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Human Machine Interface

Making Healthcare Digital

Edited Rishabha Malviya, Sonali Sundram, Bhupendra Prajapati and Sudarshan Kumar Singh
Copyright: 2023   |   Status: Contracted
ISBN: 9781394199914  |  Hardcover  |  
516 pages

One Line Description
The book contains the latest advances in healthcare and presents them in the frame of the Human-Machine Interface (HMI).

Audience
The target audience for this book is medical personnel and policymakers in healthcare and pharmaceutical professionals, as well as engineers and researchers in computer science and artificial intelligence.

Description
The Human-Machine Interface (HMI) industry has witnessed the evolution from a simple push button to a modern touch-screen display. HMI is a user interface that allows humans to operate controllers for machines, systems, or instruments. Most medical procedures are improved by HMI systems, from calling an ambulance to ensuring that a patient receives adequate treatment on time.
This book describes the scenario of biomedical technologies in the context of the advanced HMI, with a focus on direct brain-computer connection. The book describes several HMI tools and related techniques for analyzing, creating, controlling, and upgrading healthcare delivery systems, and provides details regarding how advancements in technology, particularly HMI, ensure ethical and fair use in patient care.

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Author / Editor Details
Rishabha Malviya, PhD, is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University. He has authored more than 150 research/review papers for national/international journals of repute. He has been granted more than 10 patents from different countries while a further 40 patents either published or under evaluation. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery and characterization of natural polymers as pharmaceutical excipients.

Sonali Sundram, PhD and MPharm, completed her doctorate in pharmacy and is currently working at Galgotias University, Greater Noida. Her areas of interest are neurodegeneration, clinical research, and artificial intelligence. She has edited 4 books.

Bhupendra Prajapati, PhD and MPharm, is a Professor in the Department of Pharmaceutics, Shree S.K.Patel College of Pharmaceutical Education and Research, Ganpat University, Gujarat, India. He has more than 20 years of academic and research experience and has published more than 100 research and review papers in international and national Journals. He has published two Indian patents and has three applications under evaluation.

Sudarshan Kumar Singh, PhD, is a postdoctoral research associate in the Faculty of Pharmacy, Anisabad Chiang Mai University, Chiang Mai, Thailand. His areas of interest are the fabrication of 3D-printed pharmaceutical products and microneedles for effective therapy against life-threatening diseases. He has received many awards for Reinventing Chiang Mai University Postdoctoral fellowship.

