세계의 헬스케어용 빅데이터 시장 : 업계 동향과 예측 - 컴퍼넌트별, 하드웨어 유형별, 소프트웨어 유형별, 서비스 유형별, 전개 옵션별, 응용 분야별, 헬스케어 분야별, 최종 사용자별, 경제 상황별, 지역별, 주요 참가 기업별

GLOBAL BIG DATA IN HEALTHCARE MARKET: OVERVIEW

As per Roots Analysis, the big data in healthcare market is estimated to grow from USD 78 billion in the current year to USD 540 billion by 2035, at a CAGR of 19.20% during the forecast period, till 2035.

The market sizing and opportunity analysis has been segmented across the following parameters:

Component

• Hardware
• Software
• Services

Type of Hardware

• Storage Devices
• Networking Infrastructure
• Servers

Type of Software

• Electronic Health Record
• Practice Management Software
• Revenue Cycle Management Software
• Workforce Management Software

Type of Service

• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics

Deployment Option

• Cloud-based
• On-premises

Application Area

• Clinical Data Management
• Financial Management
• Operational Management
• Population Health Management

Healthcare Vertical

• Healthcare Services
• Medical Devices
• Pharmaceuticals
• Other Verticals

End User

• Clinics
• Health Insurance Agencies
• Hospitals
• Other End Users

Economic Status

• High Income Countries
• Upper-Middle Income Countries
• Lower-Middle Income Countries

Key Geographical Regions

• North America
• Europe
• Asia
• Latin America
• Middle East and North Africa
• Rest of the World

GLOBAL BIG DATA IN HEALTHCARE MARKET: GROWTH AND TRENDS

Big Data in healthcare refers to the vast amount of data that is continuously expanding and cannot be efficiently stored or processed using traditional tools. Notably, over the past few years, the popularity of big data / big data analytics tools and technologies has increased exponentially in healthcare due to the large volumes of data being generated in this domain. Big data in healthcare turns the challenges into opportunities to provide personalized care to the patients by using huge amounts of existing data. Further, big data can be used across different verticals of healthcare industry, such as in population health management, Electronic Health Record (EHR) management, pharmaceutical research, and telemedicine and telehealth.

Owing to the increasing popularity of big data in healthcare domain, there is a huge impact of big data in healthcare market size. Big data analysis is used not only in healthcare market but also used in different sectors for the growth of the organization and to forecast future trends using machine learning and artificial intelligence. Moreover, big data has also had a considerable impact on the financial sector. Big data in the healthcare domain has several advantages and the integration of predictive analytics and machine learning algorithms with big data can enable early detection of diseases, personalized treatment plans, and precision medicine.

GLOBAL BIG DATA IN HEALTHCARE MARKET: KEY INSIGHTS

The report delves into the current state of global big data in healthcare market and identifies potential growth opportunities within industry. Some key findings from the report include:

• More than 405 players claim to offer customized solutions and services to support big data in healthcare initiatives, with around 55% offering data warehouses and data lakes for data management and analytics.
• Majority (>65%) of the service providers are based in North America, particularly in the US; most of the service providers (56%) based in the US are mid-sized companies, followed by large players (26%).

• The market landscape is highly fragmented, featuring the presence of both new entrants and established players based across different geographical regions; close to 55% of such players are mid-sized companies.
• Various analytical models derive insights from clinical, operational and financial data; 23% of the players offer a comprehensive software suite of big data analytics including predictive, prescriptive, and descriptive analytics.
• In pursuit of building a competitive edge, players are actively upgrading their existing capabilities and adding new competencies in order to augment their respective portfolios and affiliated big data offerings.
• By analyzing the key drivers and barriers affecting the evolution of big data in healthcare market, valuable insights can be generated leading to a deeper understanding of the current and future opportunities within this domain.
• Driven by the increasing adoption of cloud-based solutions and services, the big data in healthcare market is likely to grow at a CAGR of 19.06% over the next 12 years.
• The projected market opportunity is anticipated to be well distributed across different components of big data, including various types of hardware, services and software.
• High-income countries are driving market revenues by prioritizing the deployment of big data solutions to optimize operational management, leading to enhanced efficiency and effectiveness in healthcare operations.

• With the rise in demand for telehealth services and personalized medicine, the big data in healthcare market presents lucrative opportunities for players based across various geographies.

