ヘルスケアにおけるビッグデータ分析市場：分析タイプ別、用途別、展開別、エンドユーザー別、地域別、2026～2032年

ヘルスケアにおけるビッグデータ分析市場の評価-2026～2032年

集団健康管理のための分析ソリューションに対する需要の増加、健康管理と戦略を最適化するためのビジネスインテリジェンスに対するニーズの高まり、ヘルスケア産業におけるビッグデータ導入の急増が市場成長を促進する要因となっています。Verified Market Researchのアナリストによると、ヘルスケアにおけるビッグデータ分析市場規模は2024年に372億2,000万米ドルと推定され、2032年には748億2,000万米ドルに達する見込みです。

量ベースから金額ベースのケアモデルへの移行に伴い、患者の転帰を評価し、ヘルスケア提供を最適化するための堅牢なデータ分析が必要となり、ビッグデータソリューションの需要が高まっています。ヘルスケアにおけるビッグデータ分析市場は、予測期間2026～2032年にCAGR 9.12%で成長すると予測されています。

ヘルスケアにおけるビッグデータ分析市場定義/概要

ヘルスケアにおけるビッグデータ分析は、ヘルス分析と呼ばれることも多く、大量の複雑な医療関連データを収集、分析、解釈して、医療の提供や意思決定を強化できる有意義な洞察を導き出すプロセスです。このセグメントには、電子カルテ（EHR）、ゲノムデータ、リアルタイムの患者情報など、さまざまな種類のデータが含まれ、ヘルスケアプロバイダはパターンを特定し、転帰を予測し、患者ケアを改善することができます。

予測モデリングや機械学習などの先進的分析技術を活用することで、ビッグデータ分析はヘルスケア専門家が、患者の転帰改善、コスト削減、リソース配分の最適化につながる、情報に基づいた意思決定を行うことを可能にします。多様なデータソースの統合は、個々の患者のニーズをより深く理解するだけでなく、集団全体の動向や危険因子を特定することで、より広範な公衆衛生イニシアチブをサポートします。

電子カルテ（EHR）の導入拡大は、ヘルスケアにおけるビッグデータ分析の需要にどのような影響を与えていますか？

電子カルテ（EHR）の普及拡大は、ヘルスケアにおけるビッグデータ分析の需要に大きな影響を与えています。EHRは包括的でアクセスしやすい患者データを提供することで、ヘルスケア専門家が先進的分析技術を活用して患者ケアと業務効率を改善することを可能にしています。米国保健社会福祉省によると、クリニックの医師の85％以上がEHRシステムを導入しており、医療従事者間での患者情報のシームレスな交換を促進し、ケアの連携と質を高めています。EHRは膨大な量の臨床データを集約するため、健康上の転帰を予測し、リスクのある集団を特定できる予測分析モデルの開発をサポートし、最終的にはより良い臨床上の意思決定を促進します。

さらに、EHRの導入が投薬ミスの減少や臨床ガイドラインの遵守率向上につながるという研究結果もあり、ヘルスケア提供の強化におけるデータ分析の役割がさらに強調されています。その結果、EHRとビッグデータ分析の統合は、個別化医療を推進し、全体的な健康アウトカムを改善する上で重要な要素であると考えられています。

ヘルスケアにおけるビッグデータ分析の実装に関連するデータのプライバシーとセキュリティの主要懸念事項とは？

ヘルスケアにおけるビッグデータ分析の実装は、いくつかの重大なデータプライバシーとセキュリティの懸念と関連しています。医療データは機密性が高いため、不正アクセスや侵害のリスクが高く、医療機関の約93％がデータ侵害を経験しているという報告もあります。この憂慮すべき統計は、デジタル化が進む状況下での患者情報の脆弱性を浮き彫りにしています。

