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醫療保健巨量資料的全球市場 (2018年～2028年):各組件、用途、部署、分析類型、工具類型、終端用戶、地區的趨勢分析、競爭市場佔有率、預測
市場調查報告書
商品編碼
1134233

醫療保健巨量資料的全球市場 (2018年～2028年):各組件、用途、部署、分析類型、工具類型、終端用戶、地區的趨勢分析、競爭市場佔有率、預測

Global Big Data in Healthcare Market, By Components, By Application, By Deployment, By Analytics Type, By Tool Type, By End-User, By Region Trend Analysis, Competitive Market Share & Forecast, 2018-2028
出版日期: | 出版商: Blueweave Consulting | 英文 114 Pages | 商品交期: 2-3個工作天內

價格
簡介目錄

全球醫療保健巨量資料的市場規模，從2021年的281億7,000萬美元，在預測期間中預計以15.9％的年複合成長率發展，2028年終成長到約785億2,000萬美元的規模。

新興經濟各國中政府的配合措施，網際網路普及率上升，電子商務的普及率上升，行動裝置的普及率上升等對全球醫療保健巨量資料市場成長有貢獻。

本報告提供全球醫療保健巨量資料的市場調查，市場概要，市場成長的各種影響因素分析，技術趨勢，法律制度，市場規模的變化、預測，各種區分、各地區/主要國家的明細，競爭環境，主要企業簡介等彙整資訊。

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目錄

第1章 調查架構

第2章 摘要整理

第3章 全球醫療保健巨量資料市場洞察

  • 產業價值鏈分析
  • DROC分析
    • 促進因素
    • 阻礙因素
    • 機會
    • 課題
  • 技術的進步/最近的開發
  • 法律規範
  • 波特的五力分析

第4章 全球醫療保健巨量資料市場:概要

  • 市場規模、預測
  • 市場佔有率、預測
    • 各零件
      • 服務
      • 軟體
    • 各用途
      • 臨床分析
      • 財務分析
      • 運用分析
    • 各部署
      • 雲端
      • 內部部署
    • 各分析類型
      • 說明的分析
      • 診斷性分析
      • 預測的分析
      • 處方的分析
    • 各工具類型
      • 資料倉儲分析
      • 財務分析
      • 生產匯報
      • CRM分析
      • 預測的分析
      • 視覺化分析及工具
      • 風險管理分析
      • 供應鏈分析
      • 試驗分析
    • 各終端用戶
      • 金融、保險代理店
      • 醫院、診療所
      • 研究機關
    • 各地區
      • 北美
      • 歐洲
      • 亞太地區
      • 南美
      • 中東、非洲

第5章 北美的醫療保健巨量資料市場

  • 市場規模、預測
  • 市場佔有率、預測
    • 各零件
    • 各用途
    • 各部署
    • 各分析類型
    • 各工具類型
    • 各終端用戶
    • 各國

第6章 歐洲的醫療保健巨量資料市場

  • 市場規模、預測
  • 市場佔有率、預測
    • 各零件
    • 各用途
    • 各部署
    • 各分析類型
    • 各工具類型
    • 各終端用戶
    • 各國

第7章 亞太地區的醫療保健巨量資料市場

  • 市場規模、預測
  • 市場佔有率、預測
    • 各零件
    • 各用途
    • 各部署
    • 各分析類型
    • 各工具類型
    • 各終端用戶
    • 各國

第8章 南美的醫療保健巨量資料市場

  • 市場規模、預測
  • 市場佔有率、預測
    • 各零件
    • 各用途
    • 各部署
    • 各分析類型
    • 各工具類型
    • 各終端用戶
    • 各國

第9章 中東、非洲的醫療保健巨量資料市場

  • 市場規模、預測
  • 市場佔有率、預測
    • 各零件
    • 各用途
    • 各部署
    • 各分析類型
    • 各工具類型
    • 各終端用戶
    • 各國

第10章 競爭情形

  • 主要企業、產品的清單
  • 企業佔有率分析
  • 競爭基準:各經營參數
  • 主要的策略性展開 (M&A、聯盟等)

第11章 COVID-19:對全球醫療保健巨量資料市場的影響

第12章 企業簡介 (公司概要、財務矩陣、競爭情形、主要人力資源、主要的競爭對手、聯絡處、策略性展望)