GLOBAL BIG DATA IN HEALTHCARE MARKET: KEY SEGMENTS

Hardware Segment Occupies the Largest Share of the Big Data in Healthcare Market

Based on the component, the market is segmented into big data hardware, big data software and big data services. At present, hardware segment holds the maximum (>40%) share of the global big data in healthcare market. Additionally, due to the rising adoption of advanced technologies, and ongoing investments in innovation, the hardware segment is likely to grow at a faster pace compared to the other segments.

By Type of Hardware, Storage Devices Segment is the Fastest Growing Segment of the Global Big Data in Healthcare Market

Based on the type of hardware, the market is segmented into storage devices, networking infrastructure and servers. Currently, storage devices segment captures the highest proportion (~60%) of the big data in healthcare market. Further, this segment is likely to grow at a relatively higher CAGR.

Electronic Health Record Segment Occupy the Largest Share of the Big Data in Healthcare Market

Based on the type of software, the market is segmented into electronic health record, practice management software, revenue cycle management software, and workforce management software. At present, the electronic health record segment holds the maximum share (>45%) of the big data in healthcare market. In addition, workforce management software segment is likely to grow at a relatively higher CAGR.

By Type of Service, the Diagnostic Analytics Segment is the Fastest Growing Segment of the Big Data in Healthcare Market During the Forecast Period

Based on the type of service, the market is segmented into descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Currently, the diagnostic analytics segment captures the highest proportion (>30%) of the big data in healthcare market. Further, it is worth highlighting that the big data in healthcare market for prescriptive analytics segment is likely to grow at a relatively higher CAGR.

Cloud-based Segment Account for the Largest Share of the Global Big Data in Healthcare Market

Based on the deployment option, the market is segmented into cloud-based deployment and on-premises deployment. Currently, cloud-based segment holds the maximum share (~60%) of the big data in healthcare market owing to the various benefits offered by cloud-based deployment, such as scalability, flexibility, cost-effectiveness, ease of implementation and maintenance, and data accessibility. This trend is likely to remain the same in the coming years.

By Application Area, Operational Management Segment is Likely to Dominate the Big Data in Healthcare Market

Based on the application area, the market is segmented into clinical data management, financial management, operational management, and population health management. At present, the operational management segment holds the maximum share (>30%) of the big data in healthcare market. Additionally, the population health management segment is expected to show the highest growth potential during the forecast period, growing at a higher CAGR, compared to the other segments.

The Healthcare Services Segment in Healthcare Vertical Occupy the Largest Share of the Big Data in Healthcare Market

Based on the healthcare vertical, the market is segmented into healthcare services, medical devices, pharmaceuticals, and other verticals. While healthcare services segment is expected to be the primary driver of the overall market, it is worth highlighting that the global big data in healthcare market for medical devices segment is likely to grow at a relatively higher CAGR of more than 20%.

Currently, Hospitals Segment Holds the Largest Share of the Big Data in Healthcare Market

Based on end users, the global market is segmented into clinics, health insurance agencies, hospitals, and other end users. Currently, the hospitals segment holds the largest market share (>40%). However, the big data in healthcare market for clinics segment is expected to witness substantial growth in the coming years.

By Economic Status, the Upper-Middle Income Countries Segment is the Fastest Growing Segment of the Big Data in Healthcare Market During the Forecast Period

Based on the economic status, the market is segmented into high income countries, upper-middle income countries, and lower-middle income countries. Currently, the high-income countries segment captures the highest proportion (~85%) of the big data in healthcare market. Further, it is worth highlighting that the big data in healthcare market for upper-middle income countries segment is likely to grow at a relatively higher CAGR.

North America Accounts for the Largest Share of the Market

Based on key geographical regions, the market is segmented into North America, Europe, Asia, Middle East and North Africa, Latin America and Rest of the World. Currently, North America (~60%) dominates the big data in healthcare market and accounts for the largest revenue share. However, the market in Asia-Pacific is expected to grow at a higher CAGR.