プライバシー侵害は、膨大な量の個人健康情報の収集と共有を容易にする電子カルテ（EHR）の広範な使用によって、しばしば悪化します。

米国保健福祉省によると、HIPAAなどの規制は患者のプライバシーを保護するように設計されているが、特に患者の十分な同意なしに大規模なデータセットを共有することに関しては、まだギャップが存在します。その結果、データの暴露による潜在的な差別、個人情報の盗難、精神的苦痛に対する懸念が、患者やヘルスケア提供者の間で広まっています。その結果、規制の枠組みへのコンプライアンスを確保しながらこれらのリスクを軽減するために、先進的暗号化手法と強固なガバナンスの実践の必要性が強調されています。

目次

第1章 ヘルスケアにおけるビッグデータ分析の世界市場導入

• 市場の導入
• 調査範囲
• 前提条件

第2章 エグゼクティブサマリー

第3章 VERIFIED MARKET RESEARCHの調査手法

• データマイニング
• バリデーション
• 一次資料
• データソース一覧

第4章 ヘルスケアにおけるビッグデータ分析の世界市場展望

• 概要
• 市場力学
  • 促進要因
  • 抑制要因
  • 機会
• ポーターのファイブフォースモデル
• バリューチェーン分析

第5章 ヘルスケアにおけるビッグデータ分析の世界市場：分析タイプ別

• 概要
• 記述的
• 予測的
• 処方的
• 診断的

第6章 ヘルスケアにおけるビッグデータ分析の世界市場：用途別

• 概要
• 臨床分析
• 財務分析
• 業務分析
• 調査分析

第7章 ヘルスケアにおけるビッグデータ分析の世界市場：エンドユーザー別

• 概要
• 病院とクリニック
• ヘルスケアペイヤー
• 研究機関
• 製薬会社
• バイオテクノロジー企業

第8章 ヘルスケアにおけるビッグデータ分析の世界市場：展開別

• 概要
• オンプレミス
• クラウドベース
• ハイブリッド

第9章 ヘルスケアにおけるビッグデータ分析の世界市場：地域別

• 概要
• 北米
  • 米国
  • カナダ
  • メキシコ
• 欧州
  • ドイツ
  • 英国
  • フランス
  • その他の欧州
• アジア太平洋
  • 中国
  • 日本
  • インド
  • その他のアジア太平洋
• その他
  • ラテンアメリカ
  • 中東・アフリカ

第10章 ヘルスケアにおけるビッグデータ分析の世界市場競合情勢

• 概要
• 各社の市場ランキング
• 主要開発戦略

第11章 企業プロファイル

• Allscripts
• Cerner Corporation
• Dell EMC
• Epic Systems Corporation
• GE Healthcare
• Hewlett Packard Enterprise
• IBM
• Microsoft
• Optum
• Oracle

第12章 付録

• 関連調査

Big Data Analytics In Healthcare Market Valuation - 2026-2032

The increase in demand for analytics solutions for population health management, the rise in the need for business intelligence to optimize health administration and strategy, and the surge in the adoption of big data in the healthcare industry are the factors that drive the growth of the market. According to the analyst from Verified Market Research, the big data analytics in healthcare market size is estimated at USD 37.22 Billion in 2024, and is expected to reach USD 74.82 Billion by 2032.

The transition from volume-based to value-based care models necessitates robust data analytics to assess patient outcomes and optimize healthcare delivery, driving demand for big data solutions. The big data analytics in healthcare market is projected to grow at a CAGR of 9.12% during the forecast period 2026-2032.

Big Data Analytics In Healthcare Market: Definition/ Overview

Big Data Analytics in Healthcare, often referred to as health analytics, is the process of collecting, analyzing, and interpreting large volumes of complex health-related data to derive meaningful insights that can enhance healthcare delivery and decision-making. This field encompasses various data types, including electronic health records (EHRs), genomic data, and real-time patient information, allowing healthcare providers to identify patterns, predict outcomes, and improve patient care.

By leveraging advanced analytical techniques, such as predictive modeling and machine learning, Big Data Analytics enables healthcare professionals to make informed decisions that can lead to better patient outcomes, reduced costs, and optimized resource allocation. The integration of diverse data sources not only facilitates a deeper understanding of individual patient needs but also supports broader public health initiatives by identifying trends and risk factors across populations.

