세계의 질병 예측 분석 시장 : 시장 규모, 점유율, 동향 분석 - 컴포넌트별, 전개 방식별, 최종 용도별, 지역별, 부문별 예측(2025-2030년)

Predictive Disease Analytics Market Growth & Trends:

The global predictive disease analytics market size is expected to reach USD 10.2 billion by 2030 and is expected to expand at 22.7% CAGR from 2025 to 2030, according to a new report by Grand View Research, Inc. Predictive disease analytics refers to software solutions used by healthcare organizations, hospitals, and physicians to analyze and process patient data in order to provide data-driven, high-quality care, precise diagnostics, and individualized treatments. Predictive analytics is an advanced method to enhance patient outcomes in healthcare.

Additionally, predictive analytics solutions are used by healthcare providers to comprehend disease prevalence, disease management, risk management & trajectories, and to provide suitable medical care to patients for optimal outcomes. Healthcare providers use it to evaluate the risk rate to an individual's health as well as predict future outcomes in preparation. Its use at various levels to avoid unnecessary expenditure had also increased the utilization of this technology, which is anticipated to continue throughout the forecast period. As a result, the global predictive disease analytics market is expected to grow.

Predictive analytics has played a significant part in combating COVID-19 problems, decreasing the incidence of poor outcomes for patients, and addressing resource management during the crisis. The massive amount of patient data produced during the outbreak not only provided analytics firms and the healthcare sector with data to properly analyze disease spread, but it also aided in resource allocation. Several institutions, including hospitals, was using predictive analytics to determine the probability of a patient developing severe symptoms, the trajectory of their infection/disease, and several other parameters. For instance, in 2020, researchers at the Cleveland Clinic in the U. S. created an automated analytics model that predicted an individual's chance of testing positive. Hence, propelling the market growth.

The global market is expanding rapidly as a result of the increasing burden of chronic diseases, the emergence of personalized and evidence-based medicine, the growing need for increased efficiency in the healthcare sector, and the growing demand to reduce healthcare costs by eliminating unnecessary costs. As per the center for disease control, six out of every ten Americans have at least one chronic illness, such as heart disease or stroke, cancer, or diabetes. Hence, it will increase the product demand and further boost the market growth.

Another important reason driving market growth is the use of evidence-based medicine to provide the right kind of treatment to the right patient. The utilization of Electronic Health Records (EHRs) for patient records has increased in recent years, with adoption of EHRs in the case of office-based doctors increasing from 42% in 2008 to nearly 88% in 2021 in the U. S. Healthcare predictive analytics makes use of EHRs to recommend the best course of action in the event of a medical treatment or medication. This not only greatly reduces patient costs but also leads to improved outcomes. The adoption of electronic health systems, analytics tools, and artificial intelligence (AI) are expected to drive market development. The expense of healthcare has risen over the years owing to the increasing burden of chronic diseases on a global level.

The companies in the predictive disease analytics market are continuously expanding their product and services portfolio to accelerate the development of the product. For instance, in June 2022, Engagys LLC, a Health Consultant Company, announced the debut of a new affordable subscription service for healthcare systems, health plans, & healthcare technology leaders. Executive Advisory Services is intended to provide healthcare customer engagement practitioners with practical and actionable guidance, insights, and best practices that they can put to use immediately. This is expected to strengthen the company's position in predictive analytics solution and further boost the market growth.

Predictive Disease Analytics Market Report Highlights:

• Cloud-based solutions are anticipated to have the highest CAGR during the forecast period. The ease of storage, inexpensive capital requirements, increased flexibility, and efficiency are attributed to its development.
• On the basis of end user, payer segment dominated the market in 2023.Payers, such as insurance firms, health plan sponsors, as well as other third-party payers, are among the primary beneficiaries of predictive analytics in healthcare settings. Thus, boosting the segment growth.
• Due to the rising prevalence of chronic diseases, greater awareness of preventive health and rising percentage of geriatric population in North America, the region had the largest market share.

