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市場調查報告書

商品編碼

1606548

全球巨量資料和分析醫療保健市場 - 2024-2031

Global Big Data & Analytics Healthcare Market - 2024-2031

出版日期: 2024年12月04日 | 出版商:

DataM Intelligence | 英文 176 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

概述

全球巨量資料和分析醫療保健市場在 2023 年達到 336 億美元，預計到 2031 年將達到 1109.9 億美元，2024-2031 年預測期間複合年成長率為 16.2%。

巨量資料和分析醫療保健是指對醫療保健系統、設備和患者生成的大型且多樣化的資料集進行系統收集、整合和分析，以改善臨床和營運決策。該領域利用機器學習、人工智慧 (AI) 和預測建模等先進技術來得出可行的見解，旨在加強患者護理、最佳化醫療保健營運和推動醫療創新。巨量資料分析透過實現精準醫療、降低成本、改善患者治療結果和應對全球健康挑戰（例如慢性病管理和大流行期間的資源分配）來改變醫療保健。

在技術進步、電子健康記錄 (EHR) 越來越多的採用以及對更高效的醫療保健管理的需求的推動下，巨量資料和分析醫療保健市場的需求正在快速成長。例如，根據美國國立衛生研究院的數據，巨量資料增加了 80%，這歸因於雲端資源、巨量資料分析、行動技術和社群媒體技術。這種成長反映出人們越來越依賴分析來改善患者治療結果、降低成本和最佳化醫療保健環境中的營運效率。

市場動態：

驅動程式和限制

醫療資料量不斷增加

醫療保健資料量的不斷成長極大地推動了巨量資料和分析醫療保健市場的成長，預計將在預測期內推動該市場的發展。隨著醫療保健系統數位化並採用更先進的技術，各個平台產生的資料量激增。不斷成長的資料量對複雜的分析工具產生了巨大的需求，這些工具能夠提取有價值的見解，以改善患者護理、最佳化營運並降低成本。例如，根據特拉華大學的數據，美國醫院協會在 2020 年的報告中指出，醫療保健領域每年產生約 2,314 艾字節的資料。

電子病歷的全球採用已成為資料成長的關鍵因素。據國家衛生資訊科技協調員稱，截至 2021 年，近十分之九 (88%) 的美國基層開業醫師採用了電子健康記錄 (EHR)，近五分之四 (78%) 已採用經過認證的 HER ，導致以數位方式儲存和存取的患者資料大量增加。這些資料（包括患者病史、診斷、治療和藥物）是巨量資料分析工具的基礎，可幫助醫療保健提供者提供個人化護理並改善臨床結果。

此外，數據驅動的見解對於提高醫療保健效率至關重要。預測分析依賴大型資料集，可以預測患者入院情況、防止再入院並最佳化資源分配。例如，再入院對醫療保健系統來說是一筆巨大的成本。巨量資料工具被用來透過識別高風險患者的預測模型來減少再入院率。

資料管理的複雜性

由於處理、整合和分析來自多個來源的大量多樣化資料集的挑戰，資料管理的複雜性極大地阻礙了巨量資料和分析醫療保健市場的成長。這種複雜性導致效率低下、資料孤島和成本增加，減緩了市場採用速度。

醫療保健資料是由電子病歷、穿戴式裝置、醫學影像和物聯網裝置產生的，但整合結構化和非結構化資料仍然是一項重大挑戰。例如，根據美國國立衛生研究院 (NIH) 的數據，醫療保健領域超過 80% 的數字資料都是非結構化資料，需要新形式的資料處理和標準化，這對健康研究人員來說是一項挑戰。這限制了可操作的見解並延遲了決策。

由於美國的 HIPAA 和歐洲的 GDPR 等法規，醫療保健組織優先考慮病患資料的安全，這使得資料共享和管理變得更加複雜。違規行為進一步削弱信任，阻礙組織充分採用分析工具。

例如，根據 HIPAA 雜誌報導，2023 年 8 月，發現有 2,300 萬筆醫療記錄被洩露。在過去 12 個月中，平均每月有 9,989,003 筆醫療記錄被洩露。此外，科羅拉多州的一家病理實驗室正在通知超過180 萬名患者，他們的敏感資訊遭到洩露，這是醫學檢測實驗室向美國聯邦監管機構報告的最大違規行為之一，這使得醫療保健行業特別容易受到駭客的攻擊。

Big data & analytics healthcare refers to the systematic collection, integration and analysis of large and diverse datasets generated by healthcare systems, devices and patients to improve clinical and operational decision-making. This field leverages advanced technologies, such as machine learning, artificial intelligence (AI) and predictive modeling, to derive actionable insights aimed at enhancing patient care, optimizing healthcare operations and driving medical innovation. Big data analytics transforms healthcare by enabling precision medicine, reducing costs, improving patient outcomes and addressing global health challenges, such as chronic disease management and resource allocation during pandemics.