  • Allscripts Healthcare Solutions, Inc.
  • Cerner
  • Cognizant
  • Dell EMC
  • Epic System Corporation
  • Ge Healthcare
  • General Electric Healthcare
  • Hewlett Packard Enterprise (HPE)
  • International Business Machines (IBM) Corporation
  • Mckesson
  • Microsoft
  • Optum
  • Oracle Corporation
  • Philips Healthcare
  • Siemens
  • 其他的主要企業

第13章 主要策略性建議

第14章 調查手法

簡介目錄
Product Code: BWC22387

Global Big Data in Healthcare Market to Grow at a CAGR of 15.9%, during Forecast Period

Global Big Data in Healthcare Market is flourishing owing to the government initiatives in developing economies, rising internet penetration, rising e-commerce penetration, and rising availability of mobile devices all contributed to growth.

A recent study conducted by the strategic consulting and market research firm, BlueWeave Consulting, revealed that the Global Big Data in Healthcare Market was worth USD 28.17 billion in the year 2021. The market is projected to grow at a CAGR of 15.9%, earning revenues of around USD 78.52 billion by the end of 2028. The Global Big Data in Healthcare Market is booming because of the growing requirement for analysis, integration, and management of enormous volumes of data that propel big data analytics in the healthcare sector. These data are mostly gathered from different electronic health records (EHR) and patient biological information. A set of statistical algorithms and predictive models supported by high-performance analytics platforms make up big data analytics, a subset of advanced analytics. These technologies, which use powerful computing systems, provide the healthcare sector with several commercial advantages including successful marketing, new income prospects, greater operational efficiency, and better patient care. Currently, big data analytics is being adopted by a wide range of healthcare organizations, including multi-provider groups, single-physician practices, and huge hospital networks. This increase can be ascribed to the service's many benefits, which include its swift and effective detection of healthcare fraud as well as its analysis of patient information and clinical studies.

Demand for Analytics Solutions for Population Health Management is Increasing

Population health management necessitates the integration of clinical and claims data on the same platform for data analysis, resulting in complete patient care with cost-effective prescription practices. Increased demand for improved care management, early disease prediction, and hospitalization process is expected to boost the growth of the worldwide big data analytics in healthcare market in the future. Furthermore, citizen health is critical for healthcare management in the healthcare business, which necessitates predictive analysis of population health and is likely to increase big data analytics applications during the projection period.

Rise in Artificial Intelligence Demand Needs Powerful Analytics Solutions as it Builds Big Data

For several reasons, the market for big data and analytics has seen an increase in the demand for artificial intelligence (AI). AI uses natural language processing to recognize knowledge, identify different types of data, and discover potential connections between datasets. Additionally, the technology can be used to speed up and automate data preparation tasks, such as creating data models, and support data exploration for decision-making. The combination of AI and big data, according to research from 2020 Forbes, can automate close to 80% of all physical work, 70% of data processing work, and 64% of data collection tasks. By utilizing all available data, deep learning is aided by the combination of AI, big data, and analytics.

Challenge: Technical Challenges

Despite the advantages and promising future of big data in healthcare, numerous fundamental challenges remain. The technological expertise needed and the ability to ensure compliance with all security measures surrounding it are the two key barriers impeding the use of big data in healthcare. Big data necessitates particular skill sets due to the complexity of the files. Hospitals will need to seek the aid of data scientists to manipulate information in a big data environment since IT professionals who are accustomed to SQL programming and traditional databases are not prepared for the learning curve. Another significant barrier to the use of big data in healthcare is security. Big data storage is well known for luring hackers and other highly persistent dangers (APTs). Even though the majority of firms have security safeguards in place, regulations like HIPAA compliance must be protected.

Segmental Coverage

Global Big Data in Healthcare Market - By End-User

Based on end-user, the Global Big Data in Healthcare Market is segmented into Finance & Insurance Agencies, Hospitals & Clinics, and Research Organizations. Hospitals and clinic providers experienced the fastest growth. This is because hospitals and healthcare professionals were under a great deal of pressure to provide cost-effective care and better patient management during and after the pandemic, which contributed to the expansion of this end-user category. The need to maintain patient information, keep track of diseases, and provide patients with cost-effective care have all played a significant role in the widespread adoption of healthcare analytics and are expected to continue to do so.

