Handling Large Datasets
3.3.1.2 Data Preprocessing Techniques
3.3.2 Machine Learning Algorithms
3.3.2.1 Predictive Modeling for Early Detection
3.3.2.2 Pattern Recognition in Patient Data
3.3.3 Clinical Decision Support
3.3.3.1 Assisting Healthcare Professionals in Decision Making
3.3.3.2 Providing Evidence-Based Recommendations
3.4 Challenges in Implementing Decision Support Systems
3.4.1 Privacy and Security Concerns
3.4.1.1 Patient Data Protection
3.4.1.2 Compliance With Healthcare Regulations
3.4.2 User Acceptance and Adoption
3.4.2.1 Training Healthcare Professionals
3.4.2.2 Overcoming Resistance to Technology
3.4.3 Technical Challenges
3.4.3.1 System Integration Issues
3.4.3.2 Scalability and Performance Considerations
3.5 Future Trends and Innovations
3.5.1 Advancements in Artificial Intelligence
3.5.1.1 Integration of Deep Learning in Healthcare
3.5.1.2 Evolution of Predictive Analytics
3.5.2 Human–AI Collaboration
3.5.2.1 Enhancing Clinician Decision Making With AI
3.5.2.2 Ethical Considerations in Human–AI Collaboration
3.6 Conclusion
3.6.1 Summary of Key Points
3.6.2 The Future of Decision Support in Healthcare Monitoring
3.6.3 Call to Action for Healthcare Institutions
References
4. Vision-Based Management System in Healthcare Applications
K. Balasubramanian, Anu Tonk, Seema Bhakuni, S. Anita, Freddy Ajila and S. Sathish Kumar
4.1 Introduction
4.1.1 Causes of ADDE
4.1.2 Immune‑Mediated Lacrimal Gland Inflammation
4.1.3 Conjunctival Cicatrization
4.1.4 Neurogenic
4.1.5 Alacrimia
4.1.6 Diagnosing ADDE in Clinics
4.2 History
4.3 Tear Testing and Ocular Surface Analysis in a Clinical Examination
4.3.1 Schirmer Test
4.3.2 Tear Volume
4.3.3 Lacrimal Gland Examination
4.4 Other Ocular Surface Health-Related Clinical Examinations
4.4.1 Ocular Surface Staining
4.4.2 Tear Stability
4.4.3 Meibomian Gland Health
4.4.4 Examination for Ocular Surface Scarring
4.4.5 Nerve Status
4.4.6 Tear Osmolarity
4.4.7 Cytokines and Biomarkers
4.4.8 Tests for Cicatricial Etiology
4.4.9 Blood Workup for Underlying Systemic Disease
4.5 Management of ADDE
4.5.1 Nonspecific Therapy in ADDE
4.5.1.1 Lubricants
4.5.2 Topical Immunosuppressants or Immunomodulators
4.5.3 Secretagogs
4.5.4 Autologous Serum
4.5.5 Alternative Therapies
4.5.6 Punctual Occlusion
4.6 Disease‑Specific Therapy in ADDE
4.6.1 Systemic Immunomodulation in SS
4.6.2 Systemic Immunosuppression in MMP
4.6.3 Minor Transplantation of Salivary Glands for Severe Cicatricial ADDE
4.7 ADDE With NK
4.7.1 ADDE With Neuropathic Component to Pain
4.8 Unmet Needs and Future Directions
4.8.1 Gut Microbiome Modulation in SS
4.8.2 Mesenchymal Stem Cell Therapy for Lacrimal Gland Regeneration
4.8.3 Bioengineered Lacrimal Gland
4.9 Conclusion
References
5. Semantic Framework in Healthcare Systems
Pooja Dabhowale, Mukesh Yadav, Nidhi Tiwari, Ruchi Sharma, Jose Anand A. and Irshad Ahamad
5.1 Introduction
5.2 Background
5.2.1 Challenges and Potential Outcomes
5.2.2 Strengths and Weaknesses
5.3 Internet of Things
5.3.1 Adapting Existing Medical Practices 5G Network
5.3.2 Modulation Schemes Actually
5.3.3 Debugging Strategy
5.3.4 Communications Spectrum
5.4 Research Methodology
5.4.1 Communication Speed
5.4.2 Response Time
5.4.3 Support of Network Operations
5.5 Theoretical Framework
5.5.1 Semantic Technologies
5.5.2 Ontology
5.5.3 Overview of the Healthcare Semantic Frameworks
5.5.4 Different Semantic Frameworks
5.5.5 Method
5.6 Data Analysis
5.6.1 Confidentiality of Personal Information
5.6.2 Removed Obstacles
5.6.3 Exceptionally Superior
5.6.4 Current Wireless vs. 5G
5.6.4.1 5G IoMT Health Monitoring