The demand for big data and analytics healthcare market is growing rapidly, driven by advancements in technology, increasing adoption of electronic health records (EHRs) and the need for more efficient healthcare management. For instance, according to the National Institute of Health, it was recorded that an 80% increase in big data is due to cloud sources, big data analytics, mobile technology and social media technologies. This growth reflects the rising reliance on analytics for improving patient outcomes, reducing costs and optimizing operational efficiency in healthcare settings.

Market Dynamics: Drivers & Restraints

Rising volume of healthcare data

The rising volume of healthcare data is significantly driving the growth of the big data & analytics healthcare market and is expected to drive the market over the forecast period. As healthcare systems digitize and adopt more advanced technologies, the amount of data generated across various platforms has surged. This growing volume of data creates a significant demand for sophisticated analytics tools capable of extracting valuable insights to improve patient care, optimize operations and reduce costs. For instance, according to the University of Delaware, in a 2020 report, the American Hospital Association noted that the healthcare field generates approximately 2,314 exabytes of data annually.

The global adoption of EHRs has become a key contributor to data growth. According to the National Coordinator for Health Information Technology, as of 2021, nearly 9 in 10 (88%) of U.S. office-based physicians adopted any electronic health record (EHR) and nearly 4 in 5 (78%) had adopted a certified HER, leading to a massive increase in patient data being stored and accessed digitally. This data, including patient history, diagnoses, treatments and medications, serves as the foundation for Big Data analytics tools, which help healthcare providers to deliver personalized care and improve clinical outcomes.

Additionally, data-driven insights are critical for improving healthcare efficiency. Predictive analytics, which relies on large datasets, can forecast patient admissions, prevent readmissions and optimize resource allocation. For instance, hospital readmissions are a significant cost to the healthcare system. Big data tools are being employed to reduce these readmissions through predictive models that identify high-risk patients.

Complexity of data management

The complexity of data management significantly hampers the growth of the big data & analytics healthcare market due to challenges in handling, integrating and analyzing vast, diverse datasets from multiple sources. This complexity leads to inefficiencies, data silos and increased costs, slowing market adoption.

Healthcare data is generated from EHRs, wearables, medical imaging and IoT devices, but integrating structured and unstructured data remains a significant challenge. For instance, according to the National Institute of Health (NIH), over 80% of digital data in healthcare is available as unstructured data, requiring new forms of data processing and standardizing that prove challenging to health researchers. This limits actionable insights and delays decision-making.

Healthcare organizations prioritize patient data security due to regulations like HIPAA in the U.S. and GDPR in Europe, making data sharing and management more complex. Breaches further erode trust, discouraging organizations from fully adopting analytics tools.

For instance, according to the HIPAA Journal, in August 2023, 23 million breached healthcare records are noticed. Over the past 12 months, an average of 9,989,003 healthcare records were breached each month. Additionally, a Colorado-based pathology laboratory is notifying more than 1.8 million patients that their sensitive information was compromised one of the largest breaches reported by a medical testing lab to US federal regulators, making the healthcare industry especially vulnerable to hackers.

Segment Analysis

The global big data & analytics healthcare market is segmented based on component, analytics type, deployment mode, application, end-user and region.

Analytics Type:

The predictive analytics segment is expected to dominate the global big data & analytics healthcare market share

The predictive analytics segment is expected to dominate the big data & analytics healthcare market share over the forecast period due to its transformative ability to anticipate future trends, risks and health outcomes. Predictive analytics uses historical and real-time data combined with machine learning algorithms to forecast potential health events, improve patient care, optimize operations and reduce costs.

For instance, in October 2024, Clarify Health launched the industry's first AI-powered predictive analytics, Clarify Performance IQ Suite, that spans cost, quality and utilization assessment to deliver opportunity analytics. Leveraging advanced machine learning and natural language processing, the Performance IQ Suite empowers health plans and others with unparalleled insights to contain costs, improve care quality and gain a competitive edge.

