헬스케어 예측 분석 시장 보고서 : 동향, 예측, 경쟁 분석(-2031년)

The future of the global healthcare predictive analytics market looks promising, with opportunities in the payers and provider markets. The global healthcare predictive analytics market is expected to reach an estimated $41.1 billion by 2031, with a CAGR of 20.4% from 2025 to 2031. The major drivers for this market are the industry's growing need for advanced analytics tools to save costs and enhance patient outcomes, the growing popularity of individualized healthcare, the emphasis on value-based healthcare, and the rising adoption of electronic health records.

• Lucintel forecasts that, within the application category, financial will remain the largest segment over the forecast period due to healthcare fraud costs billions annually, and predictive analytics helps insurers detect suspicious patterns and behaviors, preventing fraudulent claims and saving significant amounts of money.
• In terms of regions, North America will remain the largest region over the forecast period due to well-equipped healthcare facilities with readily available advanced technological resources like electronic health records and data infrastructure.

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Emerging Trends in the Healthcare Predictive Analytics Market

The healthcare predictive analytics market is experiencing several emerging trends that are shaping its future.

• AI and Machine Learning Integration: There is an increasing use of AI and ML algorithms to enhance predictive accuracy and decision-making in healthcare.
• Personalized Medicine: There is a growing focus on using predictive analytics for personalized treatment plans based on individual patient data.
• Real-Time Analytics: The development of real-time analytics tools provides immediate insights and interventions, improving patient outcomes.
• Big Data Utilization: There is an expanding use of big data from various sources, such as wearables and EHRs, to drive predictive models.
• Predictive Maintenance: The implementation of predictive analytics for equipment maintenance and management in healthcare facilities is becoming more prevalent.

These trends indicate a shift towards more advanced, real-time, and personalized predictive analytics solutions in healthcare, promising improved patient care and operational efficiency.

Recent Developments in the Healthcare Predictive Analytics Market

Recent developments in the healthcare predictive analytics market reflect advancements in technology and application.

• Advanced AI Algorithms: There is an adoption of sophisticated AI and ML algorithms to improve predictive accuracy and patient outcomes.
• Integration with EHR Systems: Predictive analytics tools are being integrated with EHR systems to enhance data utilization and decision-making.
• Predictive Models for Chronic Diseases: The development of predictive models is aimed at better managing chronic diseases and reducing hospital readmissions.
• Enhanced Data Security: There is an implementation of robust data security measures to protect patient information while using predictive analytics.
• Collaborations and Partnerships: There is increased collaboration between healthcare providers and technology firms to advance predictive analytics solutions.
• Government Support: Government initiatives and funding promote the use of predictive analytics in improving healthcare delivery.

These developments highlight the rapid evolution of the healthcare predictive analytics market, driven by technological advancements and an increased focus on improving patient care and operational efficiency.

Strategic Growth Opportunities for Healthcare Predictive Analytics Market

Exploring strategic growth opportunities can drive expansion and innovation in the healthcare predictive analytics market.

• Expansion into Emerging Markets: Targeting emerging markets with growing healthcare infrastructure can increase market reach and impact.
• Development of Specialized Solutions: There is a need for creating predictive analytics solutions tailored to specific healthcare needs, such as oncology or cardiology.
• Integration with IoT Devices: Leveraging data from Internet of Things (IoT) devices can enhance predictive models and real-time monitoring.
• Investment in R&D: Investing in research and development is essential to drive innovation and develop cutting-edge predictive analytics technologies.
• Strategic Partnerships: Forming partnerships with healthcare providers and technology companies can expand product offerings and capabilities.
• Focus on Preventive Care: Developing predictive tools focused on preventive care can reduce healthcare costs and improve patient outcomes.

Focusing on these strategic growth opportunities can enhance the impact of predictive analytics in healthcare, driving innovation and expanding market presence.

Healthcare Predictive Analytics Market Driver and Challenges

Understanding the drivers and challenges in the healthcare predictive analytics market is crucial for navigating growth and addressing obstacles.