Impact of COVID-19 on Global Big Data in Healthcare Market

The pandemic of COVID-19 had a beneficial impact on the global market for big data analytics in healthcare. This is owing to the critical need for cutting-edge technological improvements in public health, medicine, and wellness. Developments in genomes, epigenomics, transcriptomics, proteomics, metabolomics, and pharmacogenomics have supported the astounding rate of medical data collection. These data contain a plethora of information that can assist us in better understanding patient care. The growing role of big data analytics in developing predictive care models for the healthcare sector is likely to pave the way for exciting new commercial prospects.

Competitive Landscape

The leading market players in the Global Big Data in Healthcare Market are Allscripts Healthcare Solutions, Inc., Cerner, Cognizant, Dell EMC, Epic System Corporation, Ge Healthcare, General Electric Healthcare, Hewlett Packard Enterprise (HPE), International Business Machines (IBM) Corporation, Mckesson, Microsoft, Optum, Oracle Corporation, Philips Healthcare, Siemens, and other prominent players. The Global Big Data in Healthcare Market is highly fragmented with the presence of several manufacturing companies in the country. The market leaders retain their supremacy by spending on research and development, incorporating cutting-edge technology into their goods, and releasing upgraded items for customers. Various tactics, including strategic alliances, agreements, mergers, and partnerships, are used.

The in-depth analysis of the report provides information about growth potential, upcoming trends, and statistics of the Global Big Data in Healthcare Market. It also highlights the factors driving forecasts of total market size. The report promises to provide recent technology trends in the Global Big Data in Healthcare Market and industry insights to help decision-makers make sound strategic decisions. Furthermore, the report also analyzes the growth drivers, challenges, and competitive dynamics of the market.

Table of Contents

1. Research Framework

  • 1.1. Research Objective
  • 1.2. Type Overview
  • 1.3. Market Segmentation

2. Executive Summary

3. Global Big Data in Healthcare Market Insights

  • 3.1. Industry Value Chain Analysis
  • 3.2. DROC Analysis
    • 3.2.1. Growth Drivers
      • 3.2.1.1. Growing Importance of Digital Healthcare and Interoperability
      • 3.2.1.2. Increased use of wearables, mobile health, and the Internet of Medical Things (IoMT)
      • 3.2.1.3. Initiative dedicated to digitizing healthcare
      • 3.2.1.4. Urgent Need to Reduce Medical Expenses
    • 3.2.2. Restraints
      • 3.2.2.1. Security Concerns Regarding Sensitive Patient Medical Data
      • 3.2.2.2. Lack of competent and knowledgeable staff
    • 3.2.3. Opportunities
      • 3.2.3.1. Blockchain for health information exchange
      • 3.2.3.2. Achieve full medical data interoperability to support clinical decision making
    • 3.2.4. Challenges
      • 3.2.4.1. Data security concerns
      • 3.2.4.2. Presence of large amounts of unstructured data
  • 3.3. Deployment Advancements/Recent Developments
  • 3.4. Regulatory Framework
  • 3.5. Porter's Five Forces Analysis
    • 3.5.1. Bargaining Power of Suppliers
    • 3.5.2. Bargaining Power of Buyers
    • 3.5.3. Threat of New Entrants
    • 3.5.4. Threat of Substitutes
    • 3.5.5. Intensity of Rivalry

4. Global Big Data in Healthcare Market Overview

  • 4.1. Market Size & Forecast, by Value, 2018-2028
    • 4.1.1. By Value (USD Billion)
  • 4.2. Market Share & Forecast
    • 4.2.1. By Components
      • 4.2.1.1. Services
      • 4.2.1.2. Software
    • 4.2.2. By Application
      • 4.2.2.1. Clinical Analytics
      • 4.2.2.2. Financial Analytics
      • 4.2.2.3. Operational Analytics
    • 4.2.3. By Deployment
      • 4.2.3.1. Cloud
      • 4.2.3.2. On-Premises
    • 4.2.4. By Analytics Type
      • 4.2.4.1. Descriptive Analytics
      • 4.2.4.2. Diagnostic Analytics
      • 4.2.4.3. Predictive Analytics
      • 4.2.4.4. Prescriptive Analytics
    • 4.2.5. By Tool Type
      • 4.2.5.1. Data Warehouse Analytics
      • 4.2.5.2. Financial Analytics
      • 4.2.5.3. Production Reporting
      • 4.2.5.4. CRM Analytics
      • 4.2.5.5. Predictive Analytics
      • 4.2.5.6. Visual Analytics
      • 4.2.5.7. Risk Management Analytics
      • 4.2.5.8. Supply chain Analytics
      • 4.2.5.9. Test Analytics
    • 4.2.6. By End-User
      • 4.2.6.1. Finance & Insurance Agencies
      • 4.2.6.2. Hospitals & Clinics
      • 4.2.6.3. Research Organizations
    • 4.2.7. By Region
      • 4.2.7.1. North America
      • 4.2.7.2. Europe
      • 4.2.7.3. Asia Pacific (APAC)
      • 4.2.7.4. Latin America
      • 4.2.7.5. Middle East and Africa (MEA)