Example Players in the Big Data in Healthcare Market

• Accenture
• Akka Technologies
• Altamira.ai
• Amazon Web Services
• Athena Global Technologies
• atom Consultancy Services (ACS)
• Avenga
• Happiest Minds
• InData Labs
• Itransition
• Kellton
• Keyrus
• Lutech
• Microsoft
• Nagarro
• Nous Infosystems
• NTT data
• Oracle
• Orange Mantra
• Oxagile
• Scalefocus
• Softweb Solutions
• Solix Technologies
• Spindox
• Tata Elxsi
• Teradata
• Trianz (formerly CBIG Consulting)
• Trigyn Technologies
• XenonStack

PRIMARY RESEARCH OVERVIEW

The opinions and insights presented in this study were influenced by discussions conducted with multiple stakeholders. The research report features detailed transcripts of interviews conducted with the following industry stakeholders:

• Chief Executive Officer and Founder, Company A
• Chief Executive Officer and Co-Founder, Company B
• Chief People Officer and Co-Founder, Company C
• Vice President, Company D
• Vice President, Company E
• Business Head, Company F
• Senior IT Inside Sales Lead, Company G
• Senior Manager, Company H
• Delivery Manager, Company I
• Strategy, Research and Analyst Relations Manager, Company J
• Business Development Manager, Company K
• Business Development Associate, Company L
• Business Development Specialist Advisor, Company M
• Business Development Executive, Company N

GLOBAL BIG DATA IN HEALTHCARE MARKET: RESEARCH COVERAGE

• Market Sizing and Opportunity Analysis: The report features an in-depth analysis of the global big data in healthcare market, focusing on key market segments, including [A] component, [B] type of hardware, [C] type of software, [D] type of service, [E] deployment option, [F] application area, [G] healthcare vertical, [H] end user, [I] economic status and [J] key geographical regions.
• Market Landscape: A comprehensive evaluation of big data in healthcare service providers, considering various parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters, [D] business model, [E] type of offering, [F] type of big data analytics offered, [G] type of big data storage solution offered, [H] deployment option, [I] application area and [J] end user.
• Company Competitiveness Analysis: A comprehensive competitive analysis of big data in healthcare service providers, examining factors, such as [A] supplier strength and [B] portfolio strength.
• Company Profiles: In-depth profiles of companies engaged in offering big data analytics solutions across various geographies, focusing on [A] company overviews, [B] financial information (if available), [C] big data analytics offerings and capabilities and [D] recent developments and an informed future outlook.
• Market Impact Analysis: A thorough analysis of various factors, such as drivers, restraints, opportunities, and existing challenges that are likely to impact market growth.

KEY QUESTIONS ANSWERED IN THIS REPORT

• How many companies are currently engaged in this market?
• Which are the leading companies in this market?
• What factors are likely to influence the evolution of this market?
• What is the current and future market size?
• What is the CAGR of this market?
• How is the current and future market opportunity likely to be distributed across key market segments?

REASONS TO BUY THIS REPORT

• The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
• Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
• The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

ADDITIONAL BENEFITS

• Complimentary PPT Insights Packs
• Complimentary Excel Data Packs for all Analytical Modules in the Report
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TABLE OF CONTENTS

1. PREFACE

• 1.1. Introduction
• 1.2. Market Share Insights
• 1.3. Key Market Insights
• 1.4. Report Coverage
• 1.5. Key Questions Answered
• 1.6. Chapter Outlines

2.RESEARCH METHODOLOGY

• 2.1.Chapter Overview
• 2.2.Research Assumptions
• 2.3.Project Methodology
• 2.4.Forecast Methodology
• 2.5.Robust Quality Control
• 2.6.Key Considerations
  • 2.6.1.Demographics
  • 2.6.2.Economic Factors
  • 2.6.3.Government Regulations
  • 2.6.4. Supply Chain
  • 2.6.5.COVID Impact / Related Factors
  • 2.6.6. Market Access
  • 2.6.7. Healthcare Policies
  • 2.6.8. Industry Consolidation
• 2.7. Key Market Segmentations

3. ECONOMIC AND OTHER PROJECT SPECIFIC CONSIDERATIONS

• 3.1. Chapter Overview
• 3.2. Market Dynamics
  • 3.2.1. Time Period
    • 3.2.1.1. Historical Trends
    • 3.2.1.2. Current and Forecasted Estimates
  • 3.2.2. Currency Coverage
    • 3.2.2.1. Major Currencies Affecting the Market
    • 3.2.2.2. Impact of Currency Fluctuations on the Industry
  • 3.2.3. Foreign Exchange Impact
    • 3.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
    • 3.2.3.2. Strategies for Mitigating Foreign Exchange Risk
  • 3.2.4. Recession
    • 3.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
    • 3.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
  • 3.2.5. Inflation
    • 3.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
    • 3.2.5.2. Potential Impact of Inflation on the Market Evolution