The factors responsible for driving the healthcare predictive analytics market include:

• Technological Advancements: Rapid advancements in AI and machine learning are enhancing predictive capabilities and accuracy.
• Increasing Data Availability: The growing availability of big data from EHRs, wearables, and other sources is driving predictive analytics adoption.
• Demand for Personalized Medicine: The rising demand for personalized treatment plans is fueling the need for advanced predictive analytics solutions.
• Operational Efficiency: Predictive analytics helps healthcare organizations optimize operations and reduce costs.
• Government Support: Supportive government initiatives and funding are promoting the use of predictive analytics in healthcare.

Challenges in the healthcare predictive analytics market include:

• Data Privacy Concerns: Ensuring data privacy and compliance with regulations while utilizing predictive analytics can be challenging.
• High Implementation Costs: The cost of implementing advanced predictive analytics solutions can be a barrier for some healthcare organizations.
• Data Integration Issues: Integrating data from various sources to create accurate predictive models can be complex.
• Technical Complexity: The complexity of predictive analytics technologies requires specialized expertise and training.
• Regulatory Compliance: Navigating regulatory requirements and standards can be time-consuming and challenging.
• Limited Interoperability: The lack of interoperability between different healthcare systems can hinder the effectiveness of predictive analytics.

While the healthcare predictive analytics market is driven by technological advancements and increasing demand for personalized care, addressing challenges related to data privacy, cost, and integration is essential for achieving sustainable growth and effectiveness.

List of Healthcare Predictive Analytics Companies

Companies in the market compete based on product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. Through these strategies, healthcare predictive analytics companies cater to increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the healthcare predictive analytics companies profiled in this report include-

• IBM
• Cerner
• Verisk Analytics
• McKesson
• SAS
• Oracle
• Allscripts
• Optum
• MedeAnalytics
• OSP

Healthcare Predictive Analytics by Segment

The study includes a forecast for the global healthcare predictive analytics market by application, end use, and region.

Healthcare Predictive Analytics Market by Application [Analysis by Value from 2019 to 2031]:

• Operations Management
• Financial
• Population Health
• Clinical

Healthcare Predictive Analytics Market by End Use [Analysis by Value from 2019 to 2031]:

• Payers
• Providers
• Others

Healthcare Predictive Analytics Market by Region [Analysis by Value from 2019 to 2031]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Healthcare Predictive Analytics Market

Major players in the market are expanding their operations and forming strategic partnerships to strengthen their positions. The content below highlights recent developments by major healthcare predictive analytics players in key regions: the USA, China, India, and Japan.

• USA: In the United States, the healthcare predictive analytics market is witnessing substantial growth driven by advancements in artificial intelligence (AI) and machine learning (ML). Recent developments include the integration of AI-driven predictive models into Electronic Health Records (EHR) systems, enhancing the ability to forecast patient outcomes and optimize treatment plans. There is also an increasing investment in predictive analytics for reducing hospital readmissions and managing chronic diseases. Furthermore, major healthcare organizations and technology firms are forming partnerships to develop innovative solutions that leverage big data for predictive insights, supporting value-based care models.
• China: China is rapidly advancing its healthcare predictive analytics capabilities, driven by significant investments in health IT infrastructure and AI technologies. Recent developments include the implementation of predictive analytics in public health initiatives, such as epidemic forecasting and disease prevention. Chinese technology companies are developing advanced analytics platforms that integrate big data from various sources, including wearable devices and health records, to improve disease management and patient outcomes. The government is supporting these advancements through initiatives aimed at modernizing the healthcare system and enhancing predictive analytics applications for better public health management.
• India: In India, the healthcare predictive analytics market is growing with a focus on enhancing healthcare delivery and management. Recent developments include the adoption of predictive analytics for improving patient care and operational efficiency in hospitals. Indian startups and technology firms are developing affordable analytics solutions tailored to local healthcare challenges, such as managing chronic diseases and optimizing resource allocation. There is also increasing collaboration between healthcare providers and tech companies to integrate predictive analytics into health management systems, supported by government initiatives to boost digital health infrastructure and data utilization.
• Japan: Japan's healthcare predictive analytics market is evolving with advancements in data integration and AI technologies. Recent developments include the use of predictive analytics to support personalized medicine and improve patient outcomes through advanced modeling techniques. Japanese healthcare institutions are increasingly adopting predictive tools for early disease detection and treatment optimization. The government's support for digital health innovation and research is driving the development of new predictive analytics solutions. Additionally, Japan is focusing on integrating predictive analytics with existing health information systems to enhance overall healthcare efficiency and patient management.

