AI in Healthcare
-15%
portes grátis
AI in Healthcare
How Artificial Intelligence Is Changing IT Operations and Infrastructure Services
Shimonski, Robert
John Wiley & Sons Inc
04/2021
288
Mole
Inglês
9781119680017
15 a 20 dias
378
Descrição não disponível.
Introduction xvii
Chapter 1 Healthcare IT and the Growing Need for AI Operations 1
A Brief History of AI and Healthcare 3
Healthcare IT Expansion and Growth 4
Data Overload 5
Digital Transformation of Healthcare 7
The Science of Healthcare Innovation 9
Artificial Intelligence in Healthcare 10
Healthcare IT Operations 14
AIOps Platform Strategy 18
Platform Types 19
Customer Experience and AIOps 20
AIOps Considerations and Goals 22
Summary 23
Chapter 2 AI Healthcare Operations (Clinical) 25
Clinical Impact of AIOps 26
Gaining a Competitive Edge with Intelligent Cloud, Data Analytics, and AI 27
Design and Innovation 29
AIOps for Healthcare Delivery 33
AIOps for Service Performance 38
Clinical AI, AIOps, and Future Platform Convergence 39
Security and Privacy 41
Why Security is Paramount in AIOps 41
HIPAA, PHI, and PII Protection 43
Summary 45
Chapter 3 AI Healthcare Operations (Operational Infrastructure) 47
Getting Started with AIOps 48
Strategy of AIOps Deployments 50
Creating a Scope 51
AIOps Platforms, Products, and Services Selection 54
AIOps Product Selection: General Topics 54
Product Review: AIOps Tool Splunk 57
Product Review: AIOps Tool ServiceNow 60
Product Review: AIOps Tool Dynatrace 64
Workflow and Event Management Design 67
Service Design with AIOps 67
Day-to-Day Operational Management 69
Summary 70
Chapter 4 Project Planning for AIOps 73
Project Planning Requirements 74
Assigning a Project Manager 75
Creating a Project Plan 77
Building the Project Plan 78
Planning a Healthcare System Project 83
Deploying AIOps 85
Deploying AIOps into the Environment 86
Configuring AIOps in the Environment 88
Summary 91
Chapter 5 Using AI for Metrics, Performance, and Reporting 93
System Performance Metrics 94
Information Technology Metrics 94
Using AI for Metrics, Performance, and Reporting 98
Strategy and Goals for AI Deployment 101
Benefits of Healthcare AIOps Service Performance Reporting 102
Developing Usable AIOps Metrics 104
Helpful Tools You Can Use 105
Gathering Usable Metrics 107
Using Dynatrace 108
Using Splunk 110
Using ServiceNow 117
Clinical and IT Metrics and Collective Actions 123
Usable Healthcare AIOps Dashboards 127
Summary 128
Chapter 6 AIOps and Automation in Healthcare Operations 131
Automation, Workflow, Process, and Intelligence Design 132
Designing the Framework for Automation 132
Understanding Automation 133
Improved User Experience 134
Designing Workflow and Process Engineering 135
Quality Control and Assurance 138
Foundational and Required Design Items 139
Configuring and Using AIOps Automation 146
Monitoring and Operating Event Management Services 148
Creating and Realizing Automation, ML, and AI 152
Automating Splunk and IT Service Analyzer 155
Splunk IT Service Intelligence 160
When Should You Use AI and ML? 162
Summary 163
Chapter 7 Cloud Operations and AIOps 165
Understanding the Cloud 166
Understanding Cloud Computing 166
Cloud as a Service 172
Hybrid Cloud Solutions 175
When You Should (and Shouldn't) Consider the Cloud 178
Deploying to the Cloud 179
Conducting a Request for Proposal 182
Additional Deployment Options 184
Managing in the Cloud 186
Cloud Management and Monitoring Solutions 189
Summary 191
Chapter 8 The Future of Healthcare AI 193
The Dynamically Changing World of AI 194
The Future of AI 198
Artificial Intelligence and Healthcare Innovation 201
Big Data, DataOps, Analytics, and Informatics 201
Telehealth (Telemedicine) 204
Telehealth Innovations 206
Telehealth AI 209
Future Innovation Merging Clinical and IT Operations 212
The Future and Beyond 214
AIOps, the Cloud, and Security 218
Summary 218
Chapter 9 The Convergence of Healthcare AI Technology 221
Overview of Convergence 222
Systems Integration 225
Convergence of AI, HIT, and HIE 228
IoT and AI 230
