ヘルスケア予測分析市場レポート：2031年までの動向、予測、競合分析

世界のヘルスケア予測分析市場の将来は有望で、保険者と医療提供者市場に機会があります。世界のヘルスケア予測分析市場は、2025年から2031年までのCAGRが20.4%で、2031年までに推定411億米ドルに達すると予測されます。この市場の主な促進要因は、コスト削減と患者転帰の向上を目的とした高度な分析ツールに対する業界のニーズの高まり、個別化ヘルスケアの人気の高まり、バリューベースのヘルスケアの重視、電子カルテの普及の高まりです。

• Lucintelの予測では、用途別では、予測期間中、財務が最大のセグメントであり続けると思われます。これは、ヘルスケアにおける不正行為には年間数十億米ドルのコストがかかるためであり、予測分析は保険会社が疑わしいパターンや行動を検出し、不正請求を防止して多額の費用を節約するのに役立ちます。
• 地域別では、電子カルテやデータインフラなどの高度な技術リソースを容易に利用できる医療施設が整っている北米が、予測期間中も最大地域であり続けると思われます。

ヘルスケア予測分析市場の戦略的成長機会

戦略的成長機会を探ることで、ヘルスケア予測分析市場の拡大とイノベーションを促進することができます。

• 新興市場への拡大：ヘルスケアインフラが成長している新興市場をターゲットにすることで、市場へのリーチと影響力を高めることができます。
• 専門ソリューションの開発：腫瘍学や循環器学など、特定のヘルスケアニーズに合わせた予測分析ソリューションの構築が求められています。
• IoT機器との統合：モノのインターネット（IoT）機器からのデータを活用することで、予測モデルやリアルタイムのモニタリングを強化することができます。
• 研究開発への投資：イノベーションを推進し、最先端の予測分析技術を開発するためには、研究開発への投資が不可欠です。
• 戦略的パートナーシップ：ヘルスケアプロバイダーやテクノロジー企業とパートナーシップを結ぶことで、製品の提供や機能を拡大することができます。
• 予防医療への注力：予防医療に焦点を当てた予測ツールの開発は、医療費の削減と患者の転帰の改善につながります。

こうした戦略的成長機会に注力することで、ヘルスケアにおける予測分析の影響力を高め、イノベーションを推進し、市場でのプレゼンスを拡大することができます。

ヘルスケア予測分析市場の促進要因・課題

ヘルスケア予測分析市場の促進要因・課題を理解することは、成長を導き障害に対処する上で極めて重要です。

ヘルスケア予測分析市場の促進要因は以下の通りです：

• 技術の進歩：AIと機械学習の急速な進歩により、予測能力と精度が向上しています。
• データの利用可能性の増加：EHR、ウェアラブル、その他のソースからのビッグデータの利用可能性の高まりが、予測分析の採用を促進しています。
• 個別化医療の需要：個別化された治療計画に対する需要の高まりが、高度な予測分析ソリューションの必要性を煽っています。
• 業務の効率化：予測分析は、ヘルスケア組織が業務を最適化し、コストを削減するのに役立ちます。
• 政府の支援：政府の支援策と資金援助により、ヘルスケアにおける予測分析の利用が促進されています。

ヘルスケアの予測分析市場における課題は以下の通りです：

• データプライバシーへの懸念：予測分析を活用しながらデータプライバシーを確保し、規制を遵守することは難しいです。
• 高い導入コスト：高度な予測分析ソリューションの導入コストは、一部のヘルスケア組織にとって障壁となる可能性があります。
• データ統合の問題：様々なソースからのデータを統合して正確な予測モデルを作成することは複雑な場合があります。
• 技術的な複雑さ：予測分析技術は複雑であるため、専門的な知識とトレーニングが必要となります。
• 規制遵守：規制要件や標準を理解するのは時間がかかり、困難な課題です。
• 限られた相互運用性：異なるヘルスケアシステム間の相互運用性の欠如は、予測分析の効果を妨げる可能性があります。

ヘルスケア予測分析市場は、技術の進歩と個別化ケアに対する需要の高まりによって牽引されていますが、持続的な成長と効果を達成するためには、データプライバシー、コスト、統合に関する課題に対処することが不可欠です。

目次

第1章 エグゼクティブサマリー

第2章 世界のヘルスケア予測分析市場：市場力学

• イントロダクション、背景、分類
• サプライチェーン
• 業界の促進要因と課題

第3章 2019年から2031年までの市場動向と予測分析

• マクロ経済動向（2019～2024年）と予測（2025～2031年）
• 世界のヘルスケア予測分析市場の動向（2019～2024年）と予測（2025～2031年）
• 用途別
  • 業務管理
  • 財務
  • 集団健康管理
  • 臨床
• 最終用途別
  • 保険者
  • 医療提供者
  • その他

第4章 2019年から2031年までの地域別市場動向と予測分析

• 地域別
• 北米
• 欧州
• アジア太平洋
• その他地域

第5章 競合分析

• 製品ポートフォリオ分析
• 運用統合
• ポーターのファイブフォース分析

第6章 成長機会と戦略分析

• 成長機会分析
  • 用途別
  • 最終用途別
  • 地域別
• 世界のヘルスケア予測分析市場の新たな動向
• 戦略分析
  • 新製品開発
  • 世界のヘルスケア予測分析市場の能力拡大
  • 世界のヘルスケア予測分析市場における合併、買収、合弁事業
  • 認証とライセンシング

第7章 主要企業の企業プロファイル

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

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