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Table of Contents
Foreword
Preface
Acknowledgement
Part I: Advanced Patient Care with HMI
1. Introduction to Human-Machine Interface
Shama Mujawar, Aarohi Deshpande, Aarohi Gherkar, Samson Eugin Simon and Bhupendra Prajapati
1.1 Introduction
1.2 Types of HMI
1.2.1 The Pushbutton Replacer
1.2.2 The Data Handler
1.2.3 The Overseer
1.3 Transformation of HMI
1.4 Importance and COVID Relevance With HMI
1.5 Applications
1.5.1 Biological Applications
1.5.1.1 HMI Signal Detection and Procurement Method
1.5.1.2 Healthcare and Rehabilitation
1.5.1.3 Magnetoencephalography
1.5.1.4 Flexible Hybrid Electronics (FHE)
1.5.1.5 Robotic-Assisted Surgeries
1.5.1.6 Flexible Microstructural Pressure Sensors
1.5.1.7 Biomedical Applications
1.5.1.8 CB-HMI
1.5.1.9 HMI in Medical Devices
1.5.2 Industrial Applications
1.5.2.1 Metal Industries
1.5.2.2 Video Game Industry
1.5.2.3 Aerospace and Defense
1.5.2.4 Water Purification Plant HMI Based on Multi-Agent Systems (MAS)
1.5.2.5 Virtual and Haptic Interfaces
1.5.2.6 Space Crafts
1.5.2.7 Car Wash System
1.5.2.8 Pharmaceutical Processing and Industries
1.6 Challenges
1.7 Conclusion and Future Prospects
References
2. Improving Healthcare Practice by Using HMI Interface
Vaibhav Verma, Vivek Dave and Pranay Wal
2.1 Background of Human-Machine Interaction
2.2 Introduction
2.2.1 Healthcare Practice
2.2.2 Human-Machine Interface System in Healthcare
2.3 Evolution of HMI Design
2.3.1 HMI Design 1.0
2.3.2 HMI Design 2.0
2.3.3 HMI Design 3.0
2.3.4 HMI Design 4.0
2.4 Anatomy of Human Brain
2.5 Signal Associated With Brain
2.5.1 Evoked Signals
2.5.2 Spontaneous Signals
2.5.3 Hybrid Signals
2.6 HMI Signal Processing and Acquisition Methods
2.7 Human-Machine Interface–Based Healthcare System
2.7.1 Healthcare Practice System
2.7.1.1 Healthcare Practice
2.7.1.2 Current State of Healthcare Provision
2.7.1.3 Concerns With Domestic Healthcare
2.7.2 Medical Education System
2.7.2.1 Traditional and Modern Way of Providing Medical Education
2.8 Working Model of HMI
2.9 Challenges and Limitations of HMI Design
2.10 Role of HMI in Healthcare Practice
2.10.1 Simple to Clean
2.10.2 High Chemical Tolerance
2.10.3 Transportable and Light
2.10.4 Enhancing Communication
2.11 Application of HMI Technology in Medical Fields
2.11.1 Medical and Rehabilitative Engineering Using HMI
2.11.2 Controls for Robotic Surgery and Human Prosthetics
2.11.3 Sensory Replacement Mechanism
2.11.4 Wheelchairs and Moving Robots Along With Neurological Interface
2.11.5 Cognitive Improvement
2.12 Conclusion and Future Perspective
References
3. Human-Machine Interface and Patient Safety
Arun Kumar Singh and Rishabha Malviya
3.1 Introduction
3.2 Detecting Anesthesia-Related Drug Administration Errors and Predicting Their Impact
3.2.1 Methodological Difficulties in Studying Rare, Dangerous Phenomena
3.2.2 Consequences of Errors
3.2.3 Lessons From Other Industries
3.2.4 The Double-Human Interface
3.2.5 The Culture of Denial and Effort
3.2.6 Poor Labeling
3.3 Systematic Approaches to Improve Patient Safety During Anesthesia
3.3.1 Design Principles
3.3.2 Evidence of Safety Gains
3.3.3 Consistent Color-Coding
3.3.4 The Codonics Label System
3.4 The Triumph of Software
3.4.1 Software in Hospitals
3.4.2 Software in Anesthesia
3.4.3 The Alarm Problem
3.5 Environments that Audit Themselves
3.6 New Risks and Dangers
3.7 Conclusion
References
4. Human-Machine Interface Improving Quality of Patient Care
Rishav Sharma and Rishabha Malviya
4.1 Introduction
4.2 An Advanced Framework for Human-Machine Interaction
4.2.1 A Simulated Workplace Safety and Health Program
4.3 Human–Computer Interaction (HCI)
4.4 Multimodal Processing
4.5 Integrated Multimodality at a Lower Order (Stimulus Orientation)
4.6 Higher-Order Multimodal Integration (Perceptual Binding)