How is the Growing Adoption of Electronic Health Records (EHRs) Influencing the Demand for Big Data Analytics in Healthcare?

The growing adoption of Electronic Health Records (EHRs) is significantly influencing the demand for Big Data Analytics in healthcare. EHRs, by providing comprehensive and accessible patient data, are enabling healthcare professionals to leverage advanced analytical techniques to improve patient care and operational efficiency. According to the U.S. Department of Health and Human Services, over 85% of office-based physicians have adopted EHR systems, which has facilitated the seamless exchange of patient information among providers, thereby enhancing care coordination and quality. As EHRs aggregate vast amounts of clinical data, they support the development of predictive analytics models that can forecast health outcomes and identify at-risk populations, ultimately driving better clinical decision-making.

Moreover, studies have shown that EHR implementation is associated with reduced medication errors and improved adherence to clinical guidelines, further underscoring the role of data analytics in enhancing healthcare delivery. Consequently, the integration of EHRs with Big Data Analytics is seen as a critical factor in advancing personalized medicine and improving overall health outcomes.

What are the Primary Data Privacy and Security Concerns Associated With the Implementation of Big Data Analytics in Healthcare?

The implementation of Big Data Analytics in healthcare is associated with several significant data privacy and security concerns. The sensitive nature of medical data raises the risk of unauthorized access and breaches, with reports indicating that approximately 93% of healthcare organizations have experienced a data breach at some point. This alarming statistic highlights the vulnerability of patient information in an increasingly digital landscape.

Privacy violations are often exacerbated by the extensive use of electronic health records (EHRs), which facilitate the collection and sharing of vast amounts of personal health information.

According to the U.S. Department of Health and Human Services, regulations such as HIPAA are designed to protect patient privacy; however, gaps still exist, particularly concerning the sharing of large datasets without adequate patient consent. As a result, concerns about potential discrimination, identity theft, and emotional distress due to data exposure are prevalent among patients and healthcare providers alike. Consequently, the need for advanced encryption methods and robust governance practices is emphasized to mitigate these risks while ensuring compliance with regulatory frameworks.

Category-Wise Acumens

How is Predictive Analytics Being Utilized to Anticipate Patient Outcomes and Improve Clinical Decision-Making in Healthcare?

Predictive analytics is being utilized in healthcare to anticipate patient outcomes and enhance clinical decision-making through the analysis of historical and real-time data. By employing advanced algorithms and machine learning techniques, healthcare providers are able to identify patterns and correlations within vast datasets, which allows for the forecasting of potential health events. According to the Centers for Disease Control and Prevention, over 60% of healthcare organizations are currently using predictive analytics to improve patient health outcomes and operational efficiency. For instance, predictive models can assess a patient's risk of developing chronic diseases, enabling early interventions that can significantly improve recovery rates.

Additionally, these analytics facilitate personalized treatment plans tailored to individual patient profiles, which enhances the effectiveness of care. As a result, clinicians are empowered with actionable insights that support informed decision-making, ultimately leading to better patient outcomes and reduced readmission rates.

How is Clinical Analytics Improving Patient Care Through Real-Time Monitoring and Personalized Treatment Plans?

Clinical analytics is being leveraged to improve patient care through real-time monitoring and the development of personalized treatment plans. By utilizing advanced data analytics, healthcare providers are able to continuously track patient health metrics, which facilitates timely interventions when changes in a patient's condition are detected. According to the U.S. Department of Health and Human Services, approximately 70% of healthcare organizations have implemented real-time analytics systems to enhance patient monitoring capabilities.

This technology allows for immediate alerts regarding critical changes in vital signs, enabling clinicians to respond swiftly and effectively.

Additionally, personalized treatment plans are being informed by comprehensive patient data, which includes historical health records and real-time health metrics. This integration of data supports tailored interventions that align with individual patient needs, ultimately leading to improved health outcomes. As a result, the quality of care is enhanced, and unnecessary hospitalizations can be reduced, demonstrating the significant impact of clinical analytics on modern healthcare practices.