5. North America Big Data in Healthcare Market

    • 5.1.1. Market Size & Forecast by Value, 2018-2028
    • 5.1.2. By Value (USD Billion)
  • 5.2. Market Share & Forecast
    • 5.2.1. By Components
    • 5.2.2. By Application
    • 5.2.3. By Deployment
    • 5.2.4. By Analytics Type
    • 5.2.5. By Tool Type
    • 5.2.6. By End-User
    • 5.2.7. By Country
      • 5.2.7.1. United States
      • 5.2.7.1.1. By Components
      • 5.2.7.1.2. By Application
      • 5.2.7.1.3. By Deployment
      • 5.2.7.1.4. By Analytics Type
      • 5.2.7.1.5. By Tool Type
      • 5.2.7.1.6. By End-User
      • 5.2.7.2. Canada
      • 5.2.7.2.1. By Components
      • 5.2.7.2.2. By Application
      • 5.2.7.2.3. By Deployment
      • 5.2.7.2.4. By Analytics Type
      • 5.2.7.2.5. By Tool Type
      • 5.2.7.2.6. By End-User

6. Europe Big Data in Healthcare Market

  • 6.1. Market Size & Forecast by Value, 2018-2028
    • 6.1.1. By Value (USD Billion)
  • 6.2. Market Share & Forecast
    • 6.2.1. By Components
    • 6.2.2. By Application
    • 6.2.3. By Deployment
    • 6.2.4. By Analytics Type
    • 6.2.5. By Tool Type
    • 6.2.6. By End-User
    • 6.2.7. By Country
      • 6.2.7.1. Germany
      • 6.2.7.1.1. By Components
      • 6.2.7.1.2. By Application
      • 6.2.7.1.3. By Deployment
      • 6.2.7.1.4. By Analytics Type
      • 6.2.7.1.5. By Tool Type
      • 6.2.7.1.6. By End-User
      • 6.2.7.2. United Kingdom
      • 6.2.7.2.1. By Components
      • 6.2.7.2.2. By Application
      • 6.2.7.2.3. By Deployment
      • 6.2.7.2.4. By Analytics Type
      • 6.2.7.2.5. By Tool Type
      • 6.2.7.2.6. By End-User
      • 6.2.7.3. Italy
      • 6.2.7.3.1. By Components
      • 6.2.7.3.2. By Application
      • 6.2.7.3.3. By Deployment
      • 6.2.7.3.4. By Analytics Type
      • 6.2.7.3.5. By Tool Type
      • 6.2.7.3.6. By End-User
      • 6.2.7.4. France
      • 6.2.7.4.1. By Components
      • 6.2.7.4.2. By Application
      • 6.2.7.4.3. By Deployment
      • 6.2.7.4.4. By Analytics Type
      • 6.2.7.4.5. By Tool Type
      • 6.2.7.4.6. By End-User
      • 6.2.7.5. Spain
      • 6.2.7.5.1. By Components
      • 6.2.7.5.2. By Application
      • 6.2.7.5.3. By Deployment
      • 6.2.7.5.4. By Analytics Type
      • 6.2.7.5.5. By Tool Type
      • 6.2.7.5.6. By End-User
      • 6.2.7.6. The Netherlands
      • 6.2.7.6.1. By Components
      • 6.2.7.6.2. By Application
      • 6.2.7.6.3. By Deployment
      • 6.2.7.6.4. By Analytics Type
      • 6.2.7.6.5. By Tool Type
      • 6.2.7.6.6. By End-User
      • 6.2.7.7. Belgium
      • 6.2.7.7.1. By Components
      • 6.2.7.7.2. By Application
      • 6.2.7.7.3. By Deployment
      • 6.2.7.7.4. By Analytics Type
      • 6.2.7.7.5. By Tool Type
      • 6.2.7.7.6. By End-User
      • 6.2.7.8. NORDIC Countries
      • 6.2.7.8.1. By Components
      • 6.2.7.8.2. By Application
      • 6.2.7.8.3. By Deployment
      • 6.2.7.8.4. By Analytics Type
      • 6.2.7.8.5. By Tool Type
      • 6.2.7.8.6. By End-User
      • 6.2.7.9. Rest of Europe
      • 6.2.7.9.1. By Components
      • 6.2.7.9.2. By Application
      • 6.2.7.9.3. By Deployment
      • 6.2.7.9.4. By Analytics Type
      • 6.2.7.9.5. By Tool Type
      • 6.2.7.9.6. By End-User