4.7 Gains in Performance From Multisensory Stimulation
4.8 Amplitude Envelope and Alarm Design
4.9 Recent Trends in Alarm Tone Design for Medical Devices
4.10 Percussive Tone Integration in Multimodal User Interfaces
4.11 Software in Hospitals
4.12 Brain–Machine Interface (BCI) Outfit
4.13 BCI Sensors and Techniques
4.13.1 EEG
4.13.2 ECoG
4.13.3 ECG
4.13.4 EMG
4.13.5 MEG
4.13.6 FMRI
4.14 New Generation Advanced Human-Machine Interface
4.15 Conclusion
References
5. Smart Patient Engagement through Robotics
Rakhi Mohan, A. Arun Prakash, Uma Devi N., Anjali Sharma S., Aiswarya Babu N. and Thennarasi P.
5.1 Introduction
5.1.1 Robotics in Healthcare
5.1.2 Patient Engagement Tasks (Front End)
5.1.2.1 Robotics in Nursing, Patient Handling, and Support
5.1.2.2 Robotics in Patient Reception
5.1.2.3 Robotics in Ambulance Services
5.1.2.4 Robotics in Serving (Food and Medicine)
5.1.2.5 Robotics in Surgery and Surgical Assistance
5.1.2.6 Robotics in Cleaning, Moping, Spraying and Disinfecting
5.1.2.7 Robotics in Physiotherapy, Radiology, Lab Diagnostics and Rehabilitation
(Exoskeletons)
5.1.2.8 Robotics in Tele-Presence
5.1.2.9 Robotics in Hospital Kitchen and Pantry Management
5.1.2.10 Robotics in Outdoor Medicine Delivery
5.1.2.11 Robotics in Home Healthcare
5.1.3 Documentation and Other Hospital Management Tasks (Back End)
5.1.3.1 Robotics in Patient Data Feeding and Storing
5.1.3.2 Robotics in Data Mining
5.1.3.3 Robotics in Job Allocation to Hospital Staffs
5.1.3.4 Robotics in Payroll Management
5.1.3.5 Robotics in Medicine and Medical Equipment Logistics
5.1.3.6 Robotics in Medical Waste Residual Management
5.2 Theoretical Framework
5.3 Objectives
5.4 Research Methodology
5.5 Primary and Secondary Data
5.6 Factors for Consideration
5.6.1 Patient Demographics
5.6.2 Hospital/Health Institutes Demographics
5.6.3 Patient Perception Factors
5.6.4 Hospital’s Feasibility Factors and Hospital’s Economic Factors for Implementation
5.7 Robotics Implementation
5.8 Tools for Analysis
5.9 Analysis of Patient’s Perception
5.10 Review of Literature
5.11 Hospitals Considered for the Study (Through Indirect Sources)
5.12 Analysis and Interpretation
5.12.1 Crosstabulation
5.12.2 Regression and Model Fit
5.12.3 Factor Analysis
5.12.4 Regression Analysis
5.12.5 Descriptive Statistics
5.13 Conclusion
References
Annexure
6. Accelerating Development of Medical Devices Using Human-Machine Interface
Dipanjan Karati, Swarupananda Mukherjee, Souvik Roy and Bhupendra G. Prajapati
6.1 Introduction
6.2 HMI Machineries
6.3 Brain–Computer Interface and HMI
6.4 HMI for a Mobile Medical Exoskeleton
6.5 Human Artificial Limb and Robotic Surgical Treatment by HMI
6.6 Cognitive Enhancement by HMI
6.7 Soft Electronics for the Skin Using HMI
6.8 Safety Considerations
6.9 Conclusion
References
7. The Role of a Human-Machine Interaction (HMI) System on the Medical Devices
Zahra Alidousti Shahraki and Mohsen Aghabozorgi Nafchi
7.1 Introduction
7.2 Machine Learning for HCI Systems
7.3 Patient Experience
7.4 Cognitive Science
7.5 HCI System Based on Image Processing
7.5.1 Patient’s Facial Expression
7.5.2 Gender and Age
7.5.3 Emotional Intelligence
7.6 Blockchain
7.7 Virtual Reality
7.8 The Challenges in Designing HCI Systems for Medical Devices
7.9 Conclusion
References
8. Human-Machine Interaction in Leveraging the Concept of Telemedicine
Dipa K. Israni and Nandita S. Chawla
8.1 Introduction
8.2 Innovative Development in HMI Technologies and Its Use in Telemedicine
8.2.1 Nanotechnology
8.2.2 The Internet of Things (IoT)
8.2.3 Internet of Medical Things (IoMT)
8.2.3.1 Motion Detection Sensors
8.2.3.2 Pressure Sensors
8.2.3.3 Temperature Sensors
8.2.3.4 Monitoring Cardiovascular Disease
8.2.3.5 Glucose Level Monitoring
8.2.3.6 Asthma Monitoring
8.2.3.7 GPS Smart Soles and Motion Detection Sensors
8.2.3.8 Wireless Fetal Monitoring