Features of the Global Healthcare Predictive Analytics Market

Market Size Estimates: Healthcare predictive analytics market size estimation in terms of value ($B).

Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.

Segmentation Analysis: Healthcare predictive analytics market size by application, end use, and region in terms of value ($B).

Regional Analysis: Healthcare predictive analytics market breakdown by North America, Europe, Asia Pacific, and Rest of the World.

Growth Opportunities: Analysis of growth opportunities in different applications, end uses, and regions for the healthcare predictive analytics market.

Strategic Analysis: This includes M&A, new product development, and the competitive landscape of the healthcare predictive analytics market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

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This report answers the following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the healthcare predictive analytics market by application (operations management, financial, population health, and clinical), end use (payers, providers, and others), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market, and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years, and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Global Healthcare Predictive Analytics Market : Market Dynamics

• 2.1: Introduction, Background, and Classifications
• 2.2: Supply Chain
• 2.3: Industry Drivers and Challenges

3. Market Trends and Forecast Analysis from 2019 to 2031

• 3.1. Macroeconomic Trends (2019-2024) and Forecast (2025-2031)
• 3.2. Global Healthcare Predictive Analytics Market Trends (2019-2024) and Forecast (2025-2031)
• 3.3: Global Healthcare Predictive Analytics Market by Application
  • 3.3.1: Operations Management
  • 3.3.2: Financial
  • 3.3.3: Population Health
  • 3.3.4: Clinical
• 3.4: Global Healthcare Predictive Analytics Market by End Use
  • 3.4.1: Payers
  • 3.4.2: Providers
  • 3.4.3: Others

4. Market Trends and Forecast Analysis by Region from 2019 to 2031

• 4.1: Global Healthcare Predictive Analytics Market by Region
• 4.2: North American Healthcare Predictive Analytics Market
  • 4.2.1: North American Market by Application: Operations Management, Financial, Population Health, and Clinical
  • 4.2.2: North American Market by End Use: Payers, Providers, and Others
• 4.3: European Healthcare Predictive Analytics Market
  • 4.3.1: European Market by Application: Operations Management, Financial, Population Health, and Clinical
  • 4.3.2: European Market by End Use: Payers, Providers, and Others
• 4.4: APAC Healthcare Predictive Analytics Market
  • 4.4.1: APAC Market by Application: Operations Management, Financial, Population Health, and Clinical
  • 4.4.2: APAC Market by End Use: Payers, Providers, and Others
• 4.5: ROW Healthcare Predictive Analytics Market
  • 4.5.1: ROW Market by Application: Operations Management, Financial, Population Health, and Clinical
  • 4.5.2: ROW Market by End Use: Payers, Providers, and Others

5. Competitor Analysis

• 5.1: Product Portfolio Analysis
• 5.2: Operational Integration
• 5.3: Porter's Five Forces Analysis

6. Growth Opportunities and Strategic Analysis

• 6.1: Growth Opportunity Analysis
  • 6.1.1: Growth Opportunities for the Global Healthcare Predictive Analytics Market by Application
  • 6.1.2: Growth Opportunities for the Global Healthcare Predictive Analytics Market by End Use
  • 6.1.3: Growth Opportunities for the Global Healthcare Predictive Analytics Market by Region
• 6.2: Emerging Trends in the Global Healthcare Predictive Analytics Market
• 6.3: Strategic Analysis
  • 6.3.1: New Product Development
  • 6.3.2: Capacity Expansion of the Global Healthcare Predictive Analytics Market
  • 6.3.3: Mergers, Acquisitions, and Joint Ventures in the Global Healthcare Predictive Analytics Market
  • 6.3.4: Certification and Licensing

7. Company Profiles of Leading Players

• 7.1: IBM
• 7.2: Cerner
• 7.3: Verisk Analytics
• 7.4: McKesson
• 7.5: SAS
• 7.6: Oracle
• 7.7: Allscripts
• 7.8: Optum
• 7.9: MedeAnalytics
• 7.10: OSP