Table of Contents

Chapter 1. Methodology and Scope

• 1.1. Market Segmentation & Scope
• 1.2. Market Definitions
  • 1.2.1. Component Type Segment
  • 1.2.2. Deployment Segment
  • 1.2.3. End User Segment
• 1.3. Information analysis
  • 1.3.1. Market formulation & data visualization
• 1.4. Data validation & publishing
• 1.5. Information Procurement
  • 1.5.1. Primary Research
• 1.6. Information or Data Analysis
• 1.7. Market Formulation & Validation
• 1.8. Market Model
• 1.9. Total Market: CAGR Calculation
• 1.10. Objectives
  • 1.10.1. Objective 1
  • 1.10.2. Objective 2

Chapter 2. Executive Summary

• 2.1. Market Outlook
• 2.2. Segment Snapshot
• 2.3. Competitive Insights Landscape

Chapter 3. Predictive Disease Analytics Market Variables, Trends & Scope

• 3.1. Market Lineage Outlook
  • 3.1.1. Parent market outlook
  • 3.1.2. Related/ancillary market outlook.
• 3.2. Market Dynamics
  • 3.2.1. Market Driver Analysis
    • 3.2.1.1. Rise in government initiatives and an increasing amount of money being invested in the healthcare industry
    • 3.2.1.2. Technological advancements in AI and machine learning
    • 3.2.1.3. Increasing prevalence of chronic diseases
    • 3.2.1.4. Growing shift towards personalized medicine
  • 3.2.2. Market Restraint Analysis
    • 3.2.2.1. Growing data privacy concerns
    • 3.2.2.2. Lack of skilled professionals
    • 3.2.2.3. High implementation cost
• 3.3. Predictive disease analytics Market Analysis Tools
  • 3.3.1. Industry Analysis - Porter's
    • 3.3.1.1. Supplier power
    • 3.3.1.2. Buyer power
    • 3.3.1.3. Substitution threat
    • 3.3.1.4. Threat of new entrant
    • 3.3.1.5. Competitive rivalry
  • 3.3.2. PESTEL Analysis
    • 3.3.2.1. Political Landscape
    • 3.3.2.2. Technological Landscape
    • 3.3.2.3. Economic Landscape
    • 3.3.2.4. Environmental Landscape
    • 3.3.2.5. Legal Landscape
    • 3.3.2.6. Social Landscape
  • 3.3.3. Regulatory Framework
  • 3.3.4. Emerging Technologies Trends
  • 3.3.5. Case Study & Insights
    • 3.3.5.1. Use Cases
  • 3.3.6. COVID-19 Impact Analysis

Chapter 4. Predictive Disease Analytics Market: Component Estimates & Trend Analysis

• 4.1. Segment Dashboard
• 4.2. Global Predictive Disease Analytics Movement Analysis
• 4.3. Global Predictive Disease Analytics Market Size & Trend Analysis, by Component, 2018 - 2030 (USD Million)
• 4.4. Software and Services Market
  • 4.4.1. Market estimates and forecasts 2018 - 2030 (USD Million)
• 4.5. Hardware Market
  • 4.5.1. Market estimates and forecasts 2018 - 2030 (USD Million)

Chapter 5. Predictive Disease Analytics Market: Deployment Estimates & Trend Analysis

• 5.1. Segment Dashboard
• 5.2. Global Predictive Disease Analytics Movement Analysis
• 5.3. Global Predictive Disease Analytics Market Size & Trend Analysis, by Component, 2018 - 2030 (USD Million)
• 5.4. On-premise Market
  • 5.4.1. Market estimates and forecasts 2018 - 2030 (USD Million)
• 5.5. Cloud-based Market
  • 5.5.1. Market estimates and forecasts 2018 - 2030 (USD Million)

Chapter 6. Predictive Disease Analytics Market: End Use Estimates & Trend Analysis

• 6.1. Segment Dashboard
• 6.2. Global Predictive Disease Analytics Market End Use Movement Analysis
• 6.3. Global Predictive Disease Analytics Market Size & Trend Analysis, by End Use, 2018 - 2030 (USD Million)
• 6.4. Healthcare Payers
  • 6.4.1. Market estimates and forecasts 2018 - 2030 (USD Million)
• 6.5. Healthcare Providers
  • 6.5.1. Market estimates and forecasts 2018 - 2030 (USD Million)
• 6.6. Other End Users
  • 6.6.1. Market estimates and forecasts 2018 - 2030 (USD Million)

