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巨量資料

市場調查報告書

商品編碼

1733185

醫療保健市場巨量資料分析：依分析類型、應用程式、部署、最終用戶和地區，2026-2032年

Big Data Analytics In Healthcare Market By Analytics Type, By Application, By Deployment, By End-Users, Region For 2026-2032

出版日期: 2025年04月30日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

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簡介目錄

醫療保健市場巨量資料分析評估 -2026-2032

人口健康管理對分析解決方案的需求不斷增加、最佳化健康管理和策略的商業智慧需求不斷成長以及醫療保健行業對巨量資料的採用激增都是推動市場成長的因素。根據Verified Market Research分析師預測，2024年醫療保健巨量資料分析市場規模將達372.2億美元，預計2032年將達到748.2億美元。

從基於數量到以金額為準的護理模式的轉變需要強大的資料分析來評估患者結果並最佳化醫療服務，推動對巨量資料解決方案的需求。預計2026-2032年預測期內醫療保健市場的巨量資料分析將以 9.12%的年複合成長率成長。

醫療巨量資料分析市場定義/概述

醫療保健領域的巨量資料分析，通常稱為健康分析，是收集、分析和解釋大量複雜的健康相關資料以獲得有意義的見解，增強醫療保健服務和決策的過程。此部分包括各種類型的資料，例如電子健康記錄（EHR）、基因組資料和即時患者資訊，可以幫助醫療保健提供者識別模式、預測結果並改善患者照護。

透過利用預測模型和機器學習等先進的分析技術，巨量資料分析使醫療保健專業人員能夠做出明智的決策，改善患者的治療效果、降低成本並最佳化資源分配。整合不同的資料來源不僅可以更深入地了解個別患者的需求，還可以透過識別人口的趨勢和風險因素來支持更廣泛的公共衛生舉措。

電子健康記錄（EHR）的日益普及如何影響醫療保健領域對巨量資料分析的需求？

電子健康記錄（EHR）的日益普及對醫療保健領域巨量資料分析的需求產生了重大影響。透過提供全面且可存取的患者資料，EHR 使醫療保健專業人員能夠利用先進的分析技術來改善患者照護和業務效率。根據美國衛生與公眾服務部的資料，超過 85%的醫生診所已經實施了 EHR 系統，促進了醫療服務提供者之間患者資訊的無縫交換，提高了護理協調性和品質。由於 EHR 匯總了大量臨床資料，它們支持開發預測分析模型，該模型可以預測健康結果並識別高風險族群，最終推動更好的臨床決策。

此外，研究表明，實施 EHR 可以減少用藥錯誤並提高對臨床指南的遵守率，進一步凸顯了資料分析在增強醫療保健服務方面的作用。因此，EHR 和巨量資料分析的整合被認為是推動個人化醫療和改善整體健康結果的關鍵因素。

在醫療保健領域實施巨量資料分析包括哪些關鍵的資料隱私和安全問題？

醫療保健領域巨量資料分析的實施與幾個重大的資料隱私和安全問題有關。由於醫療資料的高度機密性，未授權存取或洩漏的風險很高，有報告顯示，大約 93%的醫療機構都經歷過資料外洩。這令人震驚的統計資料凸顯了數位化的環境中患者資訊的脆弱性。

電子健康記錄（EHR）的廣泛使用往往會加劇隱私侵犯，因為電子健康記錄促進了大量個人健康資訊的收集和共用。

據美國衛生與公眾服務部表示，HIPAA 等法規目的是保護患者隱私，但仍存在漏洞，尤其是在未經患者充分同意的情況下共用大量資料集時。因此，患者和醫療保健提供者普遍擔心資料外洩可能造成的歧視、身分盜竊和情緒困擾。因此，強調需要先進的加密技術和強力的管治實踐來減輕這些風險，同時確保遵守法律規範。

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.

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