7. Asia Pacific Big Data in Healthcare Market

  • 7.1. Market Size & Forecast by Value, 2018-2028
    • 7.1.1. By Value (USD Billion)
  • 7.2. Market Share & Forecast
    • 7.2.1. By Components
    • 7.2.2. By Application
    • 7.2.3. By Deployment
    • 7.2.4. By Analytics Type
    • 7.2.5. By Tool Type
    • 7.2.6. By End-User
    • 7.2.7. By Country
      • 7.2.7.1. China
      • 7.2.7.1.1. By Components
      • 7.2.7.1.2. By Application
      • 7.2.7.1.3. By Deployment
      • 7.2.7.1.4. By Analytics Type
      • 7.2.7.1.5. By Tool Type
      • 7.2.7.1.6. By End-User
      • 7.2.7.2. India
      • 7.2.7.2.1. By Components
      • 7.2.7.2.2. By Application
      • 7.2.7.2.3. By Deployment
      • 7.2.7.2.4. By Analytics Type
      • 7.2.7.2.5. By Tool Type
      • 7.2.7.2.6. By End-User
      • 7.2.7.3. Japan
      • 7.2.7.3.1. By Components
      • 7.2.7.3.2. By Application
      • 7.2.7.3.3. By Deployment
      • 7.2.7.3.4. By Analytics Type
      • 7.2.7.3.5. By Tool Type
      • 7.2.7.3.6. By End-User
      • 7.2.7.4. South Korea
      • 7.2.7.4.1. By Components
      • 7.2.7.4.2. By Application
      • 7.2.7.4.3. By Deployment
      • 7.2.7.4.4. By Analytics Type
      • 7.2.7.4.5. By Tool Type
      • 7.2.7.4.6. By End-User
      • 7.2.7.5. Australia & New Zealand
      • 7.2.7.5.1. By Components
      • 7.2.7.5.2. By Application
      • 7.2.7.5.3. By Deployment
      • 7.2.7.5.4. By Analytics Type
      • 7.2.7.5.5. By Tool Type
      • 7.2.7.5.6. By End-User
      • 7.2.7.6. Indonesia
      • 7.2.7.6.1. By Components
      • 7.2.7.6.2. By Application
      • 7.2.7.6.3. By Deployment
      • 7.2.7.6.4. By Analytics Type
      • 7.2.7.6.5. By Tool Type
      • 7.2.7.6.6. By End-User
      • 7.2.7.7. Malaysia
      • 7.2.7.7.1. By Components
      • 7.2.7.7.2. By Application
      • 7.2.7.7.3. By Deployment
      • 7.2.7.7.4. By Analytics Type
      • 7.2.7.7.5. By Tool Type
      • 7.2.7.7.6. By End-User
      • 7.2.7.8. Singapore
      • 7.2.7.8.1. By Components
      • 7.2.7.8.2. By Application
      • 7.2.7.8.3. By Deployment
      • 7.2.7.8.4. By Analytics Type
      • 7.2.7.8.5. By Tool Type
      • 7.2.7.8.6. By End-User
      • 7.2.7.9. Philippines
      • 7.2.7.9.1. By Components
      • 7.2.7.9.2. By Application
      • 7.2.7.9.3. By Deployment
      • 7.2.7.9.4. By Analytics Type
      • 7.2.7.9.5. By Tool Type
      • 7.2.7.9.6. By End-User
      • 7.2.7.10. Vietnam
      • 7.2.7.10.1. By Components
      • 7.2.7.10.2. By Application
      • 7.2.7.10.3. By Deployment
      • 7.2.7.10.4. By Analytics Type
      • 7.2.7.10.5. By Tool Type
      • 7.2.7.10.6. By End-User
      • 7.2.7.11. Rest of Asia Pacific
      • 7.2.7.11.1. By Components
      • 7.2.7.11.2. By Application
      • 7.2.7.11.3. By Deployment
      • 7.2.7.11.4. By Analytics Type
      • 7.2.7.11.5. By Tool Type
      • 7.2.7.11.6. By End-User

