DUBLIN, Jan. 29, 2021 /PRNewswire/ -- The "Artificial Intelligence in Epidemiology Market by AI Type, Infrastructure, Deployment Model, and Services 2021 - 2026" report has been added to ResearchAndMarkets.com's offering.
This global AI epidemiology and public health market report provides a comprehensive evaluation of the positive impact that AI technology will produce with respect to healthcare informatics, and public healthcare management, and epidemiology analysis and response. The report assesses the macro factors affecting the market and the resulting need for hardware and software technology used in the public healthcare and epidemiology informatics.
The macro factors include the growth drivers and challenges of the market along with the potential application and usage areas in public health industry verticals. The report also provides the anticipated market value of AI in the public health and epidemiology informatics market globally and regionally. This includes core technology and AI-specific technologies. Market forecasts cover the period of 2021 - 2026.
The Center for Disease Control and Prevention sees epidemiology as the study and analysis of the distribution, patterns and determinants of health and disease conditions in defined populations. It is a cornerstone of public health and shapes policy decisions and evidence-based practice by identifying risk factors for disease and targets for preventive healthcare.
This includes identification of the factors involved with diseases transmitted by food and water, acquired during travel or recreational activities, bloodborne and sexually transmitted diseases, and nosocomial infections such as hospital-acquired illnesses. Epidemiology is also concerned with the identification of trends and predictive capabilities to prevent diseases.
Sources of disease data include medical claims data (commercial claims, Medicare), electronic healthcare records (EHR) including medical treatment facilities and pharmacies, death registries and socioeconomic data. It is important to note that some data is highly structured whereas other data elements are highly unstructured, such as data gathered from social media and Web scraping.
Artificial Intelligence (AI) will increasingly be relied upon to improve the efficiency and effectiveness of transforming data correlation to meaningful insights and information. For example, machine learning has been used to gather Web search and location data as a means of identifying potential unsafe areas, such as restaurants involved in food-borne illnesses.
The combination of data aggregation from multiple sources with machine learning and advanced analytics will greatly improve the efficacy of epidemiology predictive models. For example, machine learning allows epidemiologists to evaluate as many variables as desired without increasing statistical error, a problem that often arises with multiple testing bias, which is a condition that occurs when each additional test run on the data increases the possibility for error against a hypothetical target result.
Another example of AI in epidemiology is the use of natural language processing to capture clinical notes for preservation in EHR databases. As part of data capture and identification of most important information, AI will also be used to validate key terms to identify conditions, diagnoses and exposures that are otherwise difficult to capture/identify through traditional data source mining. This will be used for data discovery and validation as well as knowledge representation.
An extremely important and high growth area for AI in epidemiology is drug discovery, safety, and risk analysis, which we anticipate will be a $699 million global market by 2026. Other high opportunity areas for AI are disease and syndromic surveillance, infection prediction and forecasting, monitoring population and incidence of disease, and use of AI in Immunization Information Systems (IIS). In addition to mapping vaccinations to disease incidence, the IIS will leverage AI to identify the impact of public sentiment analysis and for public safety services such as mass notification.
Select Report Findings:
Report Benefits:
Key Topics Covered:
1.0 Executive Summary
2.0 Introduction
2.1 Defining Public Health Informatics
2.1.1 Epidemiology in PHI
2.1.1.1 Viral Disease Epidemiology
2.1.2 AI in Epidemiology and Public Health Informatics
2.1.3 Medical Informatics vs. Health Informatics
2.2 Social Technical Informatics Technology Stack
2.3 Epidemiology and Public Health Informatics Process
2.3.1 Collection of Data
2.3.2 Defining Study Model
2.3.3 Data Storage
2.3.4 Data Quality Assurance
2.3.5 Data Analysis
2.4 Computational Epidemiology
2.5 Infectious vs. Non-infectious Diseases
2.6 COVID 19 Pandemic and Public Health
2.7 Growth Driver Analysis
2.8 Market Challenge Analysis
2.9 Public Health Policy and Outcomes
2.9.1 Public Health Data Exchange
2.10 Regulatory Analysis
2.10.1 GDPR
2.10.2 HIPAA
2.10.3 ISO Standards
2.10.4 HITECH
2.10.5 ETSI
2.11 Value Chain Analysis
2.11.1 Data Warehouse
2.11.2 AI Companies
2.11.3 Software Development
2.11.4 Semiconductor Providers
2.11.5 Infrastructure and Connectivity Providers
2.11.6 Analytics Providers
2.11.7 Healthcare Service Providers
2.11.8 Regulatory Bodies
3.0 Technology and Application Analysis
3.1 Hardware Technology Analysis
3.1.1 AI Processors and Chipsets
3.1.1.1 Microprocessor Unit (MPU)
3.1.1.2 Tensor Processing Unit (TPU)
3.1.1.3 Graphics Processing Unit (GPU)
3.1.1.4 Field-Programmable Gate Array (FPGA)
3.1.1.5 Application Specific Integrated Circuits (ASIC)
3.1.1.6 Intelligent Processing Unit (IPU)
3.1.2 Memory Chip
3.1.3 Network Adaptor
3.1.4 3D Sensors
3.2 Software Technology Analysis
3.2.1 AI Solution: Cloud vs. On-premise Software
3.2.2 AI Platform Framework and APIs
3.3 AI Technology Analysis
3.3.1 Machine Learning and Deep Learning
3.3.2 Natural Language Processing (NLP)
3.3.3 Computer Vision: Image and Voice Processing
3.3.4 Neural Network Processing
3.3.5 Context Aware Processing
3.4 Enabling Technology Analysis
3.4.1 Electronic Health Records
3.4.2 Social Media Analytics
3.4.3 Traffic Surveillance Systems
3.4.4 Digital Health Passports
3.4.5 Computer-Based Simulation Models
3.4.6 Protective Gear and Equipment
3.4.7 Telemedicine Solutions
3.4.8 Semantics-Based Health Information System
3.4.9 Health Information Technology
3.4.10 Electronic Data Capture
3.4.11 Clinical Data Management Systems
3.4.12 Patient Data Management System
3.4.13 Laboratory Information Management Systems
3.4.14 Internet of Healthcare Technology
3.5 Application Analysis
3.5.1 Disease and Syndromic Surveillance
3.5.2 Infection Prediction and Forecasting
3.5.3 Immunization Information Systems
3.5.4 Public Sentiment Analysis
3.5.5 Environmental Impact Analysis
3.5.6 Drug Discovery, Safety, and Risk Analysis
3.5.7 Monitoring Population and Incidence
3.5.8 Knowledge Representation and Mass Notification
3.6 Industry Use Case Analysis
3.6.1 Government and State Agencies
3.6.2 MassHealth ACOS and MCOS
3.6.3 Research Labs
3.6.4 Pharmaceuticals Company
3.6.5 Hospital, Specialty Clinics, and Healthcare Providers
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