8.2.3.9 Smart Clothing
8.2.4 AI
8.2.5 Machine Learning Techniques
8.2.6 Deep Learning
8.2.7 Home Monitoring Devices, Augmented and Virtual
8.2.8 Drone Technology
8.2.9 Robotics
8.2.9.1 Robotics in Healthcare
8.2.9.2 History of Robotics
8.2.9.3 Tele-Surgery/Remote Surgery
8.2.10 5G Technology
8.2.11 6G
8.2.12 Big Data
8.2.13 Cloud Computing
8.2.14 Blockchain
8.2.14.1 Clinical Trials
8.2.14.2 Patient Records
8.2.14.3 Drug Tracking
8.2.14.4 Device Tracking
8.3 Advantages of Utilizing HMI in Healthcare for Telemedicine
8.3.1 Emotive Telemedicine
8.3.2 Ambient Assisted Living
8.3.2.1 Wearable Sensors for AAL
8.3.3 Monitoring and Controlling Intelligent Self-Management and Wellbeing
8.3.4 Intelligent Reminders for Treatment, Compliance, and Adherence
8.3.5 Personalized and Connected Healthcare
8.4 Obstacles to the Utilize, Accept, and Implement HMI in Telemedicine
8.4.1 Data Inconsistency and Disintegration
8.4.2 Standards and Interoperability are Lacking
8.4.3 Intermittent or Non-Existent Network Connectivity
8.4.4 Sensor Data Unreliability and Invalidity
8.4.5 Privacy, Confidentiality, and Data Consistency
8.4.6 Scalability Issues
8.4.7 Health Consequences
8.4.8 Clinical Challenges
8.4.9 Nanosensors and Biosensors Offer Health Risks
8.4.10 Limited Computing Capability and Inefficient Energy Use
8.4.11 Memory Space is Limited
8.4.12 Models of Digital Technology are Rigid and Sophisticated
8.4.13 Regulatory Frameworks
8.4.14 Incorporated IT Infrastructure
8.4.15 Misalignment with Nations’ e-Health Policies
8.4.16 Implementing Costs
8.4.17 Operational and Systems Challenges
8.4.18 Logistical Challenges
8.4.19 Communication Barriers
8.4.20 Unique Challenges
8.5 Conclusions
References
9. Making Hospital Environment Friendly for People: A Concept of HMI
Rihana Begum P., Badrud Duza Mohammad, Saravana Kumar A. and Muhasina K.M.
9.1 Introduction
9.2 A Scenario for Ubiquitous Computing and Ambient Intelligence
9.3 Emergence of Ambient Intelligence
9.4 Framework for Advanced Human-Machine Interfaces
9.5 Brain Computer Interface (BCI)
9.5.1 The BCI System: An Introduction
9.5.2 The Characteristics of a BCI
9.5.2.1 Dependent and Independent BCIs
9.5.2.2 Motor Disabilities: Options for Restoring Function
9.5.3 Components of BCI
9.5.4 Structure of the Human Brain and Its Signals
9.5.4.1 A Signal That is Evoked
9.5.4.2 Spontaneous Signals
9.5.4.3 Hybrid Signals
9.6 Development in MHI Technologies and Their Applications
9.7 Techniques of Signal Acquisition and Processing Applied to HMI
9.8 Hospital-Friendly Environment for Patients
9.8.1 Physiological Study State
9.8.1.1 Nature
9.8.1.2 Music
9.8.2 Pain State
9.8.2.1 Nature
9.8.2.2 Natural Light
9.8.3 Sleep
9.8.3.1 Nature Images
9.8.4 Patient Experience
9.8.4.1 Patient’s Satisfaction
9.8.4.2 Interaction
9.9 Applications of HMI for Patient-Friendly Hospital Environment
9.9.1 Healthcare and Engineering
9.9.2 Controls for Robotic Surgery and Human Prosthetics
9.9.3 Sensory Substitution System
9.9.4 Mobile Robots and Wheelchairs With Neural Interfaces
9.9.5 Technology on Biometric System
9.9.6 Enhancement of Cognition Level
9.9.7 fNIRS-EEG Multimodal BCI as a Future Perspective
9.10 Conclusion
References
Part II : Emerging Application and Regulatory Prospects of HMI in Healthcare
10. HMI: Disruption in the Neural Healthcare Industry
Preetam L. Nikam, Amol U. Gayke, Pavan S. Avhad, Rahul B. Bhabad and Rishabha Malviya
10.1 Introduction
10.2 Stimulation of Muscles
10.3 Cochlear Implants
10.3.1 Implants for Cochlear
10.3.2 Prosthetics for Ears
10.4 Peripheral Nervous System Interaction
10.5 Sleeve Electrodes
10.6 Flat-Interfaced Nerve Electrodes
10.7 Transverse and Longitudinal Intrafascicular Electrode (LIFE and TIME)
10.8 Multi-Channel Arrays That Penetrate
10.8.1 Numerous-Channel Arrays That Penetrate
10.9 Spinal Cord Stimulation and Central Nervous System Interaction
