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市场调查报告书

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1606548

全球大数据和分析医疗保健市场 - 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个工作天内

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简介目录

概述

全球巨量资料和分析医疗保健市场在 2023 年达到 336 亿美元，预计到 2031 年将达到 1109.9 亿美元，2024-2031 年预测期间复合年增长率为 16.2%。

巨量资料和分析医疗保健是指对医疗保健系统、设备和患者生成的大型且多样化的数据集进行系统收集、整合和分析，以改善临床和营运决策。该领域利用机器学习、人工智慧 (AI) 和预测建模等先进技术来得出可行的见解，旨在加强患者护理、优化医疗保健营运和推动医疗创新。巨量资料分析透过实现精准医疗、降低成本、改善患者治疗结果和应对全球健康挑战（例如慢性病管理和大流行期间的资源分配）来改变医疗保健。

在技术进步、电子健康记录 (EHR) 越来越多的采用以及对更高效的医疗保健管理的需求的推动下，巨量资料和分析医疗保健市场的需求正在快速增长。例如，根据美国国立卫生研究院的数据，巨量资料增加了 80%，这归因于云端资源、巨量资料分析、行动技术和社群媒体技术。这种成长反映出人们越来越依赖分析来改善患者治疗结果、降低成本和优化医疗保健环境中的营运效率。

市场动态：

驱动程式和限制

医疗资料量不断增加

医疗保健资料量的不断增长极大地推动了巨量资料和分析医疗保健市场的成长，预计将在预测期内推动该市场的发展。随着医疗保健系统数位化并采用更先进的技术，各个平台产生的资料量激增。不断增长的资料量对复杂的分析工具产生了巨大的需求，这些工具能够提取有价值的见解，以改善患者护理、优化营运并降低成本。例如，根据特拉华大学的数据，美国医院协会在 2020 年的报告中指出，医疗保健领域每年产生约 2,314 艾字节的资料。

电子病历的全球采用已成为资料成长的关键因素。据国家卫生资讯科技协调员称，截至 2021 年，近十分之九 (88%) 的美国基层开业医师采用了电子健康记录 (EHR)，近五分之四 (78%) 已采用经过认证的 HER ，导致以数位方式储存和存取的患者资料大量增加。这些资料（包括患者病史、诊断、治疗和药物）是大数据分析工具的基础，可帮助医疗保健提供者提供个人化护理并改善临床结果。

此外，数据驱动的见解对于提高医疗保健效率至关重要。预测分析依赖大型资料集，可以预测患者入院情况、防止再入院并优化资源分配。例如，再入院对医疗保健系统来说是一笔巨大的成本。巨量资料工具被用来透过识别高风险患者的预测模型来减少再入院率。

资料管理的复杂性

由于处理、整合和分析来自多个来源的大量多样化资料集的挑战，资料管理的复杂性极大地阻碍了巨量资料和分析医疗保健市场的成长。这种复杂性导致效率低下、资料孤岛和成本增加，减缓了市场采用速度。

医疗保健资料是由电子病历、穿戴式装置、医学影像和物联网装置产生的，但整合结构化和非结构化资料仍然是一项重大挑战。例如，根据美国国立卫生研究院 (NIH) 的数据，医疗保健领域超过 80% 的数字资料都是非结构化资料，需要新形式的资料处理和标准化，这对健康研究人员来说是一项挑战。这限制了可操作的见解并延迟了决策。

由于美国的 HIPAA 和欧洲的 GDPR 等法规，医疗保健组织优先考虑病患资料的安全，这使得资料共享和管理变得更加复杂。违规行为进一步削弱信任，阻碍组织充分采用分析工具。

例如，根据 HIPAA 杂誌报导，2023 年 8 月，发现有 2,300 万笔医疗记录被洩露。在过去 12 个月中，平均每月有 9,989,003 笔医疗记录被洩露。此外，科罗拉多州的一家病理实验室正在通知超过180 万名患者，他们的敏感资讯遭到洩露，这是医学检测实验室向美国联邦监管机构报告的最大违规行为之一，这使得医疗保健行业特别容易受到骇客的攻击。

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)