Country/Region-wise Acumens

How does the Presence of Major Healthcare Analytics Companies and Technology Providers in North America Influence the Region's Leadership in the Market?

The presence of major healthcare analytics companies and technology providers in North America significantly influences the region's leadership in the market by fostering innovation, enhancing competition, and driving widespread adoption of advanced analytics solutions.

With key players such as McKesson Corporation, IBM, and Optum leading the charge, substantial investments are being made in research and development, which accelerates the creation of cutting-edge technologies tailored to healthcare needs. According to the U.S. Department of Health and Human Services, approximately 83% of the North American healthcare analytics market is attributed to U.S. companies, underscoring the dominance of this region. The collaboration between these companies and healthcare institutions facilitates the integration of analytics into clinical workflows, improving patient outcomes through data- driven decision-making.

Furthermore, government initiatives, such as the National Health Information Technology (HIT) initiative, promote interoperability and data sharing, which are essential for maximizing the benefits of analytics in healthcare. As a result, North America is positioned as a leader in healthcare analytics, characterized by a robust ecosystem that supports continuous improvement in patient care and operational efficiency.

What Role do European Healthcare Regulations, Such as GDPR, Play in Shaping the Adoption and Growth of Big Data Analytics in the Region?

European healthcare regulations, particularly the General Data Protection Regulation (GDPR), play a crucial role in shaping the adoption and growth of Big Data Analytics in the region. By establishing stringent standards for data protection and privacy, GDPR mandates that healthcare organizations implement robust security measures and obtain explicit consent from patients before processing their personal health information. According to the European Commission, approximately 75% of EU citizens express concerns about how their personal data is used, highlighting the importance of trust in healthcare analytics.

The regulation also emphasizes transparency, requiring organizations to inform patients about how their data will be utilized, thus fostering a patient-centered approach to data management. Furthermore, GDPR grants patients significant rights over their data, such as the right to access and the right to be forgotten, which necessitates healthcare providers to develop systems that comply with these requirements. As a result, while GDPR may initially pose challenges for data sharing and analytics, it ultimately encourages the development of secure and ethical data practices that can enhance patient care and drive innovation in healthcare analytics across Europe.

Competitive Landscape

The competitive landscape of the Big Data Analytics market is characterized by intense rivalry among established giants and emerging players, each vying for market share through innovation and strategic partnerships.

• In October 2024, HPE introduced the HPE Private Cloud AI, a turnkey cloud-based experience co-developed with NVIDIA. This solution allows businesses to build and deploy generative AI applications quickly, streamlining the process with one-click deployment of AI applications, including virtual assistants that can be customized for various business needs.
• In June 2024, Dell EMC announced a new partnership with BlueData to integrate its EPIC(TM) software with Dell servers, facilitating the quick deployment of big data analytics environments. This collaboration enables customers to provision analytics resources in minutes, significantly enhancing operational efficiency.

Big Data Analytics In Healthcare Market, By Category

• Analytics Type:
• Descriptive
• Predictive
• Prescriptive
• Diagnostic
• Application:
• Clinical Analytics
• Financial Analytics
• Operational Analytics
• Research Analytics
• Deployment:
• On-Premise
• Cloud-Based
• Hybrid
• End-Users:
• Hospitals And Clinics
• Healthcare Payers
• Research Organizations
• Pharmaceuticals
• Biotechnology Companies

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL BIG DATA ANALYTICS IN HEALTHCARE MARKET

• 1.1 Introduction of the Market
• 1.2 Scope of Report
• 1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

• 3.1 Data Mining
• 3.2 Validation
• 3.3 Primary Interviews
• 3.4 List of Data Sources

4 GLOBAL BIG DATA ANALYTICS IN HEALTHCARE MARKET OUTLOOK

• 4.1 Overview
• 4.2 Market Dynamics
  • 4.2.1 Drivers
  • 4.2.2 Restraints
  • 4.2.3 Opportunities
• 4.3 Porters Five Force Model
• 4.4 Value Chain Analysis