4. EXECUTIVE SUMMARY

• 4.1. Chapter Overview

5. INTRODUCTION

• 5.1. Chapter Overview
• 5.2. Overview of Big Data
  • 5.2.1. Types of Big Data
    • 5.2.1.1. Structured Data
    • 5.2.1.2. Unstructured Data
    • 5.2.1.3. Semi-Structured Data
  • 5.2.2. Management and Storage of Big Data
• 5.3. Big Data Analytics
  • 5.3.1. Types of Big Data Analytics
    • 5.3.1.1. Descriptive Analytics
    • 5.3.1.2. Diagnostic Analytics
    • 5.3.1.3. Predictive Analytics
    • 5.3.1.4. Prescriptive Analytics
• 5.4. Applications of Big Data in Healthcare
• 5.5. Future Perspective

6. OVERALL MARKET LANDSCAPE

• 6.1. Chapter Overview
• 6.2. Big Data in Healthcare Service Providers: Overall Market Landscape
• 6.3. Analysis by Year of Establishment
• 6.4. Analysis by Company Size
• 6.5. Analysis by Location of Headquarters
• 6.6. Analysis by Type of Business Model
• 6.7. Analysis by Type of Offering
• 6.8. Analysis by Type of Big Data Analytics Offered
• 6.9. Analysis by Type of Big Data Storage Solution Offered
• 6.10. Analysis by Deployment Option
• 6.11. Analysis by Application Area
• 6.12. Analysis by End User

7. KEY INSIGHTS

• 7.1. Chapter Overview
• 7.2. Big Data in Healthcare Service Providers: Key Insights
  • 7.2.1. Analysis by Year of Establishment and Company Size
  • 7.2.2. Analysis by Company Size and Location of Headquarters
  • 7.2.3. Analysis by Type of Offering and Company Size
  • 7.2.4. Analysis by Type of Big Data Analytics Offered and Application Area
  • 7.2.5. Analysis by Company Size, Application Area and End User

8. COMPANY COMPETITIVENSS ANALYSIS

• 8.1. Chapter Overview
• 8.2. Assumptions and Key Parameters
• 8.3. Methodology
• 8.4. Big Data in Healthcare Service Providers: Company Competitiveness Analysis
  • 8.4.1. Big Data in Healthcare Service Providers based in North America
    • 8.4.1.1. Small Service Providers based in North America
    • 8.4.1.2. Mid-sized Service Providers based in North America
    • 8.4.1.3. Large Service Providers based in North America
    • 8.4.1.4. Very LargeService Providers based in North America
  • 8.4.2. Big Data in Healthcare Service Providers based in Europe
    • 8.4.2.1. Small Service Providers based in Europe
    • 8.4.2.2. Mid-sized Service Providers based in Europe
    • 8.4.2.3. Large and Very Large Service Providers based in Europe
  • 8.4.3. Big Data in Healthcare Service Providers based in Asia and Rest of the World
    • 8.4.3.1. Small Service Providers based in Asia and Rest of the World
    • 8.4.3.2. Mid-sized Service Providers based in Asia and Rest of the World
    • 8.4.3.3. Large Service Providers based in Asia and Rest of the World
    • 8.4.3.4. Very Large Service Providers based in Asia and Rest of the World