Chapter 7. Predictive Disease Analytics Market: Regional Estimates & Trend Analysis

• 7.1. Regional Market Share Analysis, 2024 & 2030
• 7.2. Regional Market Dashboard
• 7.3. Market Size & Forecasts Trend Analysis, 2018 - 2030:
• 7.4. North America
  • 7.4.1. U.S.
    • 7.4.1.1. Key country dynamics
    • 7.4.1.2. Regulatory framework
    • 7.4.1.3. Competitive scenario
    • 7.4.1.4. U.S. market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.4.2. Canada
    • 7.4.2.1. Key country dynamics
    • 7.4.2.2. Regulatory framework
    • 7.4.2.3. Competitive scenario
    • 7.4.2.4. Canada market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.4.3. Mexico
    • 7.4.3.1. Key country dynamics
    • 7.4.3.2. Regulatory framework
    • 7.4.3.3. Competitive scenario
    • 7.4.3.4. Mexico market estimates and forecasts 2018 - 2030 (USD Million)
• 7.5. Europe
  • 7.5.1. UK
    • 7.5.1.1. Key country dynamics
    • 7.5.1.2. Regulatory framework
    • 7.5.1.3. Competitive scenario
    • 7.5.1.4. UK market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.5.2. Germany
    • 7.5.2.1. Key country dynamics
    • 7.5.2.2. Regulatory framework
    • 7.5.2.3. Competitive scenario
    • 7.5.2.4. Germany market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.5.3. France
    • 7.5.3.1. Key country dynamics
    • 7.5.3.2. Regulatory framework
    • 7.5.3.3. Competitive scenario
    • 7.5.3.4. France market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.5.4. Italy
    • 7.5.4.1. Key country dynamics
    • 7.5.4.2. Regulatory framework
    • 7.5.4.3. Competitive scenario
    • 7.5.4.4. Italy market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.5.5. Spain
    • 7.5.5.1. Key country dynamics
    • 7.5.5.2. Regulatory framework
    • 7.5.5.3. Competitive scenario
    • 7.5.5.4. Spain market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.5.6. Norway
    • 7.5.6.1. Key country dynamics
    • 7.5.6.2. Regulatory framework
    • 7.5.6.3. Competitive scenario
    • 7.5.6.4. Norway market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.5.7. Sweden
    • 7.5.7.1. Key country dynamics
    • 7.5.7.2. Regulatory framework
    • 7.5.7.3. Competitive scenario
    • 7.5.7.4. Sweden market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.5.8. Denmark
    • 7.5.8.1. Key country dynamics
    • 7.5.8.2. Regulatory framework
    • 7.5.8.3. Competitive scenario
    • 7.5.8.4. Denmark market estimates and forecasts 2018 - 2030 (USD Million)
• 7.6. Asia Pacific
  • 7.6.1. Japan
    • 7.6.1.1. Key country dynamics
    • 7.6.1.2. Regulatory framework
    • 7.6.1.3. Competitive scenario
    • 7.6.1.4. Japan market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.6.2. China
    • 7.6.2.1. Key country dynamics
    • 7.6.2.2. Regulatory framework
    • 7.6.2.3. Competitive scenario
    • 7.6.2.4. China market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.6.3. India
    • 7.6.3.1. Key country dynamics
    • 7.6.3.2. Regulatory framework
    • 7.6.3.3. Competitive scenario
    • 7.6.3.4. India market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.6.4. Australia
    • 7.6.4.1. Key country dynamics
    • 7.6.4.2. Regulatory framework
    • 7.6.4.3. Competitive scenario
    • 7.6.4.4. Australia market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.6.5. South Korea
    • 7.6.5.1. Key country dynamics
    • 7.6.5.2. Regulatory framework
    • 7.6.5.3. Competitive scenario
    • 7.6.5.4. South Korea market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.6.6. Thailand
    • 7.6.6.1. Key country dynamics
    • 7.6.6.2. Regulatory framework
    • 7.6.6.3. Competitive scenario
    • 7.6.6.4. Thailand market estimates and forecasts 2018 - 2030 (USD Million)
• 7.7. Latin America
  • 7.7.1. Brazil
    • 7.7.1.1. Key country dynamics
    • 7.7.1.2. Regulatory framework
    • 7.7.1.3. Competitive scenario
    • 7.7.1.4. Brazil market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.7.2. Argentina
    • 7.7.2.1. Key country dynamics
    • 7.7.2.2. Regulatory framework
    • 7.7.2.3. Competitive scenario
    • 7.7.2.4. Argentina market estimates and forecasts 2018 - 2030 (USD Million)
• 7.8. MEA
  • 7.8.1. South Africa
    • 7.8.1.1. Key country dynamics
    • 7.8.1.2. Regulatory framework
    • 7.8.1.3. Competitive scenario
    • 7.8.1.4. South Africa market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.8.2. Saudi Arabia
    • 7.8.2.1. Key country dynamics
    • 7.8.2.2. Regulatory framework
    • 7.8.2.3. Competitive scenario
    • 7.8.2.4. Saudi Arabia market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.8.3. UAE
    • 7.8.3.1. Key country dynamics
    • 7.8.3.2. Regulatory framework
    • 7.8.3.3. Competitive scenario
    • 7.8.3.4. UAE market estimates and forecasts 2018 - 2030 (USD Million)
  • 7.8.4. Kuwait
    • 7.8.4.1. Key country dynamics
    • 7.8.4.2. Regulatory framework
    • 7.8.4.3. Competitive scenario
    • 7.8.4.4. Kuwait market estimates and forecasts 2018 - 2030 (USD Million)