5 GLOBAL BIG DATA ANALYTICS IN HEALTHCARE MARKET, BY ANALYTICS TYPE

• 5.1 Overview
• 5.2 Descriptive
• 5.3 Predictive
• 5.4 Prescriptive
• 5.5 Diagnostic

6 GLOBAL BIG DATA ANALYTICS IN HEALTHCARE MARKET, BY APPLICATION

• 6.1 Overview
• 6.2 Clinical Analytics
• 6.3 Financial Analytics
• 6.4 Operational Analytics
• 6.5 Research Analytics

7 GLOBAL BIG DATA ANALYTICS IN HEALTHCARE MARKET, BY END-USERS

• 7.1 Overview
• 7.2 Hospitals And Clinics
• 7.3 Healthcare Payers
• 7.4 Research Organizations
• 7.5 Pharmaceuticals
• 7.6 Biotechnology Companies

8 GLOBAL BIG DATA ANALYTICS IN HEALTHCARE MARKET, BY DEPLOYMENT

• 8.1 Overview
• 8.2 On-Premise
• 8.3 Cloud-Based
• 8.4 Hybrid

9 GLOBAL BIG DATA ANALYTICS IN HEALTHCARE MARKET, BY GEOGRAPHY

• 9.1 Overview
• 9.2 North America
  • 9.2.1 U.S.
  • 9.2.2 Canada
  • 9.2.3 Mexico
• 9.3 Europe
  • 9.3.1 Germany
  • 9.3.2 U.K.
  • 9.3.3 France
  • 9.3.4 Rest of Europe
• 9.4 Asia Pacific
  • 9.4.1 China
  • 9.4.2 Japan
  • 9.4.3 India
  • 9.4.4 Rest of Asia Pacific
• 9.5 Rest of the World
  • 9.5.1 Latin America
  • 9.5.2 Middle East and Africa

10 GLOBAL BIG DATA ANALYTICS IN HEALTHCARE MARKET COMPETITIVE LANDSCAPE

• 10.1 Overview
• 10.2 Company Market Ranking
• 10.3 Key Development Strategies

11 COMPANY PROFILES

• 11.1 Allscripts
  • 11.1.1 Overview
  • 11.1.2 Financial Performance
  • 11.1.3 Product Outlook
  • 11.1.4 Key Developments
• 11.2 Cerner Corporation
  • 11.2.1 Overview
  • 11.2.2 Financial Performance
  • 11.2.3 Product Outlook
  • 11.2.4 Key Developments
• 11.3 Dell EMC
  • 11.3.1 Overview
  • 11.3.2 Financial Performance
  • 11.3.3 Product Outlook
  • 11.3.4 Key Developments
• 11.4 Epic Systems Corporation
  • 11.4.1 Overview
  • 11.4.2 Financial Performance
  • 11.4.3 Product Outlook
  • 11.4.4 Key Developments
• 11.5 GE Healthcare
  • 11.5.1 Overview
  • 11.5.2 Financial Performance
  • 11.5.3 Product Outlook
  • 11.5.4 Key Developments
• 11.6 Hewlett Packard Enterprise
  • 11.6.1 Overview
  • 11.6.2 Financial Performance
  • 11.6.3 Product Outlook
  • 11.6.4 Key Developments
• 11.7 IBM
  • 11.7.1 Overview
  • 11.7.2 Financial Performance
  • 11.7.3 Product Outlook
  • 11.7.4 Key Developments
• 11.8 Microsoft
  • 11.8.1 Overview
  • 11.8.2 Financial Performance
  • 11.8.3 Product Outlook
  • 11.8.4 Key Developments
• 11.9 Optum
  • 11.9.1 Overview
  • 11.9.2 Financial Performance
  • 11.9.3 Product Outlook
  • 11.9.4 Key Developments
• 11.10 Oracle
  • 11.10.1 Overview
  • 11.10.2 Financial Performance
  • 11.10.3 Product Outlook
  • 11.10.4 Key Developments

12 Appendix

• 12.1 Related Research