5.6.4.2 5G Network Architecture
5.6.4.3 Interventional Surgical Procedures Performed by Robots
5.7 Conclusion
References
Part II: AI-Based System Models in Healthcare Applications
6 Predictive Analysis in Healthcare Systems
J. Sathya and F. Mary Harin Fernandez
6.1 Introduction
6.2 Related Work
6.2.1 Data Mining Techniques in Biomedical Informatics
6.2.2 Decision Support Systems in Biomedical Applications
6.2.3 Biomedical Decision Support Systems
6.2.4 Application of ML in Biomedical Informatics
6.2.5 Integrating Artificial Intelligence and Clinical Decision Support Systems in Biomedicine
6.2.6 Decision Support Systems for Cyberbullying Intervention
6.2.7 Social Network Analysis of Cyberbullying Incidents on Online Forums
6.2.8 ML Approach for Cyberbullying Detection
6.2.9 Detecting Cyberbullying on Social Media Using ML Techniques
6.2.10 Natural Language Processing Approaches for Cyberbullying Detection on Social Media
6.2.11 Anomaly Detection for Cyberbullying Detection in Online Communities
6.2.12 Cyberbullying Detection and Intervention Systems
6.2.13 Cyberbullying Detection Using Ensemble ML Techniques
6.2.14 Multimodal ML for Cyberbullying Detection
6.3 Proposed System
6.3.1 Biomedical Data Collection
6.3.1.1 Biomedical Data Analysis
6.3.1.2 Role of Wearable Devices
6.3.1.3 Biometric Monitoring Sensors
6.3.2 Cyberbullying Detection Systems
6.3.2.1 Case Management Systems
6.3.2.2 Risk Assessment Tools
6.3.2.3 Legal Framework and Guidelines
6.3.2.4 Expert Consultation
6.3.3 Machine Learning
6.3.3.1 Data Collection and Labeling
6.3.3.2 Feature Extraction
6.3.3.3 Feature Selection
6.3.3.4 Data Splitting
6.3.3.5 Model Selection
6.3.3.6 Model Training
6.3.3.7 Model Evaluation
6.3.3.8 Interpretability and Insights
6.3.3.9 Integration into Decision Support System
6.3.3.10 Continuous Improvement
6.4 Provide Support Tools and Visualizations to Aid in the Decision-Making Process
6.4.1 Sentiment Analysis for Emotional Assessment
6.4.2 Contextual Understanding and Entity Recognition
6.4.3 Risk Assessment and Decision Making
6.4.4 Early Intervention and Support Strategies
6.4.5 Privacy and Ethical Considerations
6.5 Conclusion
References
7. Machine Learning in Healthcare System
A. Hency Juliet and K. Sathya
7.1 Introduction
7.1.1 Need for ML in Healthcare
7.1.2 Importance of ML in Healthcare Organizations
7.1.3 Significance of ML in Healthcare
7.1.4 Rise of ML in Healthcare Settings
7.1.5 ML and IoMT in Healthcare
7.1.6 Applications of ML in Healthcare
7.1.7 Tasks that ML in Healthcare Can Handle
7.1.8 Ethics of ML in Healthcare
7.1.9 Challenges of Adopting ML in Healthcare
7.1.10 Future of ML in Healthcare
7.1.11 Common ML Algorithms
7.1.12 Conclusion
References
8. Deep Learning Applications in Healthcare Systems
V. Sheeja Kumari and Renjith Balu
8.1 Introduction
8.1.1 Overview of Healthcare Challenges
8.1.2 Role of Deep Learning in Addressing these Challenges
8.1.3 Importance of Integrating Deep Learning into Healthcare Systems
8.2 Fundamentals of Deep Learning
8.2.1 Artificial Neural Networks
8.2.2 Types of ANN Architectures
8.2.3 Deep Learning in Medical Imaging
8.2.3.1 Detection of Medical Images Using DL
8.2.3.2 Medical Image Segmentation Using DL
8.3 Deep Learning Architecture for Image Classification
8.3.1 AlexNet
8.3.2 Architecture Details
8.4 Conclusion
References
9. Image Analysis for Health Prediction
Pulla Sujarani and K. Kalaiselvi
9.1 Introduction
9.1.1 Image Analysis
9.2 Overview
9.3 Image Preprocessing
9.3.1 Digital Image Processing Characteristics
9.3.2 Advantages in Digital Image Preprocessing