8. Latin America Big Data in Healthcare Market

  • 8.1. Market Size & Forecast by Value, 2018-2028
    • 8.1.1. By Value (USD Billion)
  • 8.2. Market Share & Forecast
    • 8.2.1. By Components
    • 8.2.2. By Application
    • 8.2.3. By Deployment
    • 8.2.4. By Analytics Type
    • 8.2.5. By Tool Type
    • 8.2.6. By End-User
    • 8.2.7. By Country
      • 8.2.7.1. Brazil
      • 8.2.7.1.1. By Components
      • 8.2.7.1.2. By Application
      • 8.2.7.1.3. By Deployment
      • 8.2.7.1.4. By Analytics Type
      • 8.2.7.1.5. By Tool Type
      • 8.2.7.1.6. By End-User
      • 8.2.7.2. Mexico
      • 8.2.7.2.1. By Components
      • 8.2.7.2.2. By Application
      • 8.2.7.2.3. By Deployment
      • 8.2.7.2.4. By Analytics Type
      • 8.2.7.2.5. By Tool Type
      • 8.2.7.2.6. By End-User
      • 8.2.7.3. Argentina
      • 8.2.7.3.1. By Components
      • 8.2.7.3.2. By Application
      • 8.2.7.3.3. By Deployment
      • 8.2.7.3.4. By Analytics Type
      • 8.2.7.3.5. By Tool Type
      • 8.2.7.3.6. By End-User
      • 8.2.7.4. Peru
      • 8.2.7.4.1. By Components
      • 8.2.7.4.2. By Application
      • 8.2.7.4.3. By Deployment
      • 8.2.7.4.4. By Analytics Type
      • 8.2.7.4.5. By Tool Type
      • 8.2.7.4.6. By End-User
      • 8.2.7.5. Colombia
      • 8.2.7.5.1. By Components
      • 8.2.7.5.2. By Application
      • 8.2.7.5.3. By Deployment
      • 8.2.7.5.4. By Analytics Type
      • 8.2.7.5.5. By Tool Type
      • 8.2.7.5.6. By End-User
      • 8.2.7.6. Rest of Latin America
      • 8.2.7.6.1. By Components
      • 8.2.7.6.2. By Application
      • 8.2.7.6.3. By Deployment
      • 8.2.7.6.4. By Analytics Type
      • 8.2.7.6.5. By Tool Type
      • 8.2.7.6.6. By End-User