IoT Management 237
AIOps Management and Security 239
Summary 245
Appendix Sample AIOps Use Cases and Examples 247
Index 259
Chapter 1 Healthcare IT and the Growing Need for AI Operations 1
A Brief History of AI and Healthcare 3
Healthcare IT Expansion and Growth 4
Data Overload 5
Digital Transformation of Healthcare 7
The Science of Healthcare Innovation 9
Artificial Intelligence in Healthcare 10
Healthcare IT Operations 14
AIOps Platform Strategy 18
Platform Types 19
Customer Experience and AIOps 20
AIOps Considerations and Goals 22
Summary 23
Chapter 2 AI Healthcare Operations (Clinical) 25
Clinical Impact of AIOps 26
Gaining a Competitive Edge with Intelligent Cloud, Data Analytics, and AI 27
Design and Innovation 29
AIOps for Healthcare Delivery 33
AIOps for Service Performance 38
Clinical AI, AIOps, and Future Platform Convergence 39
Security and Privacy 41
Why Security is Paramount in AIOps 41
HIPAA, PHI, and PII Protection 43
Summary 45
Chapter 3 AI Healthcare Operations (Operational Infrastructure) 47
Getting Started with AIOps 48
Strategy of AIOps Deployments 50
Creating a Scope 51
AIOps Platforms, Products, and Services Selection 54
AIOps Product Selection: General Topics 54
Product Review: AIOps Tool Splunk 57
Product Review: AIOps Tool ServiceNow 60
Product Review: AIOps Tool Dynatrace 64
Workflow and Event Management Design 67
Service Design with AIOps 67
Day-to-Day Operational Management 69
Summary 70
Chapter 4 Project Planning for AIOps 73
Project Planning Requirements 74
Assigning a Project Manager 75
Creating a Project Plan 77
Building the Project Plan 78
Planning a Healthcare System Project 83
Deploying AIOps 85
Deploying AIOps into the Environment 86
Configuring AIOps in the Environment 88
Summary 91
Chapter 5 Using AI for Metrics, Performance, and Reporting 93
System Performance Metrics 94
Information Technology Metrics 94
Using AI for Metrics, Performance, and Reporting 98
Strategy and Goals for AI Deployment 101
Benefits of Healthcare AIOps Service Performance Reporting 102
Developing Usable AIOps Metrics 104
Helpful Tools You Can Use 105
Gathering Usable Metrics 107
Using Dynatrace 108
Using Splunk 110
Using ServiceNow 117
Clinical and IT Metrics and Collective Actions 123
Usable Healthcare AIOps Dashboards 127
Summary 128
Chapter 6 AIOps and Automation in Healthcare Operations 131
Automation, Workflow, Process, and Intelligence Design 132
Designing the Framework for Automation 132
Understanding Automation 133
Improved User Experience 134
Designing Workflow and Process Engineering 135
Quality Control and Assurance 138
Foundational and Required Design Items 139
Configuring and Using AIOps Automation 146
Monitoring and Operating Event Management Services 148
Creating and Realizing Automation, ML, and AI 152
Automating Splunk and IT Service Analyzer 155
Splunk IT Service Intelligence 160
When Should You Use AI and ML? 162
Summary 163
Chapter 7 Cloud Operations and AIOps 165
Understanding the Cloud 166
Understanding Cloud Computing 166
Cloud as a Service 172
Hybrid Cloud Solutions 175
When You Should (and Shouldn't) Consider the Cloud 178
Deploying to the Cloud 179
Conducting a Request for Proposal 182
Additional Deployment Options 184
Managing in the Cloud 186
Cloud Management and Monitoring Solutions 189
Summary 191
Chapter 8 The Future of Healthcare AI 193
The Dynamically Changing World of AI 194
The Future of AI 198
Artificial Intelligence and Healthcare Innovation 201
Big Data, DataOps, Analytics, and Informatics 201
Telehealth (Telemedicine) 204
Telehealth Innovations 206
Telehealth AI 209
Future Innovation Merging Clinical and IT Operations 212
The Future and Beyond 214
AIOps, the Cloud, and Security 218
Summary 218
Chapter 9 The Convergence of Healthcare AI Technology 221
Overview of Convergence 222
Systems Integration 225
Convergence of AI, HIT, and HIE 228
IoT and AI 230
IoT Management 237
AIOps Management and Security 239
Summary 245
Appendix Sample AIOps Use Cases and Examples 247
Index 259