9. COMPANY PROFILES: BIG DATA IN HEALTHCARE SERVICE PROVIDERS IN NORTH AMERICA

• 9.1. Chapter Overview
• 9.2. Detailed Company Profiles of Leading Players in North America
  • 9.2.1. Amazon Web Services
    • 9.2.1.1. Company Overview
    • 9.2.1.2. Financial Information
    • 9.2.1.3. Big Data Offerings and Capabilities
    • 9.2.1.4. Recent Developments and Future Outlook
  • 9.2.2. Microsoft
    • 9.2.2.1. Company Overview
    • 9.2.2.2. Financial Information
    • 9.2.2.3. Big Data Offerings and Capabilities
    • 9.2.2.4. Recent Developments and Future Outlook
  • 9.2.3. Oracle
    • 9.2.3.1. Company Overview
    • 9.2.3.2. Financial Information
    • 9.2.3.3. Big Data Offerings and Capabilities
    • 9.2.3.4. Recent Developments and Future Outlook
  • 9.2.4. Teradata
    • 9.2.4.1. Company Overview
    • 9.2.4.2. Financial Information
    • 9.2.4.3. Big Data Offerings and Capabilities
    • 9.2.4.4. Recent Developments and Future Outlook
• 9.3. Short Company Profiles of Other Prominent Players in North America
  • 9.3.1. Itransition
    • 9.3.1.1. Company Overview
    • 9.3.1.2. Big Data Offerings and Capabilities
  • 9.3.2 Nous Infosystems
    • 9.3.2.1. Company Overview
    • 9.3.2.2. Big Data Offerings and Capabilities
  • 9.3.3 Oxagile
    • 9.3.3.1. Company Overview
    • 9.3.3.2. Big Data Offerings and Capabilities
  • 9.3.4 Softweb Solutions
    • 9.3.4.1. Company Overview
    • 9.3.4.2. Big Data Offerings and Capabilities
  • 9.3.5 Solix Technologies
    • 9.3.5.1. Company Overview
    • 9.3.5.2. Big Data Offerings and Capabilities
  • 9.3.6 Trianz (formerly CBIG Consulting)
    • 9.3.6.1. Company Overview
    • 9.3.6.2. Big Data Offerings and Capabilities

10. COMPANY PROFILES: BIG DATA IN HEALTHCARE SERVICE PROVIDERS IN EUROPE

• 10.1. Chapter Overview
• 10.2. Detailed Company Profiles of Leading Players in Europe
  • 10.2.1. Accenture
    • 10.2.1.1. Company Overview
    • 10.2.1.2. Financial Information
    • 10.2.1.3. Big Data Offerings and Capabilities
    • 10.2.1.4. Recent Developments and Future Outlook
  • 10.2.2. Keyrus
    • 10.2.2.1. Company Overview
    • 10.2.2.2. Financial Information
    • 10.2.2.3. Big Data Offerings and Capabilities
    • 10.2.2.4. Recent Developments and Future Outlook
• 10.3. Short Company Profiles of Other Prominent Players in Europe
  • 10.3.1. Akka Technologies
    • 10.3.1.1. Company Overview
    • 10.3.1.2. Big Data Offerings and Capabilities
  • 10.3.2 Altamira.ai
    • 10.3.2.1. Company Overview
    • 10.3.2.2. Big Data Offerings and Capabilities
  • 10.3.3 atom Consultancy Services (ACS)
    • 10.3.3.1. Company Overview
    • 10.3.3.2. Big Data Offerings and Capabilities
  • 10.3.4 Avenga
    • 10.3.4.1. Company Overview
    • 10.3.4.2. Big Data Offerings and Capabilities
  • 10.3.5 Lutech
    • 10.3.5.1. Company Overview
    • 10.3.5.2. Big Data Offerings and Capabilities
  • 10.3.6 Nagarro
    • 10.3.6.1. Company Overview
    • 10.3.6.2. Big Data Offerings and Capabilities
  • 10.3.7 Scalefocus
    • 10.3.7.1. Company Overview
    • 10.3.7.2. Big Data Offerings and Capabilities
  • 10.3.8 Spindox
    • 10.3.8.1. Company Overview
    • 10.3.8.2. Big Data Offerings and Capabilities

11. COMPANY PROFILES: BIG DATA IN HEALTHCARE SERVICE PROVIDERS IN ASIA AND REST OF THE WORLD

• 11.1. Chapter Overview
• 11.2. Detailed Company Profiles of Leading Players in Asia and Rest of the World
  • 11.2.1. Tata Elxsi
    • 11.2.1.1. Company Overview
    • 11.2.1.2. Big Data Offerings and Capabilities
    • 11.2.1.3. Recent Developments and Future Outlook
  • 11.2.2. Kellton
    • 11.2.2.1. Company Overview
    • 11.2.2.2. Financial Information
    • 11.2.2.3. Big Data Offerings and Capabilities
    • 11.2.2.4. Recent Developments and Future Outlook
• 11.3. Short Company Profiles of Other Prominent Players in Asia and Rest of the World
  • 11.3.1. Athena Global Technologies
    • 11.3.1.1. Company Overview
    • 11.3.1.2. Big Data Offerings and Capabilities
  • 11.3.2 Happiest Minds
    • 11.3.2.1. Company Overview
    • 11.3.2.2. Big Data Offerings and Capabilities
  • 11.3.3 InData Labs
    • 11.3.3.1. Company Overview
    • 11.3.3.2. Big Data Offerings and Capabilities
  • 11.3.4 NTT data
    • 11.3.4.1. Company Overview
    • 11.3.4.2. Big Data Offerings and Capabilities
  • 11.3.5 OrangeMantra
    • 11.3.5.1. Company Overview
    • 11.3.5.2. Big Data Offerings and Capabilities
  • 11.3.6 Trigyn Technologies
    • 11.3.6.1. Company Overview
    • 11.3.6.2. Big Data Offerings and Capabilities
  • 11.3.7 XenonStack
    • 11.3.7.1. Company Overview
    • 11.3.7.2. Big Data Offerings and Capabilities