10.9.1 Cortical Connections
10.9.2 Stimulation of the Auditory Nucleus and Ganglions
10.9.3 Stimulation of the Deep Brain
10.10 Computer–Brain Interfaces
10.11 Conclusion
References
11. Dynamics of EHR in M-Healthcare Application
Eva Kaushik and Rohit Kaushik
11.1 Introduction
11.1.1 Why EHR is Needed in the Nation?
11.1.2 Empowering Patients in Healthcare Management
11.1.3 Data Management in EHR
11.1.4 Long-Term Architectural Approach
11.2 Background Related Work
11.3 Methodology
11.3.1 Use-Cases on Ground Base Reality
11.3.2 Integration of Technology to Solve Healthcare Issues
11.3.3 Workflow
11.4 Tools and Technologies
11.5 Limitations
11.6 Future Scope
11.6.1 Personalized EHR Cards
11.7 Discussion
11.7.1 Electronic Health Records and Personal Health Records
11.7.2 Physicians’ Review Toward EHR
11.7.3 Interoperability
11.8 Conclusion
References
12. Role of Human-Machine Interface in the Biomedical Device Development to Handle COVID-19 Pandemic Situation in an Efficient Way
Soma Datta and Nabendu Chaki
12.1 Introduction: Background and Driving Forces
12.1.1 Observed Scenario During May 2021
12.1.1.1 Transmission Medium
12.1.2 Limitation of Vaccine Technology
12.1.3 Adverse Effect of Protective Measure
12.1.4 Revoking of Restrictions Causes Surges in Pandemic
12.2 Methods
12.2.1 Determine Major Influencing Factors
12.2.2 Analyzed the Selected Influencing Factor
12.2.2.1 Evidence 1
12.2.2.2 Evidence 2
12.2.2.3 Evidence 3
12.2.3 Managing Mechanism to Reduce the Spreading Rate of COVID-19
12.2.4 The Households Health Safety Systems to Disinfect Outdoor Cloths
12.2.4.1 Present Households Disinfect Systems for Cloth and Personal Belonging
12.2.4.2 The Outline of Households Health Safety Systems to Disinfect Outdoor Clothes
12.2.5 Upgradation of Individual Room Air Conditioning System
12.2.5.1 The Outline of the AI-Based Room Ventilator System
12.2.6 Design of Next-Generation Mask
12.3 Results
12.4 Conclusion
Acknowledgment
References
13. Role of HMI in the Drug Manufacturing Process
Biswajit Basu, Kevinkumar Garala and Bhupendra G. Prajapati
13.1 Introduction
13.1.1 Dialogue Systems
13.2 Types of HMI
13.3 Advantages and Disadvantages of HMI
13.4 Roles of HMI in the Pharmaceutical Manufacturing Process
13.5 Common Applications for Human-Machine Interfaces
13.5.1 Automotive Dashboards
13.5.2 Monitoring of Machinery and Equipment
13.5.3 Digital Displays
13.5.4 Building Automation
13.5.5 Video and Audio Production
13.6 Healthcare System-Based Human–Computer Interaction
13.6.1 Healthcare System
13.6.2 Teaching of Medicine and Physiology
13.7 Performance Test of Healthcare System Based on HCI
13.7.1 HCI-Based Medical Teaching System
13.8 Human-Machine Interface for Healthcare and Rehabilitation
13.8.1 Ambient Intelligence and Ubiquitous Computing Scenario
13.8.2 The Advanced Human-Machine Interface Framework
13.9 Human-Machine Interface for Research Reactor: Instrumentation and Control System
13.10 Future Scope of Human-Machine Interface (HMI)
13.11 Conclusion
References
14. Breaking the Silence: Brain–Computer Interface for Communication
Preetam L. Nikam, Sheetal Wagh, Sarika Shinde, Abhishek Mokal, Smita Andhale, Prathmesh Wagh, Vivek Bhosale and Rishabha Malviya
14.1 Introduction
14.2 Survey of BCI
14.3 Techniques of BCI
14.3.1 Potentials Associated With an Event
14.3.2 Cortical Gradual Potentials
14.3.3 Evoked Visual Possibilities
14.3.4 Sensorimotor Rhythms
14.3.5 Motor Imagery
14.4 BCI Components
14.4.1 Signal Acquisition
14.4.2 Signal Processing
14.4.3 Extraction of Features
14.4.4 Signal Categorization
14.5 BCI Signal Acquisition Methods
14.6 BCI Invasion
14.7 BCI With Limited Invasion
14.8 BCI Not Invasive
14.9 BCI Applications
14.9.1 Movement
14.9.2 Recreation
14.9.3 Reconstruction
14.9.4 Interaction
14.9.5 Interaction With Others
14.9.6 Diagnosis and Treatment of Depression