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Healthcare IT; healthcare artificial intelligence; healthcare AI; healthcare information technology; AI; IT; artificial intelligence operations; AI operations; aiops; healthcare automation; ai clinical operations; healthcare ai planning
Introduction xvii
Chapter 1 Healthcare IT and the Growing Need for AI Operations 1
A Brief History of AI and Healthcare 3
Healthcare IT Expansion and Growth 4
Data Overload 5
Digital Transformation of Healthcare 7
The Science of Healthcare Innovation 9
Artificial Intelligence in Healthcare 10
Healthcare IT Operations 14
AIOps Platform Strategy 18
Platform Types 19
Customer Experience and AIOps 20
AIOps Considerations and Goals 22
Summary 23
Chapter 2 AI Healthcare Operations (Clinical) 25
Clinical Impact of AIOps 26
Gaining a Competitive Edge with Intelligent Cloud, Data Analytics, and AI 27
Design and Innovation 29
AIOps for Healthcare Delivery 33
AIOps for Service Performance 38
Clinical AI, AIOps, and Future Platform Convergence 39
Security and Privacy 41
Why Security is Paramount in AIOps 41
HIPAA, PHI, and PII Protection 43
Summary 45
Chapter 3 AI Healthcare Operations (Operational Infrastructure) 47
Getting Started with AIOps 48
Strategy of AIOps Deployments 50
Creating a Scope 51
AIOps Platforms, Products, and Services Selection 54
AIOps Product Selection: General Topics 54
Product Review: AIOps Tool Splunk 57
Product Review: AIOps Tool ServiceNow 60
Product Review: AIOps Tool Dynatrace 64
Workflow and Event Management Design 67
Service Design with AIOps 67
Day-to-Day Operational Management 69
Summary 70
Chapter 4 Project Planning for AIOps 73
Project Planning Requirements 74
Assigning a Project Manager 75
Creating a Project Plan 77
Building the Project Plan 78
Planning a Healthcare System Project 83
Deploying AIOps 85
Deploying AIOps into the Environment 86
Configuring AIOps in the Environment 88
Summary 91
Chapter 5 Using AI for Metrics, Performance, and Reporting 93
System Performance Metrics 94
Information Technology Metrics 94
Using AI for Metrics, Performance, and Reporting 98
Strategy and Goals for AI Deployment 101
Benefits of Healthcare AIOps Service Performance Reporting 102
Developing Usable AIOps Metrics 104
Helpful Tools You Can Use 105
Gathering Usable Metrics 107
Using Dynatrace 108
Using Splunk 110
Using ServiceNow 117
Clinical and IT Metrics and Collective Actions 123
Usable Healthcare AIOps Dashboards 127
Summary 128
Chapter 6 AIOps and Automation in Healthcare Operations 131
Automation, Workflow, Process, and Intelligence Design 132
Designing the Framework for Automation 132
Understanding Automation 133
Improved User Experience 134
Designing Workflow and Process Engineering 135
Quality Control and Assurance 138
Foundational and Required Design Items 139
Configuring and Using AIOps Automation 146
Monitoring and Operating Event Management Services 148
Creating and Realizing Automation, ML, and AI 152
Automating Splunk and IT Service Analyzer 155
Splunk IT Service Intelligence 160
When Should You Use AI and ML? 162
Summary 163
Chapter 7 Cloud Operations and AIOps 165
Understanding the Cloud 166
Understanding Cloud Computing 166
Cloud as a Service 172
Hybrid Cloud Solutions 175
When You Should (and Shouldn't) Consider the Cloud 178
Deploying to the Cloud 179
Conducting a Request for Proposal 182
Additional Deployment Options 184
Managing in the Cloud 186
Cloud Management and Monitoring Solutions 189
Summary 191
Chapter 8 The Future of Healthcare AI 193
The Dynamically Changing World of AI 194
The Future of AI 198
Artificial Intelligence and Healthcare Innovation 201
Big Data, DataOps, Analytics, and Informatics 201
Telehealth (Telemedicine) 204
Telehealth Innovations 206
Telehealth AI 209
Future Innovation Merging Clinical and IT Operations 212
The Future and Beyond 214
AIOps, the Cloud, and Security 218
Summary 218
Chapter 9 The Convergence of Healthcare AI Technology 221
Overview of Convergence 222
Systems Integration 225
Convergence of AI, HIT, and HIE 228
IoT and AI 230
IoT Management 237