9.4 Image Filtering
9.4.1 Different Types of Filters
9.5 Image Enhancement
9.6 Image Segmentation
9.6.1 Types of Segmentation Techniques
9.6.2 Image Segmentation Techniques
9.7 Feature Extraction
9.7.1 Applications of Feature Extraction
9.7.2 Principal Component Analysis (PCA)
9.7.3 Gray Level Co-Occurrence Matrix (GLCM)
9.7.4 GLCM Matrix Calculation
9.8 Classification
9.8.1 Support Vector Machine (SVM)
9.8.2 Logistic Regression (LR)
9.8.3 Decision Tree (DT)
9.8.4 Deep Convolutional Neural Network (DCNN)
9.8.5 Classification Algorithm Applications
9.8.6 Classification Model Evaluations
9.8.7 Evaluation Process
9.9 Conclusion
References
10. Machine Learning in Biomedical Text Processing
Shibi Mathai and K. Kalaiselvi
10.1 Introduction
10.1.1 Textual Data Characteristics in Biomedicine
10.2 Fundamentals of ML for Text Processing
10.2.1 Overview of ML
10.2.2 Text as Data: Preprocessing Steps
10.2.3 ML Algorithms Basics
10.3 NLP Techniques in Biomedicine
10.3.1 Tokenization, Stemming, and Lemmatization
10.3.2 Named Entity Recognition (NER) in Biomedical Texts
10.3.3 Relationship Extraction
10.3.4 Ontologies in Biomedical NLP
10.4 NLP Techniques in Biomedicine
10.4.1 Supervised vs. Unsupervised Learning
10.4.2 Common Algorithms
10.4.3 Deep Learning Approaches
10.4.4 Pretrained Models
10.5 Feature Engineering and Selection in Biomedical Text
10.5.1 Importance of Feature Engineering
10.5.2 Techniques for Feature Extraction
10.5.3 Dimensionality Reduction in High-Dimensional Text Data
10.6 Applications of ML in Biomedical Text Mining
10.6.1 Literature-Based Discovery
10.6.2 Clinical Decision Support Systems
10.6.3 Drug Repurposing
10.6.4 Predictive Modelling in Clinical Research
10.7 Evaluation Metrics and Model Validation
10.7.1 Performance Metrics
10.7.2 Challenges in Evaluating Biomedical Text Mining Systems
10.7.3 Case Studies of Model Validation
10.8 Ethical Considerations and Data Privacy
10.8.1 Ensuring Patient Confidentiality
10.8.2 Biases in ML Models
10.8.3 Ethical Implications of Automated Decision-Making
10.9 Future Directions and Challenges
10.9.1 Integration of Heterogeneous Data Sources
10.9.2 Transfer Learning and Multi-Task Learning
10.9.3 The Future Role of AI in Personalized Medicine
10.10 Conclusion
10.10.1 Summary of Key Points
10.10.2 The Potential Impact on Healthcare Outcomes
10.10.3 Final Thoughts and Call to Action for Further Research
References
11. Decision Making Biomedical Support System
V. Sheeja Kumari, J. Vijila and Renjith Balu
11.1 Introduction
11.1.1 Importance of Decision Support Systems in Healthcare
11.1.2 Purpose and Scope of the Biomedical Decision Support System
11.2 System Architecture and Components
11.2.1 Disease Databases
11.3 Machine Learning Algorithms
11.4 Expert Systems
11.5 Statistical Analysis Tools
11.6 User Interface
11.7 Interactivity for Healthcare Professionals
11.8 User-Friendly Design
11.9 Summary
References
Part III: Modernization and Future – Healthcare Applications
12. Medical Imaging and Diagnostics with Machine Learning
M. Sowmiya, D. Bhanu, K. Shruthi, Punitha Jilt, B. Beaula Pinky and A. Yasmine Begum
12.1 Introduction
12.2 Establishing a Smart Sensor Network With the Help of AI
12.3 Impact of Nanotechnology and IoMT in Healthcare
12.4 Artificial Intelligence’s Impact on the Surgery
12.5 The Importance of Artificial Intelligence in Treating Diabetes and Cancer
12.6 Challenges and Future Scope
12.7 Conclusions
References
13. Predicting Ventilation Needs in Intensive Care Unit
Yashini Priyankha S., S. Sumathi, T. Mangavarkarasi, Jose Anand A. and Mithileysh Sathiyanarayanan