12. MARKET IMPACT ANALYSIS: DRIVERS, RESTRAINTS, OPPORTUNITIES AND CHALLENGES

• 12.1. Chapter Overview
• 12.2. Market Drivers
• 12.3. Market Restraints
• 12.4. Market Opportunities
• 12.5. Market Challenges
• 12.6. Conclusion

13. GLOBAL BIG DATA IN HEALTHCARE MARKET

• 13.1. Chapter Overview
• 13.2. Key Assumptions and Methodology
• 13.3. Global Big Data in Healthcare Market, Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 13.3.1. Scenario Analysis
    • 13.3.1.1. Conservative Scenario
    • 13.3.1.2. Optimistic Scenario
• 13.4. Key Market Segmentations

14. BIG DATA IN HEALTHCARE MARKET, BY COMPONENT

• 14.1. Chapter Overview
• 14.2. Key Assumptions and Methodology
• 14.3. Big Data in Healthcare Market: Distribution by Component
  • 14.3.1. Big Data Hardware: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 14.3.2. Big Data Software: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 14.3.3. Big Data Services: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
• 14.4. Data Triangulation and Validation

15. BIG DATA IN HEALTHCARE MARKET, BY TYPE OF HARDWARE

• 15.1. Chapter Overview
• 15.2. Key Assumptions and Methodology
• 15.3. Big Data in Healthcare Market: Distribution by Type of Hardware
  • 15.3.1. Storage Devices: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 15.3.2. Servers: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 15.3.3. Networking Infrastructure: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
• 15.4. Data Triangulation and Validation

16. BIG DATA IN HEALTHCARE MARKET, BY TYPE OF SOFTWARE

• 16.1. Chapter Overview
• 16.2. Key Assumptions and Methodology
• 16.3. Big Data in Healthcare Market: Distribution by Type of Software
  • 16.3.1. Electronic Health Record: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 16.3.2. Revenue Cycle Management Software: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 16.3.3. Practice Management Software: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 16.3.4. Workforce Management Software: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
• 16.4. Data Triangulation and Validation

17. BIG DATA IN HEALTHCARE MARKET, BY TYPE OF SERVICE

• 17.1. Chapter Overview
• 17.2. Key Assumptions and Methodology
• 17.3. Big Data in Healthcare Market: Distribution by Type of Services
  • 17.3.1. Diagnostic Analytics: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 17.3.2. Descriptive Analytics: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 17.3.3. Predictive Analytics: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 17.3.4. Prescriptive Analytics: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
• 17.4. Data Triangulation and Validation

18. BIG DATA IN HEALTHCARE MARKET, BY DEPLOYMENT OPTION

• 18.1. Chapter Overview
• 18.2. Key Assumptions and Methodology
• 18.3. Big Data in Healthcare Market: Distribution by Deployment Option
  • 18.3.1. Cloud-based Deployment: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 18.3.2. On-premises Deployment: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
• 18.4. Data Triangulation and Validation

19. BIG DATA IN HEALTHCARE MARKET, BY APPLICATION AREA

• 19.1. Chapter Overview
• 19.2. Key Assumptions and Methodology
• 19.3. Big Data in Healthcare Market: Distribution by Application Area
  • 19.3.1. Operational Management: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 19.3.2. Clinical Data Management: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 19.3.3. Financial Management: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 19.3.4. Population Health Management: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
• 19.4. Data Triangulation and Validation

20. BIG DATA IN HEALTHCARE MARKET, BY HEALTHCARE VERTICAL

• 20.1. Chapter Overview
• 20.2. Key Assumptions and Methodology
• 20.3. Big Data in Healthcare Market: Distribution by Healthcare Vertical
  • 20.3.1. Healthcare Services: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 20.3.2. Pharmaceuticals: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 20.3.3. Medical Devices: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 20.3.4. Other Verticals: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
• 20.4. Data Triangulation and Validation