AIOps Management and Security 239
Summary 245
Appendix Sample AIOps Use Cases and Examples 247
Index 259
Chapter 1 Healthcare IT and the Growing Need for AI Operations 1
A Brief History of AI and Healthcare 3
Healthcare IT Expansion and Growth 4
Data Overload 5
Digital Transformation of Healthcare 7
The Science of Healthcare Innovation 9
Artificial Intelligence in Healthcare 10
Healthcare IT Operations 14
AIOps Platform Strategy 18
Platform Types 19
Customer Experience and AIOps 20
AIOps Considerations and Goals 22
Summary 23
Chapter 2 AI Healthcare Operations (Clinical) 25
Clinical Impact of AIOps 26
Gaining a Competitive Edge with Intelligent Cloud, Data Analytics, and AI 27
Design and Innovation 29
AIOps for Healthcare Delivery 33
AIOps for Service Performance 38
Clinical AI, AIOps, and Future Platform Convergence 39
Security and Privacy 41
Why Security is Paramount in AIOps 41
HIPAA, PHI, and PII Protection 43
Summary 45
Chapter 3 AI Healthcare Operations (Operational Infrastructure) 47
Getting Started with AIOps 48
Strategy of AIOps Deployments 50
Creating a Scope 51
AIOps Platforms, Products, and Services Selection 54
AIOps Product Selection: General Topics 54
Product Review: AIOps Tool Splunk 57
Product Review: AIOps Tool ServiceNow 60
Product Review: AIOps Tool Dynatrace 64
Workflow and Event Management Design 67
Service Design with AIOps 67
Day-to-Day Operational Management 69
Summary 70
Chapter 4 Project Planning for AIOps 73
Project Planning Requirements 74
Assigning a Project Manager 75
Creating a Project Plan 77
Building the Project Plan 78
Planning a Healthcare System Project 83
Deploying AIOps 85
Deploying AIOps into the Environment 86
Configuring AIOps in the Environment 88
Summary 91
Chapter 5 Using AI for Metrics, Performance, and Reporting 93
System Performance Metrics 94
Information Technology Metrics 94
Using AI for Metrics, Performance, and Reporting 98
Strategy and Goals for AI Deployment 101
Benefits of Healthcare AIOps Service Performance Reporting 102
Developing Usable AIOps Metrics 104
Helpful Tools You Can Use 105
Gathering Usable Metrics 107
Using Dynatrace 108
Using Splunk 110
Using ServiceNow 117
Clinical and IT Metrics and Collective Actions 123
Usable Healthcare AIOps Dashboards 127
Summary 128
Chapter 6 AIOps and Automation in Healthcare Operations 131
Automation, Workflow, Process, and Intelligence Design 132
Designing the Framework for Automation 132
Understanding Automation 133
Improved User Experience 134
Designing Workflow and Process Engineering 135
Quality Control and Assurance 138
Foundational and Required Design Items 139
Configuring and Using AIOps Automation 146
Monitoring and Operating Event Management Services 148
Creating and Realizing Automation, ML, and AI 152
Automating Splunk and IT Service Analyzer 155
Splunk IT Service Intelligence 160
When Should You Use AI and ML? 162
Summary 163
Chapter 7 Cloud Operations and AIOps 165
Understanding the Cloud 166
Understanding Cloud Computing 166
Cloud as a Service 172
Hybrid Cloud Solutions 175
When You Should (and Shouldn't) Consider the Cloud 178
Deploying to the Cloud 179
Conducting a Request for Proposal 182
Additional Deployment Options 184
Managing in the Cloud 186
Cloud Management and Monitoring Solutions 189
Summary 191
Chapter 8 The Future of Healthcare AI 193
The Dynamically Changing World of AI 194
The Future of AI 198
Artificial Intelligence and Healthcare Innovation 201
Big Data, DataOps, Analytics, and Informatics 201
Telehealth (Telemedicine) 204
Telehealth Innovations 206
Telehealth AI 209
Future Innovation Merging Clinical and IT Operations 212
The Future and Beyond 214
AIOps, the Cloud, and Security 218
Summary 218
Chapter 9 The Convergence of Healthcare AI Technology 221
Overview of Convergence 222
Systems Integration 225
Convergence of AI, HIT, and HIE 228
IoT and AI 230
IoT Management 237
AIOps Management and Security 239
Summary 245
Appendix Sample AIOps Use Cases and Examples 247
Index 259
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