9. Middle East & Africa Big Data in Healthcare Market

  • 9.1. Market Size & Forecast by Value, 2018-2028
    • 9.1.1. By Value (USD Billion)
  • 9.2. Market Share & Forecast
    • 9.2.1. By Components
    • 9.2.2. By Application
    • 9.2.3. By Deployment
    • 9.2.4. By Analytics Type
    • 9.2.5. By Tool Type
    • 9.2.6. By End-User
    • 9.2.7. By Country
      • 9.2.7.1. Saudi Arabia
      • 9.2.7.1.1. By Components
      • 9.2.7.1.2. By Application
      • 9.2.7.1.3. By Deployment
      • 9.2.7.1.4. By Analytics Type
      • 9.2.7.1.5. By Tool Type
      • 9.2.7.1.6. By End-User
      • 9.2.7.2. UAE
      • 9.2.7.2.1. By Components
      • 9.2.7.2.2. By Application
      • 9.2.7.2.3. By Deployment
      • 9.2.7.2.4. By Analytics Type
      • 9.2.7.2.5. By Tool Type
      • 9.2.7.2.6. By End-User
      • 9.2.7.3. Qatar
      • 9.2.7.3.1. By Components
      • 9.2.7.3.2. By Application
      • 9.2.7.3.3. By Deployment
      • 9.2.7.3.4. By Analytics Type
      • 9.2.7.3.5. By Tool Type
      • 9.2.7.3.6. By End-User
      • 9.2.7.4. Kuwait
      • 9.2.7.4.1. By Components
      • 9.2.7.4.2. By Application
      • 9.2.7.4.3. By Deployment
      • 9.2.7.4.4. By Analytics Type
      • 9.2.7.4.5. By Tool Type
      • 9.2.7.4.6. By End-User
      • 9.2.7.5. Iran
      • 9.2.7.5.1. By Components
      • 9.2.7.5.2. By Application
      • 9.2.7.5.3. By Deployment
      • 9.2.7.5.4. By Analytics Type
      • 9.2.7.5.5. By Tool Type
      • 9.2.7.5.6. By End-User
      • 9.2.7.6. South Africa
      • 9.2.7.6.1. By Components
      • 9.2.7.6.2. By Application
      • 9.2.7.6.3. By Deployment
      • 9.2.7.6.4. By Analytics Type
      • 9.2.7.6.5. By Tool Type
      • 9.2.7.6.6. By End-User
      • 9.2.7.7. Nigeria
      • 9.2.7.7.1. By Components
      • 9.2.7.7.2. By Application
      • 9.2.7.7.3. By Deployment
      • 9.2.7.7.4. By Analytics Type
      • 9.2.7.7.5. By Tool Type
      • 9.2.7.7.6. By End-User
      • 9.2.7.8. Kenya
      • 9.2.7.8.1. By Components
      • 9.2.7.8.2. By Application
      • 9.2.7.8.3. By Deployment
      • 9.2.7.8.4. By Analytics Type
      • 9.2.7.8.5. By Tool Type
      • 9.2.7.8.6. By End-User
      • 9.2.7.9. Egypt
      • 9.2.7.9.1. By Components
      • 9.2.7.9.2. By Application
      • 9.2.7.9.3. By Deployment
      • 9.2.7.9.4. By Analytics Type
      • 9.2.7.9.5. By Tool Type
      • 9.2.7.9.6. By End-User
      • 9.2.7.10. Morocco
      • 9.2.7.10.1. By Components
      • 9.2.7.10.2. By Application
      • 9.2.7.10.3. By Deployment
      • 9.2.7.10.4. By Analytics Type
      • 9.2.7.10.5. By Tool Type
      • 9.2.7.10.6. By End-User
      • 9.2.7.11. Algeria
      • 9.2.7.11.1. By Components
      • 9.2.7.11.2. By Application
      • 9.2.7.11.3. By Deployment
      • 9.2.7.11.4. By Analytics Type
      • 9.2.7.11.5. By Tool Type
      • 9.2.7.11.6. By End-User
      • 9.2.7.12. Rest of Middle East & Africa
      • 9.2.7.12.1. By Components
      • 9.2.7.12.2. By Application
      • 9.2.7.12.3. By Deployment
      • 9.2.7.12.4. By Analytics Type
      • 9.2.7.12.5. By Tool Type
      • 9.2.7.12.6. By End-User

10. Competitive Landscape

  • 10.1. List of Key Players and Their Analytics Types
  • 10.2. Global Big data in healthcare Company Market Share Analysis, 2021
  • 10.3. Competitive Benchmarking, By Operating Parameters
  • 10.4. Key Strategic Developments (Mergers, Acquisitions, Partnerships)

11. Impact of Covid-19 on Global Big Data in Healthcare Market

12. Company Profile (Company Overview, Financial Matrix, Competitive Landscape, Key Personnel, Key Competitors, Contact Address, Strategic Outlook, SWOT)

  • 12.1. Allscripts Healthcare Solutions, Inc.
  • 12.2. Cerner
  • 12.3. Cognizant
  • 12.4. Dell EMC
  • 12.5. Epic System Corporation
  • 12.6. Ge Healthcare
  • 12.7. General Electric Healthcare
  • 12.8. Hewlett Packard Enterprise (HPE)
  • 12.9. International Business Machines (IBM) Corporation
  • 12.10. Mckesson
  • 12.11. Microsoft
  • 12.12. Optum
  • 12.13. Oracle Corporation
  • 12.14. Philips Healthcare
  • 12.15. Siemens
  • 12.16. Other Prominent Players

13. Key Strategic Recommendations

14. Research Methodology

  • 14.1. Qualitative Research
    • 14.1.1. Primary & Secondary Research
  • 14.2. Quantitative Research
  • 14.3. Market Breakdown & Data Triangulation
    • 14.3.1. Secondary Research
    • 14.3.2. Primary Research
  • 14.4. Breakdown of Primary Research Respondents, By Region
  • 14.5. Assumptions & Limitations