Human-Machine Interface
Sybex Inc.,U.S. (Verlag)
978-1-394-19991-4 (ISBN)
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.
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.
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
| Erscheinungsdatum | 31.10.2023 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Gewicht | 1025 g |
| Einbandart | gebunden |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Medizin / Pharmazie ► Gesundheitswesen | |
| Technik ► Medizintechnik | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 1-394-19991-0 / 1394199910 |
| ISBN-13 | 978-1-394-19991-4 / 9781394199914 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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