Chapter 8. Competitive Landscape

• 8.1. Company/Competition Categorization
• 8.2. Company Market Position Analysis, 2024
• 8.3. Company Profiles/Listing
  • 8.3.1. Oracle
    • 8.3.1.1. Company overview
    • 8.3.1.2. Financial performance
    • 8.3.1.3. Product benchmarking
    • 8.3.1.4. Strategic initiatives
  • 8.3.2. Cerner Corporation
    • 8.3.2.1. Company overview
    • 8.3.2.2. Financial performance
    • 8.3.2.3. Product benchmarking
    • 8.3.2.4. Strategic initiatives
  • 8.3.3. IBM
    • 8.3.3.1. Company overview
    • 8.3.3.2. Financial performance
    • 8.3.3.3. Product benchmarking
    • 8.3.3.4. Strategic initiatives
  • 8.3.4. SAS
    • 8.3.4.1. Company overview
    • 8.3.4.2. Financial performance
    • 8.3.4.3. Product benchmarking
    • 8.3.4.4. Strategic initiatives
  • 8.3.5. Allscripts Healthcare Solutions Inc.
    • 8.3.5.1. Company overview
    • 8.3.5.2. Financial performance
    • 8.3.5.3. Product benchmarking
    • 8.3.5.4. Strategic initiatives
  • 8.3.6. MedeAnalytics, Inc.
    • 8.3.6.1. Company overview
    • 8.3.6.2. Financial performance
    • 8.3.6.3. Product benchmarking
    • 8.3.6.4. Strategic initiatives
  • 8.3.7. Health Catalyst
    • 8.3.7.1. Company overview
    • 8.3.7.2. Financial performance
    • 8.3.7.3. Product benchmarking
    • 8.3.7.4. Strategic initiatives
  • 8.3.8. Apixio Inc
    • 8.3.8.1. Company overview
    • 8.3.8.2. Financial performance
    • 8.3.8.3. Product benchmarking
    • 8.3.8.4. Strategic initiatives
  • 8.3.9. GE Healthcare (a division of General Electric Company)
    • 8.3.9.1. Company overview
    • 8.3.9.2. Financial performance
    • 8.3.9.3. Product benchmarking
    • 8.3.9.4. Strategic initiatives
  • 8.3.10. Siemens Healthineers
    • 8.3.10.1. Company overview
    • 8.3.10.2. Financial performance
    • 8.3.10.3. Product benchmarking
    • 8.3.10.4. Strategic initiatives