13.1 Introduction
13.2 AI-Based Predictive Models for Healthcare Ventilation Systems
13.3 AI Based Ventilator Weaning Predicting Unit
13.4 Predictive Applications of AI in Healthcare
13.5 AI Impacts on Ventilation Requirements
13.6 ICU and Healthcare Future With AI
13.7 Conclusion
References
14. Modernized Health Record Maintenance
K. Balasubadra, Franklin Baltodano, Indira Pineda, S. Mayakannan, Eduardo Hernández and Navin M. George
14.1 Introduction
14.1.1 Blockchain
14.1.2 Types of Blockchain
14.1.2.1 Public Blockchain
14.1.2.2 Private Blockchain
14.1.2.3 Consortium Blockchain
14.1.2.4 Hybrid Blockchain
14.2 Literature Survey
14.3 Materials and Methods
14.3.1 Blockchain Complexity
14.3.2 High-Energy Consumption
14.3.3 Scalability Challenges
14.3.4 Brain Drain for Blockchain
14.3.5 Healthcare Components of Blockchain
14.3.5.1 Healthcare Blockchain
14.3.5.2 Securing Patient Data
14.3.5.3 Healthcare Data Management
14.3.5.4 Challenges in Healthcare Data Management
14.4 Having a Proper Strategy
14.5 A Common Database to be Maintained Like a Repository
14.6 The Database Must Have Genuine Data
14.7 Case Study and Applications
14.7.1 Methodology
14.7.2 Healthcare Data Management Using Blockchain Technologies
14.7.2.1 Pharmaceutical Sector
14.7.2.2 Pharmaceutical Research and Drug Discovery
14.7.2.3 Supply Chain and Counterfeit Drug Detection
14.7.2.4 Prescription Management
14.7.2.5 Precision Tracking
14.7.2.6 Advantages
14.7.2.7 Accessing and Sharing Health Data
14.7.2.8 Data to Empower Patients
14.7.2.9 Malpractice Concerns
14.7.2.10 Institutional and Interpersonal Competition
14.7.3 Ethics and Dissemination
14.7.4 Analytics
14.7.4.1 Predictive Analytics
14.7.4.2 Telemedicine
14.7.4.3 Analytics With Centralized Server
14.7.5 Blockchain to the Rescue
14.7.6 Blockchain as a Service (BaaS)
14.7.6.1 Baas Operations
14.8 Conclusion
References
15. Natural Language Processing in Medical Applications
V. Prasanna Srinivasan, Evelyn Rosero, P. Sengottuvelan, Abhinav Singhal, Chandraketu Singh and S. Mayakannan
15.1 Introduction
15.2 Related Studies on Medical Systems - Use of Machine Learning
15.3 Health Data Formats in Medical Systems
15.4 Prototype of Algorithms and Data Conversion
15.4.1 Implementation Details and Problems
15.4.2 Natural Language Processing Parser Details
15.4.3 Testing Methods
15.5 Results and Discussion
15.5.1 Data Transformation, Representation and NLP Parser Details
15.5.2 Accuracy of Algorithm
15.5.3 Performance Impact of TWNFI Hyperparameters
15.5.4 NLP Transformation Accuracy
15.5.5 Results Discussion
15.6 Conclusions
References
16. Chat Bots for Medical Enquiries
K. Saravanan, Indira Pineda, Franklin Baltodano, Krunal Vishavadia, Vanessa Valverde and Jose Anand A.
16.1 Introduction
16.1.1 Challenges
16.1.2 Motivation
16.1.3 Chatbots Types: Application Aspect
16.2 Artificial Intelligence ‑ Chatbot: Components of Architecture
16.3 Artificial Intelligence ‑ Chatbot: Models for Generating a Response
16.4 AI Chatbots: Methods and Technologies
16.4.1 Deep Learning (DL)
16.4.2 Natural Language Processing
16.4.3 Natural Language Understanding
