Human-Machine Interface

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.

• Foreword xxiiiPreface xxvAcknowledgement xxviiPart I: Advanced Patient Care with HMI 11 Introduction to Human-Machine Interface 3Shama Mujawar, Aarohi Deshpande, Aarohi Gherkar, Samson Eugin Simon and Bhupendra Prajapati1.1 Introduction 41.2 Types of HMI 61.2.1 The Pushbutton Replacer 61.2.2 The Data Handler 71.2.3 The Overseer 71.3 Transformation of HMI 71.4 Importance and COVID Relevance With HMI 91.5 Applications 111.5.1 Biological Applications 121.5.1.1 HMI Signal Detection and Procurement Method 121.5.1.2 Healthcare and Rehabilitation 121.5.1.3 Magnetoencephalography 131.5.1.4 Flexible Hybrid Electronics (FHE) 131.5.1.5 Robotic-Assisted Surgeries 131.5.1.6 Flexible Microstructural Pressure Sensors 141.5.1.7 Biomedical Applications 141.5.1.8 Cb-hmi 151.5.1.9 HMI in Medical Devices 151.5.2 Industrial Applications 151.5.2.1 Metal Industries 161.5.2.2 Video Game Industry 161.5.2.3 Aerospace and Defense 161.5.2.4 Water Purification Plant HMI Based on Multi-Agent Systems (MAS) 171.5.2.5 Virtual and Haptic Interfaces 171.5.2.6 Space Crafts 171.5.2.7 Car Wash System 181.5.2.8 Pharmaceutical Processing and Industries 181.6 Challenges 181.7 Conclusion and Future Prospects 19References 202 Improving Healthcare Practice by Using HMI Interface 25Vaibhav Verma, Vivek Dave and Pranay Wal2.1 Background of Human-Machine Interaction 262.2 Introduction 262.2.1 Healthcare Practice 262.2.2 Human-Machine Interface System in Healthcare 262.3 Evolution of HMI Design 272.3.1 HMI Design 1.0 272.3.2 HMI Design 2.0 282.3.3 HMI Design 3.0 282.3.4 HMI Design 4.0 282.4 Anatomy of Human Brain 282.5 Signal Associated With Brain 312.5.1 Evoked Signals 312.5.2 Spontaneous Signals 322.5.3 Hybrid Signals 322.6 HMI Signal Processing and Acquisition Methods 322.7 Human-Machine Interface–Based Healthcare System 362.7.1 Healthcare Practice System 362.7.1.1 Healthcare Practice 362.7.1.2 Current State of Healthcare Provision 372.7.1.3 Concerns With Domestic Healthcare 382.7.2 Medical Education System 382.7.2.1 Traditional and Modern Way of Providing Medical Education 382.8 Working Model of HMI 382.9 Challenges and Limitations of HMI Design 402.10 Role of HMI in Healthcare Practice 402.10.1 Simple to Clean 412.10.2 High Chemical Tolerance 412.10.3 Transportable and Light 412.10.4 Enhancing Communication 412.11 Application of HMI Technology in Medical Fields 422.11.1 Medical and Rehabilitative Engineering Using HMI 422.11.2 Controls for Robotic Surgery and Human Prosthetics 452.11.3 Sensory Replacement Mechanism 472.11.4 Wheelchairs and Moving Robots Along With Neurological Interface 482.11.5 Cognitive Improvement 492.12 Conclusion and Future Perspective 51References 523 Human-Machine Interface and Patient Safety 59Arun Kumar Singh and Rishabha Malviya3.1 Introduction 593.2 Detecting Anesthesia-Related Drug Administration Errors and Predicting Their Impact 603.2.1 Methodological Difficulties in Studying Rare, Dangerous Phenomena 613.2.2 Consequences of Errors 633.2.3 Lessons From Other Industries 653.2.4 The Double-Human Interface 663.2.5 The Culture of Denial and Effort 673.2.6 Poor Labeling 683.3 Systematic Approaches to Improve Patient Safety During Anesthesia 693.3.1 Design Principles 693.3.2 Evidence of Safety Gains 703.3.3 Consistent Color-Coding 713.3.4 The Codonics Label System 723.4 The Triumph of Software 733.4.1 Software in Hospitals 743.4.2 Software in Anesthesia 753.4.3 The Alarm Problem 763.5 Environments that Audit Themselves 773.6 New Risks and Dangers 773.7 Conclusion 78References 794 Human-Machine Interface Improving Quality of Patient Care 89Rishav Sharma and Rishabha Malviya4.1 Introduction 904.2 An Advanced Framework for Human-Machine Interaction 924.2.1 A Simulated Workplace Safety and Health Program 924.3 Human–Computer Interaction (HCI) 934.4 Multimodal Processing 954.5 Integrated Multimodality at a Lower Order (Stimulus Orientation) 964.6 Higher-Order Multimodal Integration (Perceptual Binding) 964.7 Gains in Performance From Multisensory Stimulation 974.8 Amplitude Envelope and Alarm Design 984.9 Recent Trends in Alarm Tone Design for Medical Devices 994.10 Percussive Tone Integration in Multimodal User Interfaces 994.11 Software in Hospitals 1004.12 Brain–Machine Interface (BCI) Outfit 1014.13 BCI Sensors and Techniques 1014.13.1 Eeg 1024.13.2 ECoG 1024.13.3 Ecg 1024.13.4 Emg 1034.13.5 Meg 1034.13.6 Fmri 1034.14 New Generation Advanced Human-Machine Interface 1044.15 Conclusion 105References 1065 Smart Patient Engagement through Robotics 115Rakhi Mohan, A. Arun Prakash, Uma Devi N., Anjali Sharma S., Aiswarya Babu N. and Thennarasi P.5.1 Introduction 1165.1.1 Robotics in Healthcare 1165.1.2 Patient Engagement Tasks (Front End) 1185.1.2.1 Robotics in Nursing, Patient Handling, and Support 1185.1.2.2 Robotics in Patient Reception 1195.1.2.3 Robotics in Ambulance Services 1205.1.2.4 Robotics in Serving (Food and Medicine) 1205.1.2.5 Robotics in Surgery and Surgical Assistance 1215.1.2.6 Robotics in Cleaning, Moping, Spraying and Disinfecting 1225.1.2.7 Robotics in Physiotherapy, Radiology, Lab Diagnostics and Rehabilitation (Exoskeletons) 1225.1.2.8 Robotics in Tele-Presence 1225.1.2.9 Robotics in Hospital Kitchen and Pantry Management 1235.1.2.10 Robotics in Outdoor Medicine Delivery 1235.1.2.11 Robotics in Home Healthcare 1235.1.3 Documentation and Other Hospital Management Tasks (Back End) 1245.1.3.1 Robotics in Patient Data Feeding and Storing 1245.1.3.2 Robotics in Data Mining 1245.1.3.3 Robotics in Job Allocation to Hospital Staffs 1255.1.3.4 Robotics in Payroll Management 1255.1.3.5 Robotics in Medicine and Medical Equipment Logistics 1265.1.3.6 Robotics in Medical Waste Residual Management 1265.2 Theoretical Framework 1265.3 Objectives 1275.4 Research Methodology 1275.5 Primary and Secondary Data 1275.6 Factors for Consideration 1275.6.1 Patient Demographics 1275.6.2 Hospital/Health Institutes Demographics 1275.6.3 Patient Perception Factors 1285.6.4 Hospital’s Feasibility Factors and Hospital’s Economic Factors for Implementation 1285.7 Robotics Implementation 1285.8 Tools for Analysis 1295.9 Analysis of Patient’s Perception 1295.10 Review of Literature 1295.11 Hospitals Considered for the Study (Through Indirect Sources) 1315.12 Analysis and Interpretation 1335.12.1 Crosstabulation 1335.12.2 Regression and Model Fit 1375.12.3 Factor Analysis 1405.12.4 Regression Analysis 1475.12.5 Descriptive Statistics 1495.13 Conclusion 153References 153Annexure 1546 Accelerating Development of Medical Devices Using Human-Machine Interface 161Dipanjan Karati, Swarupananda Mukherjee, Souvik Roy and Bhupendra G. Prajapati6.1 Introduction 1626.2 HMI Machineries 1646.3 Brain–Computer Interface and HMI 1656.4 HMI for a Mobile Medical Exoskeleton 1666.5 Human Artificial Limb and Robotic Surgical Treatment by HMI 1676.6 Cognitive Enhancement by HMI 1706.7 Soft Electronics for the Skin Using HMI 1716.8 Safety Considerations 1736.9 Conclusion 174References 1747 The Role of a Human-Machine Interaction (HMI) System on the Medical Devices 183Zahra Alidousti Shahraki and Mohsen Aghabozorgi Nafchi7.1 Introduction 1847.2 Machine Learning for HCI Systems 1857.3 Patient Experience 1877.4 Cognitive Science 1907.5 HCI System Based on Image Processing 1927.5.1 Patient’s Facial Expression 1937.5.2 Gender and Age 1947.5.3 Emotional Intelligence 1997.6 Blockchain 2017.7 Virtual Reality 2037.8 The Challenges in Designing HCI Systems for Medical Devices 2067.9 Conclusion 207References 2088 Human-Machine Interaction in Leveraging the Concept of Telemedicine 211Dipa K. Israni and Nandita S. Chawla8.1 Introduction 2128.2 Innovative Development in HMI Technologies and Its Use in Telemedicine 2138.2.1 Nanotechnology 2148.2.2 The Internet of Things (IoT) 2158.2.3 Internet of Medical Things (IoMT) 2168.2.3.1 Motion Detection Sensors 2178.2.3.2 Pressure Sensors 2178.2.3.3 Temperature Sensors 2178.2.3.4 Monitoring Cardiovascular Disease 2178.2.3.5 Glucose Level Monitoring 2178.2.3.6 Asthma Monitoring 2178.2.3.7 GPS Smart Soles and Motion Detection Sensors 2188.2.3.8 Wireless Fetal Monitoring 2188.2.3.9 Smart Clothing 2188.2.4 Ai 2198.2.5 Machine Learning Techniques 2208.2.6 Deep Learning 2218.2.7 Home Monitoring Devices, Augmented and Virtual 2228.2.8 Drone Technology 2238.2.9 Robotics 2238.2.9.1 Robotics in Healthcare 2248.2.9.2 History of Robotics 2248.2.9.3 Tele-Surgery/Remote Surgery 2248.2.10 5G Technology 2258.2.11 6g 2258.2.12 Big Data 2268.2.13 Cloud Computing 2268.2.14 Blockchain 2278.2.14.1 Clinical Trials 2288.2.14.2 Patient Records 2288.2.14.3 Drug Tracking 2288.2.14.4 Device Tracking 2298.3 Advantages of Utilizing HMI in Healthcare for Telemedicine 2308.3.1 Emotive Telemedicine 2308.3.2 Ambient Assisted Living 2328.3.2.1 Wearable Sensors for AAL 2328.3.3 Monitoring and Controlling Intelligent Self-Management and Wellbeing 2338.3.4 Intelligent Reminders for Treatment, Compliance, and Adherence 2338.3.5 Personalized and Connected Healthcare 2338.4 Obstacles to the Utilize, Accept, and Implement HMI in Telemedicine 2348.4.1 Data Inconsistency and Disintegration 2348.4.2 Standards and Interoperability are Lacking 2348.4.3 Intermittent or Non-Existent Network Connectivity 2348.4.4 Sensor Data Unreliability and Invalidity 2358.4.5 Privacy, Confidentiality, and Data Consistency 2358.4.6 Scalability Issues 2358.4.7 Health Consequences 2358.4.8 Clinical Challenges 2368.4.9 Nanosensors and Biosensors Offer Health Risks 2368.4.10 Limited Computing Capability and Inefficient Energy Use 2368.4.11 Memory Space is Limited 2378.4.12 Models of Digital Technology are Rigid and Sophisticated 2378.4.13 Regulatory Frameworks 2378.4.14 Incorporated IT Infrastructure 2378.4.15 Misalignment with Nations’ e-Health Policies 2388.4.16 Implementing Costs 2388.4.17 Operational and Systems Challenges 2388.4.18 Logistical Challenges 2398.4.19 Communication Barriers 2398.4.20 Unique Challenges 2398.5 Conclusions 239References 2409 Making Hospital Environment Friendly for People: A Concept of HMI 247Rihana Begum P., Badrud Duza Mohammad, Saravana Kumar A. and Muhasina K.M.9.1 Introduction 2489.2 A Scenario for Ubiquitous Computing and Ambient Intelligence 2499.3 Emergence of Ambient Intelligence 2509.4 Framework for Advanced Human-Machine Interfaces 2519.5 Brain Computer Interface (BCI) 2529.5.1 The BCI System: An Introduction 2529.5.2 The Characteristics of a BCI 2539.5.2.1 Dependent and Independent BCIs 2539.5.2.2 Motor Disabilities: Options for Restoring Function 2539.5.3 Components of BCI 2549.5.4 Structure of the Human Brain and Its Signals 2549.5.4.1 A Signal That is Evoked 2569.5.4.2 Spontaneous Signals 2569.5.4.3 Hybrid Signals 2579.6 Development in MHI Technologies and Their Applications 2579.7 Techniques of Signal Acquisition and Processing Applied to HMI 2589.8 Hospital-Friendly Environment for Patients 2609.8.1 Physiological Study State 2609.8.1.1 Nature 2609.8.1.2 Music 2609.8.2 Pain State 2609.8.2.1 Nature 2609.8.2.2 Natural Light 2619.8.3 Sleep 2619.8.3.1 Nature Images 2619.8.4 Patient Experience 2619.8.4.1 Patient’s Satisfaction 2619.8.4.2 Interaction 2629.9 Applications of HMI for Patient-Friendly Hospital Environment 2639.9.1 Healthcare and Engineering 2639.9.2 Controls for Robotic Surgery and Human Prosthetics 2659.9.3 Sensory Substitution System 2669.9.4 Mobile Robots and Wheelchairs With Neural Interfaces 2679.9.5 Technology on Biometric System 2689.9.6 Enhancement of Cognition Level 2699.9.7 fNIRS-EEG Multimodal BCI as a Future Perspective 2709.10 Conclusion 270References 271Part II : Emerging Application and Regulatory Prospects of HMI in Healthcare 27910 HMI: Disruption in the Neural Healthcare Industry 281Preetam L. Nikam, Amol U. Gayke, Pavan S. Avhad, Rahul B. Bhabad and Rishabha Malviya10.1 Introduction 28210.2 Stimulation of Muscles 28310.3 Cochlear Implants 28310.3.1 Implants for Cochlear 28310.3.2 Prosthetics for Ears 28410.4 Peripheral Nervous System Interaction 28410.5 Sleeve Electrodes 28510.6 Flat-Interfaced Nerve Electrodes 28710.7 Transverse and Longitudinal Intrafascicular Electrode (LIFE and TIME) 28710.8 Multi-Channel Arrays That Penetrate 28810.8.1 Numerous-Channel Arrays That Penetrate 28810.9 Spinal Cord Stimulation and Central Nervous System Interaction 28910.9.1 Cortical Connections 28910.9.2 Stimulation of the Auditory Nucleus and Ganglions 29010.9.3 Stimulation of the Deep Brain 29010.10 Computer–Brain Interfaces 29010.11 Conclusion 291References 29111 Dynamics of EHR in M-Healthcare Application 295Eva Kaushik and Rohit Kaushik11.1 Introduction 29611.1.1 Why EHR is Needed in the Nation? 29611.1.2 Empowering Patients in Healthcare Management 29711.1.3 Data Management in EHR 29811.1.4 Long-Term Architectural Approach 29811.2 Background Related Work 29911.3 Methodology 30011.3.1 Use-Cases on Ground Base Reality 30011.3.2 Integration of Technology to Solve Healthcare Issues 30111.3.3 Workflow 30211.4 Tools and Technologies 30311.5 Limitations 30411.6 Future Scope 30511.6.1 Personalized EHR Cards 30511.7 Discussion 30611.7.1 Electronic Health Records and Personal Health Records 30611.7.2 Physicians’ Review Toward EHR 30711.7.3 Interoperability 30711.8 Conclusion 308References 30812 Role of Human-Machine Interface in the Biomedical Device Development to Handle COVID-19 Pandemic Situation in an Efficient Way 311Soma Datta and Nabendu Chaki12.1 Introduction: Background and Driving Forces 31212.1.1 Observed Scenario During May 2021 31412.1.1.1 Transmission Medium 31412.1.2 Limitation of Vaccine Technology 31412.1.3 Adverse Effect of Protective Measure 31412.1.4 Revoking of Restrictions Causes Surges in Pandemic 31512.2 Methods 31512.2.1 Determine Major Influencing Factors 31612.2.2 Analyzed the Selected Influencing Factor 31712.2.2.1 Evidence 1 31812.2.2.2 Evidence 2 31812.2.2.3 Evidence 3 32012.2.3 Managing Mechanism to Reduce the Spreading Rate of COVID- 19 32012.2.4 The Households Health Safety Systems to Disinfect Outdoor Cloths 32112.2.4.1 Present Households Disinfect Systems for Cloth and Personal Belonging 32112.2.4.2 The Outline of Households Health Safety Systems to Disinfect Outdoor Clothes 32212.2.5 Upgradation of Individual Room Air Conditioning System 32412.2.5.1 The Outline of the AI-Based Room Ventilator System 32412.2.6 Design of Next-Generation Mask 32412.3 Results 32512.4 Conclusion 325Acknowledgment 325References 32613 Role of HMI in the Drug Manufacturing Process 329Biswajit Basu, Kevinkumar Garala and Bhupendra G. Prajapati13.1 Introduction 33013.1.1 Dialogue Systems 33113.2 Types of HMI 33313.3 Advantages and Disadvantages of HMI 33413.4 Roles of HMI in the Pharmaceutical Manufacturing Process 33913.5 Common Applications for Human-Machine Interfaces 34313.5.1 Automotive Dashboards 34313.5.2 Monitoring of Machinery and Equipment 34413.5.3 Digital Displays 34413.5.4 Building Automation 34413.5.5 Video and Audio Production 34413.6 Healthcare System-Based Human–Computer Interaction 34513.6.1 Healthcare System 34513.6.2 Teaching of Medicine and Physiology 34613.7 Performance Test of Healthcare System Based on HCI 34913.7.1 HCI-Based Medical Teaching System 34913.8 Human-Machine Interface for Healthcare and Rehabilitation 34913.8.1 Ambient Intelligence and Ubiquitous Computing Scenario 34913.8.2 The Advanced Human-Machine Interface Framework 35013.9 Human-Machine Interface for Research Reactor: Instrumentation and Control System 35113.10 Future Scope of Human-Machine Interface (HMI) 35213.11 Conclusion 353References 35314 Breaking the Silence: Brain–Computer Interface for Communication 357Preetam L. Nikam, Sheetal Wagh, Sarika Shinde, Abhishek Mokal, Smita Andhale, Prathmesh Wagh, Vivek Bhosale and Rishabha Malviya14.1 Introduction 35814.2 Survey of BCI 35914.3 Techniques of BCI 36114.3.1 Potentials Associated With an Event 36114.3.2 Cortical Gradual Potentials 36114.3.3 Evoked Visual Possibilities 36114.3.4 Sensorimotor Rhythms 36214.3.5 Motor Imagery 36214.4 BCI Components 36214.4.1 Signal Acquisition 36314.4.2 Signal Processing 36314.4.3 Extraction of Features 36314.4.4 Signal Categorization 36314.5 BCI Signal Acquisition Methods 36414.6 BCI Invasion 36414.7 BCI With Limited Invasion 36414.8 BCI Not Invasive 36414.9 BCI Applications 36514.9.1 Movement 36514.9.2 Recreation 36514.9.3 Reconstruction 36614.9.4 Interaction 36614.9.5 Interaction With Others 36614.9.6 Diagnosis and Treatment of Depression 36614.9.7 Reduces Healthcare Costs 36714.10 BCI Healthcare Challenges 36714.10.1 Ethical Difficulties 36714.10.2 Goodwill 36714.10.3 Legality 36814.10.4 Freedom of Privacy 36814.10.5 Issues With Standardization 36814.10.6 Problems With Reliability 36814.10.7 Prolonged Training Process 36914.10.8 Expensive Acquisition and Control 36914.11 Conclusion 370References 37015 Regulatory Perspective: Human-Machine Interfaces 375Artiben Patel, Ravi Patel, Rakesh Patel, Bhupendra Prajapati and Shivani JaniAbbreviations 37615.1 Introduction 37615.2 Why are Regulations Needed? 37715.2.1 Safety 37815.2.2 Uniform Requirements 37815.2.3 Promote Innovation 37815.2.4 Free Movement of Goods 37815.2.5 Compensation 37915.2.6 Fostering Innovation 37915.3 US Regulatory Perspective 37915.3.1 History of Medical Device Regulation and Its Supervision in the United States 38015.3.2 Classification of Medical Devices 38415.3.3 Reclassification 38515.3.4 How to Determine if the Product is a Medical Device or How to Classify the Medical Device 38515.3.5 Device Development Process 38715.3.6 Overview of Device Regulations 39115.3.7 Quality and Compliance of Medical Devices 39315.3.8 Human Factors and Medical Devices 39515.3.9 Continuous Improvement of Regulations 40215.4 Conclusion 407References 40716 Towards the Digitization of Healthcare Record Management 411Shivani Patel, Bhavinkumar Gayakvad, Ravisinh Solanki, Ravi Patel and Dignesh Khunt16.1 Introduction 41216.2 Digital Health Records: Concept and Organization 41616.3 Mechanism and Operation of Digital Health Record 41916.3.1 Physician-Hosted EHR 42016.3.2 Remotely-Hosted EHR 42016.3.2.1 Subsidized System 42016.3.2.2 Dedicated Hosted System 42116.3.2.3 Cloud-Based or Internet-Based Computing 42116.4 Benefits of Digital Health Records 42616.4.1 Security 42616.4.2 Costs 42716.4.3 Access 42716.4.4 Storage 42716.4.5 Accuracy and Readability 42716.4.6 Practice Management 42816.4.7 Quality of Care 42816.5 Limitations of Digital Health Records 42816.5.1 Completeness 42816.5.2 Correctness 42916.5.3 Complexity 42916.5.4 Acceptability 43016.5.4.1 People 43016.5.4.2 Hardware, Software and Network 43016.5.4.3 Procedure 43016.6 Risk & Problems Associated With the System 43116.6.1 Lack of Concord 43116.6.2 Privacy and Data Security Issues 43116.6.3 Problems in Patient Matching 43216.6.4 Alteration of Algorithms in Decision-Support Models 43216.6.5 Increased Workload of Clinicians 43216.7 Future Benefits 43216.8 Miscellaneous 43416.8.1 Policies Regarding Data Exchange 43416.8.1.1 Directed Exchange 43516.8.1.2 Query-Based Exchange 43516.8.1.3 Consumer-Mediated Exchange 43516.8.2 Current Practices of Digital Health Records 43816.8.2.1 India 43816.8.2.2 Australia 43916.8.2.3 Canada 43916.8.2.4 USA 44016.8.2.5 China 44016.8.3 Data Analysis 44216.8.4 Role and Benefits to the Stakeholders 44316.8.4.1 Advantages to the Patient 44316.8.4.2 Advantages to the Healthcare Providers 44416.8.4.3 Advantages to the Society 44416.9 Conclusion 445References 44617 Intelligent Healthcare Supply Chain 449Chirag Kalaria, Shambhavi Singh and Bhupendra G. Prajapati17.1 Introduction 45017.2 Supply Chain – Method Networking? 45117.3 Healthcare Supply Chain and Steps Involved 45117.4 Importance of HSC 45217.5 Risks and Complexities Affecting the Globally Distributed HSC 45317.5.1 Legacy HSC 45317.5.1.1 SWOT Analysis of Legacy HSC 45417.5.2 What is an Intelligent Supply Chain? 45417.5.3 Difference Between Legacy HSC and Intelligent HSC 45617.6 Technologies Come to Aid to Build an Intelligent HSC 45717.6.1 Hmi 45717.6.2 Ai 45817.6.3 Ml/dl 45917.7 Blockchain 46017.8 Robotics 46117.9 Cloud Computing 46317.10 Big Data Analytics (BDA) 46517.11 Industry 4.0 46517.12 Internet of Things (IoT) 46717.13 Digital Twins 46917.14 Supply Chain Control Tower 47017.15 Predictive Maintenance 47217.16 A Digital Transformation Roadmap 47317.17 Prerequisite for Designing Intelligent HSC 47517.18 HMI—Usage in HSC Management 47617.19 HMI—A Face of the Supply Chain Control Tower 47717.20 The Intelligent Future of the Healthcare Industry 47817.21 Conclusion 480References 481Index 483