Predicting readmissions is one of the most common applications. Hospitals use predictive models to assess the likelihood of a patient being readmitted within 30 days of discharge. These models use factors like age, medical history and current health status to predict readmission risks. For instance, Corewell Health care coordinators shared that a recent initiative, which uses predictive analytics to forecast risk and reduce readmissions, has kept 200 patients from being readmitted and resulted in a $5 million cost savings.

North America is expected to hold a significant position in the global Big Data & Analytics healthcare market

North America region is expected to hold the largest market share over the forecast period. North America, especially the United States boasts one of the most sophisticated healthcare systems in the world, with widespread adoption of Electronic Health Records (EHRs), telemedicine and health data management systems. For instance, according to Oxford Academic, the study found that basic EHR adoption in the US surged from 6.6% to 81.2, creating a vast pool of structured and unstructured healthcare data that drives demand for analytics tools.

North America is home to many of the world's leading technology companies offering big data & analytics solutions in healthcare. Key players like IBM Watson Health and other local key players in the United States have been at the forefront of developing analytics tools for healthcare.

For instance, in November 2023, Cercle.ai, Inc., a new AI company focused on advancing healthcare for women, launched out of stealth. Leveraging AI, the Cercle Biomedical Graph platform collects billions of de-identified biomedical and genomics data points drawn securely from healthcare clinics and research labs around the world. It then converts often unstructured, fragmented clinical data into insights for researchers and providers.

Asia Pacific is growing at the fastest pace in the Big Data & Analytics healthcare market

The Asia Pacific region is experiencing the fastest growth in the big data & analytics healthcare market. Many Asia Pacific countries are undergoing a digital transformation in healthcare, with governments pushing for digitization of healthcare records, telemedicine adoption and smart health initiatives. Countries like China, India and Singapore have implemented national strategies to boost healthcare IT infrastructure and integrate advanced technologies, including big data analytics.

For instance, in China, the government's Healthy China 2030 initiative is driving the use of health data analytics, including the integration of electronic health records (EHRs) and wearable devices across hospitals.

The APAC region is seeing an expansion in healthcare IT infrastructure, including the adoption of cloud computing, AI, machine learning and IoT devices. These technologies generate large volumes of data that can be analyzed to improve healthcare services.

For instance, in January 2024, GenepoweRx launched an AI platform GeneConnectRx, for big data analytics and drug discovery. This revolutionary step in personalized medicine marks a paradigm shift, empowering healthcare providers to customize treatments based on individual genetic makeup. GeneConnectRx integrates internal data, global resources, and cutting-edge models to forecast potential molecules for revolutionary drug discovery.

Competitive Landscape

The major global players in the big data & analytics healthcare market include IBM, Koninklijke Philips N.V., Optum, Inc., FLATIRON HEALTH, Health Catalyst, Microsoft, Oracle, Google, Wipro, Cisco Systems, Inc. and among others.

Why Purchase the Report?

Pipeline & Innovations: Reviews ongoing clinical trials, product pipelines, and forecasts upcoming advancements in medical devices and pharmaceuticals.
Product Performance & Market Positioning: Analyzes product performance, market positioning, and growth potential to optimize strategies.
Real-World Evidence: Integrates patient feedback and data into product development for improved outcomes.
Physician Preferences & Health System Impact: Examines healthcare provider behaviors and the impact of health system mergers on adoption strategies.
Market Updates & Industry Changes: Covers recent regulatory changes, new policies, and emerging technologies.
Competitive Strategies: Analyzes competitor strategies, market share, and emerging players.
Pricing & Market Access: Reviews pricing models, reimbursement trends, and market access strategies.
Market Entry & Expansion: Identifies optimal strategies for entering new markets and partnerships.
Regional Growth & Investment: Highlights high-growth regions and investment opportunities.
Supply Chain Optimization: Assesses supply chain risks and distribution strategies for efficient product delivery.
Sustainability & Regulatory Impact: Focuses on eco-friendly practices and evolving regulations in healthcare.
Post-market Surveillance: Uses post-market data to enhance product safety and access.
Pharmacoeconomics & Value-Based Pricing: Analyzes the shift to value-based pricing and data-driven decision-making in R&D.

The global big data & analytics healthcare market report delivers a detailed analysis with 60+ key tables, more than 50 visually impactful figures, and 176 pages of expert insights, providing a complete view of the market landscape.