16.4.4 NLG
16.5 A Development of Conversational Agents: State‑of‑the‑Art Chatbots
16.5.1 Turing Test
16.5.2 ELIZA
16.5.3 PARRY
16.5.4 Racter
16.5.5 Jabberwocky
16.5.6 Loebner Prize Competition
16.5.7 Dr. Sbaitso
16.5.8 ALICE
16.5.9 SmarterChild
16.5.10 MITSUKU
16.5.11 Watson
16.5.12 SIRI
16.5.13 Google Now/Assistant
16.5.14 ALEXA
16.5.15 Dialogflow
16.5.16 LUIS
16.5.17 Amazon Lex
16.6 AI Chatbots: Customer-Based Services
16.7 AI‑Chatbots: Public Administration-Based Services
16.8 Chatbot Performance Evaluation
16.9 Conclusion
References
17. Secured Health Insurance Management
A. Ravisankar, P. Manikandan, Iskandar Muda, Shrinivas V. Kulkarni, Rolando Marcel Torres Castillo and Jose Anand A.
17.1 Introduction
17.1.1 Service Coverage Index (SCI)
17.1.2 Financial Risk Protection
17.1.3 Changes in India’s Health Insurance System
17.1.4 HIC and the Availability of Digital Health Technologies
17.1.5 Healthcare for Mothers and Children in India
17.2 Methods
17.2.1 Study Setting
17.2.2 Study Design
17.2.3 Technology Adoption Models: A Conceptual Framework
17.2.4 Digital Health Intervention (MedStrat HIMS)
17.3 Results
17.3.1 MedStrat HIMS Clients
17.3.2 State Health Agency Kerala (https://sha.kerala.gov.in/)
17.3.3 West Bengal Health Scheme (WBHS) (https://wbhealthscheme.gov.in/)
17.3.4 Accelerating UHC through Scaling the MedStrat HIMS
17.3.5 First Level Training
17.3.6 Second Level Training
17.3.7 Third Level Training and After-Deployment Supervision Support
17.3.8 The Proposed Structure for Expanding HIMS
17.4 Discussion
17.4.1 Scaling Up Digital Health Insurance Systems: Lessons from MedStrat HIMS
17.5 Conclusion
References
18. Future of Healthcare Applications
Vettrivel Arul, Hitendra Kumar Lautre, T. Priya, Satish Kumar Verma, Freddy Ajila and Ramu Samineni
18.1 Introduction
18.2 A History of Blockchain Technology (1991 - 2021)
18.2.1 Technical Information of Blockchain Technology
18.2.2 Types of Blockchain Technology
18.3 Motivations
18.3.1 Data Security and Safety
18.3.2 Data Integrity
18.3.3 Data Privacy
18.3.4 Authentication
18.3.5 Interoperability
18.3.6 Efficiency and Implementation
18.3.7 Data Storage
18.4 Topmost Healthcare Projects in Blockchain Technology Based on Market Capital
18.4.1 MediBlock (MEDX)
18.4.2 Dentacoin (DCN)
18.4.3 Solve (SOLVE)
18.4.4 Medicalchain (MTN)
18.4.5 Aenco (AEN)
18.4.6 Safe Insure (SINS)
18.4.7 Humans Cape (HUM)
18.4.8 MediShares (MDS)
18.4.9 Lympo (LYM)
18.4.10 Farma Trust (FTT)
18.4.11 MediLedger
18.4.12 Guardtime HSX
18.4.13 MedRec
18.5 Healthcare Applications for Blockchain Technology
18.5.1 Applications for Healthcare Management Based on the Blockchain
18.5.2 Internet of Medical Things (IoMT)
18.6 Research Challenges and Future Direction
18.6.1 Security and Privacy of Data
18.6.2 Managing Storage Capacity
18.6.3 Interoperability and Scalability
18.6.4 Related to Blockchain Size
18.6.5 Related to Computing Power Limitations
18.6.6 Related to Latency and Throughput Limits
18.6.7 Standardization Challenges
18.6.8 Confidence and Data Ownership
18.7 Conclusion
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page