21. BIG DATA IN HEALTHCARE MARKET, BY END USER

• 21.1. Chapter Overview
• 21.2. Key Assumptions and Methodology
• 21.3. Big Data in Healthcare Market: Distribution by End User
  • 21.3.1. Hospitals: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 21.3.2. Health Insurance Agencies: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 21.3.3. Clinics: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 21.3.4. Other End Users: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
• 21.4. Data Triangulation and Validation

22. BIG DATA IN HEALTHCARE MARKET, BY ECONOMIC STATUS

• 22.1. Chapter Overview
• 22.2. Key Assumptions and Methodology
• 22.3. Big Data in Healthcare Market: Distribution by Economic Status
  • 22.3.1. High Income Countries: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.1. US: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.2. Canada: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.3. Germany: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.4. UK: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.5. UAE: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.6. South Korea: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.7. France: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.8. Australia: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.9. New Zealand: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.10. Italy: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.11. Saudi Arabia: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.1.12. Nordic Countries: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 22.3.2. Upper-Middle Income Countries: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.2.1. China: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.2.2. Russia: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.2.3. Brazil: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.2.4. Japan: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.2.5. South Africa: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 22.3.3. Lower-Middle Income Countries: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
    • 22.3.3.1. India: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
• 22.4. Data Triangulation and Validation

23. BIG DATA IN HEALTHCARE MARKET, BY GEOGRAPHY

• 23.1. Chapter Overview
• 23.2. Key Assumptions and Methodology
• 23.3. Big Data in Healthcare Market: Distribution by Geography
  • 23.3.1. North America: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 23.3.2. Europe: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 23.3.3. Asia: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 23.3.4. Middle East and North Africa: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 23.3.5. Latin America: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
  • 23.3.6. Rest of the World: Historical Trends (Since 2018) and Forecasted Estimates (Till 2035)
• 23.4. Data Triangulation and Validation

24. BIG DATA IN HEALTHCARE MARKET, REVENUE FORECAST OF LEADING PLAYERS

• 24.1. Chapter Overview
• 24.2. Key Assumptions and Methodology
• 24.3. Microsoft: Revenue Generated from Big Data in Healthcare Offerings Since FY 2018
• 24.4. Optum: Revenue Generated from Big Data in Healthcare Offerings Since FY 2018
• 24.5. IBM: Revenue Generated from Big Data in Healthcare Offerings Since FY 2018
• 24.6. Oracle: Revenue Generated from Big Data in Healthcare Offerings Since FY 2018
• 24.7. Allscripts: Revenue Generated from Big Data in Healthcare Offerings Since FY 2018

25. CONCLUSION

• 25.1. Chapter Overview

26. EXECUTIVE INSIGHTS

• 26.1. Chapter Overview
• 26.2. Company A
  • 26.2.1. Company Snapshot
  • 26.2.2. Interview Transcript
• 26.3. Company B
  • 26.3.1. Company Snapshot
  • 26.3.2. Interview Transcript
• 26.4. Company C
  • 26.4.1. Company Snapshot
  • 26.4.2. Interview Transcript
• 26.5. Company D
  • 26.5.1. Company Snapshot
  • 26.5.2. Interview Transcrip
• 26.6. Company E
  • 26.6.1. Company Snapshot
  • 26.6.2. Interview Transcript
• 26.7. Company F
  • 26.7.1. Company Snapshot
  • 26.7.2. Interview Transcript
• 26.8. Company G
  • 26.8.1. Company Snapshot
  • 26.8.2. Interview Transcript
• 26.9. Company H
  • 26.9.1. Company Snapshot
  • 26.9.2. Interview Transcript
• 26.10. Company I
  • 26.10.1. Company Snapshot
  • 26.10.2. Interview Transcript
• 26.11. Company J
  • 26.11.1. Company Snapshot
  • 26.11.2. Interview Transcript
• 26.12. Company K
  • 26.12.1. Company Snapshot
  • 26.12.2. Interview Transcript
• 26.13. Company L
  • 26.13.1. Company Snapshot
  • 26.13.2. Interview Transcript
• 26.14. Company M
  • 26.14.1. Company Snapshot
  • 26.14.2. Interview Transcript
• 26.15. Company N
  • 26.15.1. Company Snapshot
  • 26.15.2. Interview Transcript

27. APPENDIX I: TABULATED DATA

28. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS