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 xxiii

Preface xxv

Acknowledgement xxvii

Part I: Advanced Patient Care with HMI 1

1 Introduction to Human-Machine Interface 3
Shama Mujawar, Aarohi Deshpande, Aarohi Gherkar, Samson Eugin Simon and Bhupendra Prajapati

1.1 Introduction 4

1.2 Types of HMI 6

1.2.1 The Pushbutton Replacer 6

1.2.2 The Data Handler 7

1.2.3 The Overseer 7

1.3 Transformation of HMI 7

1.4 Importance and COVID Relevance With HMI 9

1.5 Applications 11

1.5.1 Biological Applications 12

1.5.1.1 HMI Signal Detection and Procurement Method 12

1.5.1.2 Healthcare and Rehabilitation 12

1.5.1.3 Magnetoencephalography 13

1.5.1.4 Flexible Hybrid Electronics (FHE) 13

1.5.1.5 Robotic-Assisted Surgeries 13

1.5.1.6 Flexible Microstructural Pressure Sensors 14

1.5.1.7 Biomedical Applications 14

1.5.1.8 Cb-hmi 15

1.5.1.9 HMI in Medical Devices 15

1.5.2 Industrial Applications 15

1.5.2.1 Metal Industries 16

1.5.2.2 Video Game Industry 16

1.5.2.3 Aerospace and Defense 16

1.5.2.4 Water Purification Plant HMI Based on Multi-Agent Systems (MAS) 17

1.5.2.5 Virtual and Haptic Interfaces 17

1.5.2.6 Space Crafts 17

1.5.2.7 Car Wash System 18

1.5.2.8 Pharmaceutical Processing and Industries 18

1.6 Challenges 18

1.7 Conclusion and Future Prospects 19

References 20

2 Improving Healthcare Practice by Using HMI Interface 25
Vaibhav Verma, Vivek Dave and Pranay Wal

2.1 Background of Human-Machine Interaction 26

2.2 Introduction 26

2.2.1 Healthcare Practice 26

2.2.2 Human-Machine Interface System in Healthcare 26

2.3 Evolution of HMI Design 27

2.3.1 HMI Design 1.0 27

2.3.2 HMI Design 2.0 28

2.3.3 HMI Design 3.0 28

2.3.4 HMI Design 4.0 28

2.4 Anatomy of Human Brain 28

2.5 Signal Associated With Brain 31

2.5.1 Evoked Signals 31

2.5.2 Spontaneous Signals 32

2.5.3 Hybrid Signals 32

2.6 HMI Signal Processing and Acquisition Methods 32

2.7 Human-Machine Interface–Based Healthcare System 36

2.7.1 Healthcare Practice System 36

2.7.1.1 Healthcare Practice 36

2.7.1.2 Current State of Healthcare Provision 37

2.7.1.3 Concerns With Domestic Healthcare 38

2.7.2 Medical Education System 38

2.7.2.1 Traditional and Modern Way of Providing Medical Education 38

2.8 Working Model of HMI 38

2.9 Challenges and Limitations of HMI Design 40

2.10 Role of HMI in Healthcare Practice 40

2.10.1 Simple to Clean 41

2.10.2 High Chemical Tolerance 41

2.10.3 Transportable and Light 41

2.10.4 Enhancing Communication 41

2.11 Application of HMI Technology in Medical Fields 42

2.11.1 Medical and Rehabilitative Engineering Using HMI 42

2.11.2 Controls for Robotic Surgery and Human Prosthetics 45

2.11.3 Sensory Replacement Mechanism 47

2.11.4 Wheelchairs and Moving Robots Along With Neurological Interface 48

2.11.5 Cognitive Improvement 49

2.12 Conclusion and Future Perspective 51

References 52

3 Human-Machine Interface and Patient Safety 59
Arun Kumar Singh and Rishabha Malviya

3.1 Introduction 59

3.2 Detecting Anesthesia-Related Drug Administration Errors and Predicting Their Impact 60

3.2.1 Methodological Difficulties in Studying Rare, Dangerous Phenomena 61

3.2.2 Consequences of Errors 63

3.2.3 Lessons From Other Industries 65

3.2.4 The Double-Human Interface 66

3.2.5 The Culture of Denial and Effort 67

3.2.6 Poor Labeling 68

3.3 Systematic Approaches to Improve Patient Safety During Anesthesia 69

3.3.1 Design Principles 69

3.3.2 Evidence of Safety Gains 70

3.3.3 Consistent Color-Coding 71

3.3.4 The Codonics Label System 72

3.4 The Triumph of Software 73

3.4.1 Software in Hospitals 74

3.4.2 Software in Anesthesia 75

3.4.3 The Alarm Problem 76

3.5 Environments that Audit Themselves 77

3.6 New Risks and Dangers 77

3.7 Conclusion 78

References 79

4 Human-Machine Interface Improving Quality of Patient Care 89
Rishav Sharma and Rishabha Malviya

4.1 Introduction 90

4.2 An Advanced Framework for Human-Machine Interaction 92

4.2.1 A Simulated Workplace Safety and Health Program 92

4.3 Human–Computer Interaction (HCI) 93

4.4 Multimodal Processing 95

4.5 Integrated Multimodality at a Lower Order (Stimulus Orientation) 96

4.6 Higher-Order Multimodal Integration (Perceptual Binding) 96

4.7 Gains in Performance From Multisensory Stimulation 97

4.8 Amplitude Envelope and Alarm Design 98

4.9 Recent Trends in Alarm Tone Design for Medical Devices 99

4.10 Percussive Tone Integration in Multimodal User Interfaces 99

4.11 Software in Hospitals 100

4.12 Brain–Machine Interface (BCI) Outfit 101

4.13 BCI Sensors and Techniques 101

4.13.1 Eeg 102

4.13.2 ECoG 102

4.13.3 Ecg 102

4.13.4 Emg 103

4.13.5 Meg 103

4.13.6 Fmri 103

4.14 New Generation Advanced Human-Machine Interface 104

4.15 Conclusion 105

References 106

5 Smart Patient Engagement through Robotics 115
Rakhi Mohan, A. Arun Prakash, Uma Devi N., Anjali Sharma S., Aiswarya Babu N. and Thennarasi P.

5.1 Introduction 116

5.1.1 Robotics in Healthcare 116

5.1.2 Patient Engagement Tasks (Front End) 118

5.1.2.1 Robotics in Nursing, Patient Handling, and Support 118

5.1.2.2 Robotics in Patient Reception 119

5.1.2.3 Robotics in Ambulance Services 120

5.1.2.4 Robotics in Serving (Food and Medicine) 120

5.1.2.5 Robotics in Surgery and Surgical Assistance 121

5.1.2.6 Robotics in Cleaning, Moping, Spraying and Disinfecting 122

5.1.2.7 Robotics in Physiotherapy, Radiology, Lab Diagnostics and Rehabilitation (Exoskeletons) 122

5.1.2.8 Robotics in Tele-Presence 122

5.1.2.9 Robotics in Hospital Kitchen and Pantry Management 123

5.1.2.10 Robotics in Outdoor Medicine Delivery 123

5.1.2.11 Robotics in Home Healthcare 123

5.1.3 Documentation and Other Hospital Management Tasks (Back End) 124

5.1.3.1 Robotics in Patient Data Feeding and Storing 124

5.1.3.2 Robotics in Data Mining 124

5.1.3.3 Robotics in Job Allocation to Hospital Staffs 125

5.1.3.4 Robotics in Payroll Management 125

5.1.3.5 Robotics in Medicine and Medical Equipment Logistics 126

5.1.3.6 Robotics in Medical Waste Residual Management 126

5.2 Theoretical Framework 126

5.3 Objectives 127

5.4 Research Methodology 127

5.5 Primary and Secondary Data 127

5.6 Factors for Consideration 127

5.6.1 Patient Demographics 127

5.6.2 Hospital/Health Institutes Demographics 127

5.6.3 Patient Perception Factors 128

5.6.4 Hospital’s Feasibility Factors and Hospital’s Economic Factors for Implementation 128

5.7 Robotics Implementation 128

5.8 Tools for Analysis 129

5.9 Analysis of Patient’s Perception 129

5.10 Review of Literature 129

5.11 Hospitals Considered for the Study (Through Indirect Sources) 131

5.12 Analysis and Interpretation 133

5.12.1 Crosstabulation 133

5.12.2 Regression and Model Fit 137

5.12.3 Factor Analysis 140

5.12.4 Regression Analysis 147

5.12.5 Descriptive Statistics 149

5.13 Conclusion 153

References 153

Annexure 154

6 Accelerating Development of Medical Devices Using Human-Machine Interface 161
Dipanjan Karati, Swarupananda Mukherjee, Souvik Roy and Bhupendra G. Prajapati

6.1 Introduction 162

6.2 HMI Machineries 164

6.3 Brain–Computer Interface and HMI 165

6.4 HMI for a Mobile Medical Exoskeleton 166

6.5 Human Artificial Limb and Robotic Surgical Treatment by HMI 167

6.6 Cognitive Enhancement by HMI 170

6.7 Soft Electronics for the Skin Using HMI 171

6.8 Safety Considerations 173

6.9 Conclusion 174

References 174

7 The Role of a Human-Machine Interaction (HMI) System on the Medical Devices 183
Zahra Alidousti Shahraki and Mohsen Aghabozorgi Nafchi

7.1 Introduction 184

7.2 Machine Learning for HCI Systems 185

7.3 Patient Experience 187

7.4 Cognitive Science 190

7.5 HCI System Based on Image Processing 192

7.5.1 Patient’s Facial Expression 193

7.5.2 Gender and Age 194

7.5.3 Emotional Intelligence 199

7.6 Blockchain 201

7.7 Virtual Reality 203

7.8 The Challenges in Designing HCI Systems for Medical Devices 206

7.9 Conclusion 207

References 208

8 Human-Machine Interaction in Leveraging the Concept of Telemedicine 211
Dipa K. Israni and Nandita S. Chawla

8.1 Introduction 212

8.2 Innovative Development in HMI Technologies and Its Use in Telemedicine 213

8.2.1 Nanotechnology 214

8.2.2 The Internet of Things (IoT) 215

8.2.3 Internet of Medical Things (IoMT) 216

8.2.3.1 Motion Detection Sensors 217

8.2.3.2 Pressure Sensors 217

8.2.3.3 Temperature Sensors 217

8.2.3.4 Monitoring Cardiovascular Disease 217

8.2.3.5 Glucose Level Monitoring 217

8.2.3.6 Asthma Monitoring 217

8.2.3.7 GPS Smart Soles and Motion Detection Sensors 218

8.2.3.8 Wireless Fetal Monitoring 218

8.2.3.9 Smart Clothing 218

8.2.4 Ai 219

8.2.5 Machine Learning Techniques 220

8.2.6 Deep Learning 221

8.2.7 Home Monitoring Devices, Augmented and Virtual 222

8.2.8 Drone Technology 223

8.2.9 Robotics 223

8.2.9.1 Robotics in Healthcare 224

8.2.9.2 History of Robotics 224

8.2.9.3 Tele-Surgery/Remote Surgery 224

8.2.10 5G Technology 225

8.2.11 6g 225

8.2.12 Big Data 226

8.2.13 Cloud Computing 226

8.2.14 Blockchain 227

8.2.14.1 Clinical Trials 228

8.2.14.2 Patient Records 228

8.2.14.3 Drug Tracking 228

8.2.14.4 Device Tracking 229

8.3 Advantages of Utilizing HMI in Healthcare for Telemedicine 230

8.3.1 Emotive Telemedicine 230

8.3.2 Ambient Assisted Living 232

8.3.2.1 Wearable Sensors for AAL 232

8.3.3 Monitoring and Controlling Intelligent Self-Management and Wellbeing 233

8.3.4 Intelligent Reminders for Treatment, Compliance, and Adherence 233

8.3.5 Personalized and Connected Healthcare 233

8.4 Obstacles to the Utilize, Accept, and Implement HMI in Telemedicine 234

8.4.1 Data Inconsistency and Disintegration 234

8.4.2 Standards and Interoperability are Lacking 234

8.4.3 Intermittent or Non-Existent Network Connectivity 234

8.4.4 Sensor Data Unreliability and Invalidity 235

8.4.5 Privacy, Confidentiality, and Data Consistency 235

8.4.6 Scalability Issues 235

8.4.7 Health Consequences 235

8.4.8 Clinical Challenges 236

8.4.9 Nanosensors and Biosensors Offer Health Risks 236

8.4.10 Limited Computing Capability and Inefficient Energy Use 236

8.4.11 Memory Space is Limited 237

8.4.12 Models of Digital Technology are Rigid and Sophisticated 237

8.4.13 Regulatory Frameworks 237

8.4.14 Incorporated IT Infrastructure 237

8.4.15 Misalignment with Nations’ e-Health Policies 238

8.4.16 Implementing Costs 238

8.4.17 Operational and Systems Challenges 238

8.4.18 Logistical Challenges 239

8.4.19 Communication Barriers 239

8.4.20 Unique Challenges 239

8.5 Conclusions 239

References 240

9 Making Hospital Environment Friendly for People: A Concept of HMI 247
Rihana Begum P., Badrud Duza Mohammad, Saravana Kumar A. and Muhasina K.M.

9.1 Introduction 248

9.2 A Scenario for Ubiquitous Computing and Ambient Intelligence 249

9.3 Emergence of Ambient Intelligence 250

9.4 Framework for Advanced Human-Machine Interfaces 251

9.5 Brain Computer Interface (BCI) 252

9.5.1 The BCI System: An Introduction 252

9.5.2 The Characteristics of a BCI 253

9.5.2.1 Dependent and Independent BCIs 253

9.5.2.2 Motor Disabilities: Options for Restoring Function 253

9.5.3 Components of BCI 254

9.5.4 Structure of the Human Brain and Its Signals 254

9.5.4.1 A Signal That is Evoked 256

9.5.4.2 Spontaneous Signals 256

9.5.4.3 Hybrid Signals 257

9.6 Development in MHI Technologies and Their Applications 257

9.7 Techniques of Signal Acquisition and Processing Applied to HMI 258

9.8 Hospital-Friendly Environment for Patients 260

9.8.1 Physiological Study State 260

9.8.1.1 Nature 260

9.8.1.2 Music 260

9.8.2 Pain State 260

9.8.2.1 Nature 260

9.8.2.2 Natural Light 261

9.8.3 Sleep 261

9.8.3.1 Nature Images 261

9.8.4 Patient Experience 261

9.8.4.1 Patient’s Satisfaction 261

9.8.4.2 Interaction 262

9.9 Applications of HMI for Patient-Friendly Hospital Environment 263

9.9.1 Healthcare and Engineering 263

9.9.2 Controls for Robotic Surgery and Human Prosthetics 265

9.9.3 Sensory Substitution System 266

9.9.4 Mobile Robots and Wheelchairs With Neural Interfaces 267

9.9.5 Technology on Biometric System 268

9.9.6 Enhancement of Cognition Level 269

9.9.7 fNIRS-EEG Multimodal BCI as a Future Perspective 270

9.10 Conclusion 270

References 271

Part II : Emerging Application and Regulatory Prospects of HMI in Healthcare 279

10 HMI: Disruption in the Neural Healthcare Industry 281
Preetam L. Nikam, Amol U. Gayke, Pavan S. Avhad, Rahul B. Bhabad and Rishabha Malviya

10.1 Introduction 282

10.2 Stimulation of Muscles 283

10.3 Cochlear Implants 283

10.3.1 Implants for Cochlear 283

10.3.2 Prosthetics for Ears 284

10.4 Peripheral Nervous System Interaction 284

10.5 Sleeve Electrodes 285

10.6 Flat-Interfaced Nerve Electrodes 287

10.7 Transverse and Longitudinal Intrafascicular Electrode (LIFE and TIME) 287

10.8 Multi-Channel Arrays That Penetrate 288

10.8.1 Numerous-Channel Arrays That Penetrate 288

10.9 Spinal Cord Stimulation and Central Nervous System Interaction 289

10.9.1 Cortical Connections 289

10.9.2 Stimulation of the Auditory Nucleus and Ganglions 290

10.9.3 Stimulation of the Deep Brain 290

10.10 Computer–Brain Interfaces 290

10.11 Conclusion 291

References 291

11 Dynamics of EHR in M-Healthcare Application 295
Eva Kaushik and Rohit Kaushik

11.1 Introduction 296

11.1.1 Why EHR is Needed in the Nation? 296

11.1.2 Empowering Patients in Healthcare Management 297

11.1.3 Data Management in EHR 298

11.1.4 Long-Term Architectural Approach 298

11.2 Background Related Work 299

11.3 Methodology 300

11.3.1 Use-Cases on Ground Base Reality 300

11.3.2 Integration of Technology to Solve Healthcare Issues 301

11.3.3 Workflow 302

11.4 Tools and Technologies 303

11.5 Limitations 304

11.6 Future Scope 305

11.6.1 Personalized EHR Cards 305

11.7 Discussion 306

11.7.1 Electronic Health Records and Personal Health Records 306

11.7.2 Physicians’ Review Toward EHR 307

11.7.3 Interoperability 307

11.8 Conclusion 308

References 308

12 Role of Human-Machine Interface in the Biomedical Device Development to Handle COVID-19 Pandemic Situation in an Efficient Way 311
Soma Datta and Nabendu Chaki

12.1 Introduction: Background and Driving Forces 312

12.1.1 Observed Scenario During May 2021 314

12.1.1.1 Transmission Medium 314

12.1.2 Limitation of Vaccine Technology 314

12.1.3 Adverse Effect of Protective Measure 314

12.1.4 Revoking of Restrictions Causes Surges in Pandemic 315

12.2 Methods 315

12.2.1 Determine Major Influencing Factors 316

12.2.2 Analyzed the Selected Influencing Factor 317

12.2.2.1 Evidence 1 318

12.2.2.2 Evidence 2 318

12.2.2.3 Evidence 3 320

12.2.3 Managing Mechanism to Reduce the Spreading Rate of COVID- 19 320

12.2.4 The Households Health Safety Systems to Disinfect Outdoor Cloths 321

12.2.4.1 Present Households Disinfect Systems for Cloth and Personal Belonging 321

12.2.4.2 The Outline of Households Health Safety Systems to Disinfect Outdoor Clothes 322

12.2.5 Upgradation of Individual Room Air Conditioning System 324

12.2.5.1 The Outline of the AI-Based Room Ventilator System 324

12.2.6 Design of Next-Generation Mask 324

12.3 Results 325

12.4 Conclusion 325

Acknowledgment 325

References 326

13 Role of HMI in the Drug Manufacturing Process 329
Biswajit Basu, Kevinkumar Garala and Bhupendra G. Prajapati

13.1 Introduction 330

13.1.1 Dialogue Systems 331

13.2 Types of HMI 333

13.3 Advantages and Disadvantages of HMI 334

13.4 Roles of HMI in the Pharmaceutical Manufacturing Process 339

13.5 Common Applications for Human-Machine Interfaces 343

13.5.1 Automotive Dashboards 343

13.5.2 Monitoring of Machinery and Equipment 344

13.5.3 Digital Displays 344

13.5.4 Building Automation 344

13.5.5 Video and Audio Production 344

13.6 Healthcare System-Based Human–Computer Interaction 345

13.6.1 Healthcare System 345

13.6.2 Teaching of Medicine and Physiology 346

13.7 Performance Test of Healthcare System Based on HCI 349

13.7.1 HCI-Based Medical Teaching System 349

13.8 Human-Machine Interface for Healthcare and Rehabilitation 349

13.8.1 Ambient Intelligence and Ubiquitous Computing Scenario 349

13.8.2 The Advanced Human-Machine Interface Framework 350

13.9 Human-Machine Interface for Research Reactor: Instrumentation and Control System 351

13.10 Future Scope of Human-Machine Interface (HMI) 352

13.11 Conclusion 353

References 353

14 Breaking the Silence: Brain–Computer Interface for Communication 357
Preetam L. Nikam, Sheetal Wagh, Sarika Shinde, Abhishek Mokal, Smita Andhale, Prathmesh Wagh, Vivek Bhosale and Rishabha Malviya

14.1 Introduction 358

14.2 Survey of BCI 359

14.3 Techniques of BCI 361

14.3.1 Potentials Associated With an Event 361

14.3.2 Cortical Gradual Potentials 361

14.3.3 Evoked Visual Possibilities 361

14.3.4 Sensorimotor Rhythms 362

14.3.5 Motor Imagery 362

14.4 BCI Components 362

14.4.1 Signal Acquisition 363

14.4.2 Signal Processing 363

14.4.3 Extraction of Features 363

14.4.4 Signal Categorization 363

14.5 BCI Signal Acquisition Methods 364

14.6 BCI Invasion 364

14.7 BCI With Limited Invasion 364

14.8 BCI Not Invasive 364

14.9 BCI Applications 365

14.9.1 Movement 365

14.9.2 Recreation 365

14.9.3 Reconstruction 366

14.9.4 Interaction 366

14.9.5 Interaction With Others 366

14.9.6 Diagnosis and Treatment of Depression 366

14.9.7 Reduces Healthcare Costs 367

14.10 BCI Healthcare Challenges 367

14.10.1 Ethical Difficulties 367

14.10.2 Goodwill 367

14.10.3 Legality 368

14.10.4 Freedom of Privacy 368

14.10.5 Issues With Standardization 368

14.10.6 Problems With Reliability 368

14.10.7 Prolonged Training Process 369

14.10.8 Expensive Acquisition and Control 369

14.11 Conclusion 370

References 370

15 Regulatory Perspective: Human-Machine Interfaces 375
Artiben Patel, Ravi Patel, Rakesh Patel, Bhupendra Prajapati and Shivani Jani

Abbreviations 376

15.1 Introduction 376

15.2 Why are Regulations Needed? 377

15.2.1 Safety 378

15.2.2 Uniform Requirements 378

15.2.3 Promote Innovation 378

15.2.4 Free Movement of Goods 378

15.2.5 Compensation 379

15.2.6 Fostering Innovation 379

15.3 US Regulatory Perspective 379

15.3.1 History of Medical Device Regulation and Its Supervision in the United States 380

15.3.2 Classification of Medical Devices 384

15.3.3 Reclassification 385

15.3.4 How to Determine if the Product is a Medical Device or How to Classify the Medical Device 385

15.3.5 Device Development Process 387

15.3.6 Overview of Device Regulations 391

15.3.7 Quality and Compliance of Medical Devices 393

15.3.8 Human Factors and Medical Devices 395

15.3.9 Continuous Improvement of Regulations 402

15.4 Conclusion 407

References 407

16 Towards the Digitization of Healthcare Record Management 411
Shivani Patel, Bhavinkumar Gayakvad, Ravisinh Solanki, Ravi Patel and Dignesh Khunt