Target Audience 2023

Manufacturers: Pharmaceutical, Medical Device, Biotech Companies, Contract Manufacturers, Distributors, Hospitals.
Regulatory & Policy: Compliance Officers, Government, Health Economists, Market Access Specialists
Technology & Innovation: AI/Robotics Providers, R&D Professionals, Clinical Trial Managers, Pharmacovigilance Experts.
Investors: Healthcare Investors, Venture Fund Investors, Pharma Marketing & Sales.
Consulting & Advisory: Healthcare Consultants, Industry Associations, Analysts.
Supply Chain: Distribution and Supply Chain Managers.
Consumers & Advocacy: Patients, Advocacy Groups, Insurance Companies.
Academic & Research: Academic Institutions.

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Component
3.2. Snippet by Analytics Type
3.3. Snippet by Deployment Mode
3.4. Snippet by Application
3.5. Snippet by End-User
3.6. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Rising Volume of Healthcare Data
  - 4.1.1.2. Shift Towards Value-Based Care
- 4.1.2. Restraints
  - 4.1.2.1. Complexity of Data Management
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Patent Analysis
5.5. Regulatory Analysis
5.6. SWOT Analysis
5.7. Unmet Needs

6. By Component

6.1. Introduction
- 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 6.1.2. Market Attractiveness Index, By Component
6.2. Software*
- 6.2.1. Introduction
- 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
6.3. Hardware
6.4. Services

7. By Analytics Type

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Analytics Type
- 7.1.2. Market Attractiveness Index, By Analytics Type
7.2. Predictive Analytics*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Descriptive Analytics
7.4. Diagnostic Analytics
7.5. Prescriptive Analytics
7.6. Others

8. By Deployment Mode

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 8.1.2. Market Attractiveness Index, By Deployment Mode
8.2. On-Premises*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Cloud-Based

9. By Application

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 9.1.2. Market Attractiveness Index, By Application
9.2. Clinical Analytics*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Financial Analytics
9.4. Operational Analytics
9.5. Fraud Detection and Risk Management
9.6. Others

10. By End-User

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.1.2. Market Attractiveness Index, By End-User
10.2. Pharmaceutical and Biotechnology Companies*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Hospitals and Clinics
10.4. Finance and Insurance Agencies
10.5. Research Organizations

11. By Region

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 11.1.2. Market Attractiveness Index, By Region
11.2. North America
- 11.2.1. Introduction
- 11.2.2. Key Region-Specific Dynamics
- 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Analytics Type
- 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.2.8.1. U.S.
  - 11.2.8.2. Canada
  - 11.2.8.3. Mexico
11.3. Europe
- 11.3.1. Introduction
- 11.3.2. Key Region-Specific Dynamics
- 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Analytics Type
- 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.3.8.1. Germany
  - 11.3.8.2. U.K.
  - 11.3.8.3. France
  - 11.3.8.4. Spain
  - 11.3.8.5. Italy
  - 11.3.8.6. Rest of Europe
11.4. South America
- 11.4.1. Introduction
- 11.4.2. Key Region-Specific Dynamics
- 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Analytics Type
- 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.4.8.1. Brazil
  - 11.4.8.2. Argentina
  - 11.4.8.3. Rest of South America
11.5. Asia-Pacific
- 11.5.1. Introduction
- 11.5.2. Key Region-Specific Dynamics
- 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Analytics Type
- 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.5.8.1. China
  - 11.5.8.2. India
  - 11.5.8.3. Japan
  - 11.5.8.4. South Korea
  - 11.5.8.5. Rest of Asia-Pacific
11.6. Middle East and Africa
- 11.6.1. Introduction
- 11.6.2. Key Region-Specific Dynamics
- 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Analytics Type
- 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

12. Competitive Landscape

12.1. Competitive Scenario
12.2. Market Positioning/Share Analysis
12.3. Mergers and Acquisitions Analysis

13. Company Profiles

13.1. IBM*
- 13.1.1. Company Overview
- 13.1.2. Product Portfolio and Description
- 13.1.3. Financial Overview
- 13.1.4. Key Developments
13.2. Koninklijke Philips N.V.
13.3. Optum, Inc.
13.4. FLATIRON HEALTH
13.5. Health Catalyst
13.6. Microsoft
13.7. Oracle
13.8. Google
13.9. Wipro
13.10. Cisco Systems, Inc. (LIST NOT EXHAUSTIVE)