Predictive Disease Analytics Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032
The Global Predictive Disease Analytics Market, valued at USD 2.5 billion in 2023, is projected to grow at a CAGR of 21.7% from 2024 to 2032. This growth is driven by a focus on streamlining healthcare processes, preventive healthcare, and advancements in AI and machine learning technologies.
The demand for precision medicine and data-driven decision-making in healthcare accelerates the adoption of predictive analytics solutions. These technologies enable healthcare providers to foresee patient outcomes, refine treatment plans, and reduce healthcare costs by leveraging data to predict future health events. For example, in April 2024, researchers at Clemson University are exploring AI technologies for precision medicine, examining drug mechanisms alongside patients' genetic profiles.
A notable trend is the rise in cloud-based solutions. Cloud deployment offers scalability, flexibility, and cost-effectiveness, allowing healthcare organizations to manage large data volumes and access analytics tools remotely, enhancing data integration and real-time analysis.
The overall predictive disease analytics industry is segmented based on component, deployment mode, end-use, and region.
The software segment, which generated USD 2 billion in 2023, includes tools and platforms for health data analysis and predictive insights. This segment is expected to maintain a significant market share due to the demand for advanced analytics that integrate with existing healthcare systems. Features like data visualization, risk assessment, and outcome prediction make the software essential for healthcare organizations. In July 2024, Cardio Diagnostics Holdings Inc. launched its CDIO.AI web-solution with AI-driven functionalities for cardiovascular diseases.
The market, categorized by deployment mode into on-premises and cloud, saw the on-premises segment leading with USD 1.5 billion in 2023. On-premises deployment, which installs predictive analytics software within an organization's IT environment, offers control and customization but requires significant investment in hardware and maintenance. Organizations with stringent data privacy requirements prefer on-premises solutions to maintain control over sensitive health information and ensure regulatory compliance. For instance, in March 2018, NVIDIA Healthcare introduced generative AI microservices for medtech, drug discovery, and digital health.
North America predictive disease analytics market, with a revenue of USD 919.1 million in 2023, is set to grow at a CAGR of 20.9% from 2024 to 2032. The region's demand for predictive disease analytics is driven by a shift towards value-based healthcare and cost containment. Predictive analytics helps identify high-risk patients, optimize treatment plans, and reduce unnecessary hospital visits. Rapid technological advancements and access to sophisticated data analytics platforms further accelerate adoption, leveraging AI and machine learning for accurate predictions and actionable insights.
- Chapter 1 Methodology and Scope
- 1.1 Market scope and definitions
- 1.2 Research design
- 1.2.1 Research approach
- 1.2.2 Data collection methods
- 1.3 Base estimates and calculations
- 1.3.1 Base year calculation
- 1.3.2 Key trends for market estimation
- 1.4 Forecast model
- 1.5 Primary research and validation
- 1.5.1 Primary sources
- 1.5.2 Data mining sources
- Chapter 2 Executive Summary
- 2.1 Industry 360° synopsis
- Chapter 3 Industry Insights
- 3.1 Industry ecosystem analysis
- 3.2 Industry impact forces
- 3.2.1 Growth drivers
- 3.2.1.1 Increasing focus on streamlining of healthcare processes
- 3.2.1.2 Rising focus on preventive healthcare
- 3.2.1.3 Advancements in AI and machine learning technologies coupled with improved patient outcomes
- 3.2.2 Industry pitfalls and challenges
- 3.2.2.1 Data privacy and security concerns
- 3.3 Growth potential analysis
- 3.4 Regulatory landscape
- 3.5 Innovation landscape
- 3.6 Porter's analysis
- 3.7 PESTEL analysis
- 3.8 Future market trends
- 3.9 Gap analysis
- Chapter 4 Competitive Landscape, 2023
- 4.1 Introduction
- 4.2 Company matrix analysis
- 4.3 Competitive analysis of major key players
- 4.4 Competitive positioning matrix
- 4.5 Strategy dashboard
- Chapter 5 Market Estimates and Forecast, By Component, 2021 – 2032 ($ Mn)
- 5.1 Key trends
- 5.2 Software
- 5.3 Services
- Chapter 6 Market Estimates and Forecast, By Deployment Mode, 2021 – 2032 ($ Mn)
- 6.1 Key trends
- 6.2 On-premises
- 6.3 Cloud
- Chapter 7 Market Estimates and Forecast, By End-use, 2021 – 2032 ($ Mn)
- 7.1 Key trends
- 7.2 Healthcare payers
- 7.3 Healthcare providers
- 7.4 Other end-users
- Chapter 8 Market Estimates and Forecast, By Region, 2021 – 2032 ($ Mn)
- 8.1 Key trends
- 8.2 North America
- 8.2.1 U.S.
- 8.2.2 Canada
- 8.3 Europe
- 8.3.1 Germany
- 8.3.2 UK
- 8.3.3 France
- 8.3.4 Spain
- 8.3.5 Italy
- 8.3.6 Netherlands
- 8.3.7 Rest of Europe
- 8.4 Asia Pacific
- 8.4.1 China
- 8.4.2 Japan
- 8.4.3 India
- 8.4.4 Australia
- 8.4.5 South Korea
- 8.4.6 Rest of Asia Pacific
- 8.5 Latin America
- 8.5.1 Brazil
- 8.5.2 Mexico
- 8.5.3 Argentina
- 8.5.4 Rest of Latin America
- 8.6 Middle East and Africa
- 8.6.1 South Africa
- 8.6.2 Saudi Arabia
- 8.6.3 UAE
- 8.6.4 Rest of Middle East and Africa
- Chapter 9 Company Profiles
- 9.1 Allscripts Healthcare Solutions Inc.
- 9.2 Anaconda Inc.
- 9.3 Apixio Inc.
- 9.4 Epic System Corporation
- 9.5 Health Catalyst
- 9.6 IBM
- 9.7 McKesson Corporation
- 9.8 MedeAnalytics, Inc.
- 9.9 Microsoft Corporation
- 9.10 Optum
- 9.11 Oracle
- 9.12 Philips Healthcare
- 9.13 SAS
- 9.14 Siemens Healthineers
Research Assistance
Download our eBook: How to Succeed Using Market Research
Learn how to effectively navigate the market research process to help guide your organization on the journey to success.
Download eBook