14.9.7 Reduces Healthcare Costs
14.10 BCI Healthcare Challenges
14.10.1 Ethical Difficulties
14.10.2 Goodwill
14.10.3 Legality
14.10.4 Freedom of Privacy
14.10.5 Issues With Standardization
14.10.6 Problems With Reliability
14.10.7 Prolonged Training Process
14.10.8 Expensive Acquisition and Control
14.11 Conclusion
References
15. Regulatory Perspective: Human-Machine Interfaces
Artiben Patel, Ravi Patel, Rakesh Patel, Bhupendra Prajapati and Shivani Jani
Abbreviations
15.1 Introduction
15.2 Why are Regulations Needed?
15.2.1 Safety
15.2.2 Uniform Requirements
15.2.3 Promote Innovation
15.2.4 Free Movement of Goods
15.2.5 Compensation
15.2.6 Fostering Innovation
15.3 US Regulatory Perspective
15.3.1 History of Medical Device Regulation and Its Supervision in the United States
15.3.2 Classification of Medical Devices
15.3.3 Reclassification
15.3.4 How to Determine if the Product is a Medical Device or How to Classify the Medical Device
15.3.5 Device Development Process
15.3.6 Overview of Device Regulations
15.3.7 Quality and Compliance of Medical Devices
15.3.8 Human Factors and Medical Devices
15.3.9 Continuous Improvement of Regulations
15.4 Conclusion
References
16. Towards the Digitization of Healthcare Record Management
Shivani Patel, Bhavinkumar Gayakvad, Ravisinh Solanki, Ravi Patel and Dignesh Khunt
16.1 Introduction
16.2 Digital Health Records: Concept and Organization
16.3 Mechanism and Operation of Digital Health Record
16.3.1 Physician-Hosted EHR
16.3.2 Remotely-Hosted EHR
16.3.2.1 Subsidized System
16.3.2.2 Dedicated Hosted System
16.3.2.3 Cloud-Based or Internet-Based Computing
16.4 Benefits of Digital Health Records
16.4.1 Security
16.4.2 Costs
16.4.3 Access
16.4.4 Storage
16.4.5 Accuracy and Readability
16.4.6 Practice Management
16.4.7 Quality of Care
16.5 Limitations of Digital Health Records
16.5.1 Completeness
16.5.2 Correctness
16.5.3 Complexity
16.5.4 Acceptability
16.5.4.1 People
16.5.4.2 Hardware, Software and Network
16.5.4.3 Procedure
16.6 Risk & Problems Associated With the System
16.6.1 Lack of Concord
16.6.2 Privacy and Data Security Issues
16.6.3 Problems in Patient Matching
16.6.4 Alteration of Algorithms in Decision-Support Models
16.6.5 Increased Workload of Clinicians
16.7 Future Benefits
16.8 Miscellaneous
16.8.1 Policies Regarding Data Exchange
16.8.1.1 Directed Exchange
16.8.1.2 Query-Based Exchange
16.8.1.3 Consumer-Mediated Exchange
16.8.2 Current Practices of Digital Health Records
16.8.2.1 India
16.8.2.2 Australia
16.8.2.3 Canada
16.8.2.4 USA
16.8.2.5 China
16.8.3 Data Analysis
16.8.4 Role and Benefits to the Stakeholders
16.8.4.1 Advantages to the Patient
16.8.4.2 Advantages to the Healthcare Providers
16.8.4.3 Advantages to the Society
16.9 Conclusion
References
17. Intelligent Healthcare Supply Chain
Chirag Kalaria, Shambhavi Singh and Bhupendra G. Prajapati
17.1 Introduction
17.2 Supply Chain – Method Networking?
17.3 Healthcare Supply Chain and Steps Involved
17.4 Importance of HSC
17.5 Risks and Complexities Affecting the Globally Distributed HSC
17.5.1 Legacy HSC
17.5.1.1 SWOT Analysis of Legacy HSC
17.5.2 What is an Intelligent Supply Chain?
17.5.3 Difference Between Legacy HSC and Intelligent HSC
17.6 Technologies Come to Aid to Build an Intelligent HSC
17.6.1 HMI
17.6.2 AI
17.6.3 ML/DL
17.7 Blockchain
17.8 Robotics
17.9 Cloud Computing
17.10 Big Data Analytics (BDA)
17.11 Industry 4.0
17.12 Internet of Things (IoT)
17.13 Digital Twins
17.14 Supply Chain Control Tower
17.15 Predictive Maintenance
17.16 A Digital Transformation Roadmap
17.17 Prerequisite for Designing Intelligent HSC
17.18 HMI—Usage in HSC Management
17.19 HMI—A Face of the Supply Chain Control Tower
17.20 The Intelligent Future of the Healthcare Industry
17.21 Conclusion
References
Index

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