16.1 Introduction 412

16.2 Digital Health Records: Concept and Organization 416

16.3 Mechanism and Operation of Digital Health Record 419

16.3.1 Physician-Hosted EHR 420

16.3.2 Remotely-Hosted EHR 420

16.3.2.1 Subsidized System 420

16.3.2.2 Dedicated Hosted System 421

16.3.2.3 Cloud-Based or Internet-Based Computing 421

16.4 Benefits of Digital Health Records 426

16.4.1 Security 426

16.4.2 Costs 427

16.4.3 Access 427

16.4.4 Storage 427

16.4.5 Accuracy and Readability 427

16.4.6 Practice Management 428

16.4.7 Quality of Care 428

16.5 Limitations of Digital Health Records 428

16.5.1 Completeness 428

16.5.2 Correctness 429

16.5.3 Complexity 429

16.5.4 Acceptability 430

16.5.4.1 People 430

16.5.4.2 Hardware, Software and Network 430

16.5.4.3 Procedure 430

16.6 Risk & Problems Associated With the System 431

16.6.1 Lack of Concord 431

16.6.2 Privacy and Data Security Issues 431

16.6.3 Problems in Patient Matching 432

16.6.4 Alteration of Algorithms in Decision-Support Models 432

16.6.5 Increased Workload of Clinicians 432

16.7 Future Benefits 432

16.8 Miscellaneous 434

16.8.1 Policies Regarding Data Exchange 434

16.8.1.1 Directed Exchange 435

16.8.1.2 Query-Based Exchange 435

16.8.1.3 Consumer-Mediated Exchange 435

16.8.2 Current Practices of Digital Health Records 438

16.8.2.1 India 438

16.8.2.2 Australia 439

16.8.2.3 Canada 439

16.8.2.4 USA 440

16.8.2.5 China 440

16.8.3 Data Analysis 442

16.8.4 Role and Benefits to the Stakeholders 443

16.8.4.1 Advantages to the Patient 443

16.8.4.2 Advantages to the Healthcare Providers 444

16.8.4.3 Advantages to the Society 444

16.9 Conclusion 445

References 446

17 Intelligent Healthcare Supply Chain 449
Chirag Kalaria, Shambhavi Singh and Bhupendra G. Prajapati

17.1 Introduction 450

17.2 Supply Chain – Method Networking? 451

17.3 Healthcare Supply Chain and Steps Involved 451

17.4 Importance of HSC 452

17.5 Risks and Complexities Affecting the Globally Distributed HSC 453

17.5.1 Legacy HSC 453

17.5.1.1 SWOT Analysis of Legacy HSC 454

17.5.2 What is an Intelligent Supply Chain? 454

17.5.3 Difference Between Legacy HSC and Intelligent HSC 456

17.6 Technologies Come to Aid to Build an Intelligent HSC 457

17.6.1 Hmi 457

17.6.2 Ai 458

17.6.3 Ml/dl 459

17.7 Blockchain 460

17.8 Robotics 461

17.9 Cloud Computing 463

17.10 Big Data Analytics (BDA) 465

17.11 Industry 4.0 465

17.12 Internet of Things (IoT) 467

17.13 Digital Twins 469

17.14 Supply Chain Control Tower 470

17.15 Predictive Maintenance 472

17.16 A Digital Transformation Roadmap 473

17.17 Prerequisite for Designing Intelligent HSC 475

17.18 HMI—Usage in HSC Management 476

17.19 HMI—A Face of the Supply Chain Control Tower 477

17.20 The Intelligent Future of the Healthcare Industry 478

17.21 Conclusion 480

References 481

Index 483