헬스케어 분야 빅데이터 시장 : 컴포넌트별, 하드웨어 유형별, 소프트웨어 유형별, 서비스 유형별, 전개 옵션별, 적용 분야별, 헬스케어 업계별, 최종사용자별, 지역별, 주요 기업별 - 업계 동향과 세계 예측(- 2035년)

Big Data in Healthcare Market by Component, Type of Hardware, Type of Software, and Type of Service, Deployment Option, Application Area, Healthcare Vertical, End User, Geography, and Leading Players: Industry Trends and Global Forecasts, Till 2035

US $ 4,799 ￦ 6,696,000

PDF (Single User License)

PDF 보고서를 1명만 이용할 수 있는 라이선스입니다. 인쇄 가능하며 인쇄물의 이용 범위는 PDF 이용 범위와 동일합니다.

US $ 6,999 ￦ 9,766,000

PDF (One-Location Site License)

PDF 보고서를 동일 사업장의 특정 사업 부문에 속한 모든 분들이 이용할 수 있는 라이선스입니다. 인쇄 가능하며 인쇄물의 이용 범위는 PDF 이용 범위와 동일합니다.

US $ 10,599 ￦ 14,789,000

PDF (Department License)

PDF 보고서를 부서 단위로 12명까지 이용할 수 있는 라이선스입니다. 인쇄 가능하며 인쇄물의 이용 범위는 PDF 이용 범위와 동일합니다.

US $ 17,999 ￦ 25,115,000

PDF (Enterprise License)

PDF 보고서를 동일 기업의 모든 분이 이용할 수 있는 라이선스입니다. 인쇄 가능하며 인쇄물의 이용 범위는 PDF 이용 범위와 동일합니다.

한글목차

세계 헬스케어 분야 빅데이터 시장 규모는 2035년까지 예측 기간 동안 19.20%의 연평균 복합 성장률(CAGR)로 확대되어 현재 780억 달러에서 2035년까지 5,400억 달러로 성장할 것으로 예상됩니다.

헬스케어 분야에서의 빅데이터 분석의 통합은 업계에 혁명을 불러일으키고 서비스 제공업체에 엄청난 기회를 제공할 수 있는 엄청난 잠재력을 가지고 있습니다. 방대한 양의 데이터에서 실행 가능한 통찰력을 수집, 분석 및 도출하는 능력은 임상적 의사결정을 강화하고, 자원 배분을 최적화하며, 환자 결과를 개선할 수 있습니다. 또한, 예측 분석과 머신러닝 알고리즘을 빅데이터와 통합하여 질병의 조기 발견, 개인화된 치료 계획, 정밀의료를 가능하게 합니다. 이러한 패러다임의 변화는 서비스 제공업체에게 실시간 모니터링 시스템, 데이터 기반 진단을 위한 클라우드 기반 플랫폼 등 혁신적인 솔루션을 개발할 수 있는 기회를 제공합니다. 이러한 발전의 총체적 결과, 의료 비용의 대폭적인 절감, 업무 효율성 향상, 더 나은 품질의 의료 서비스 제공이 가능해질 수 있습니다. 헬스케어 업계가 빅데이터 분석을 계속 도입함에 따라, 변화의 영향력과 비즈니스 기회의 크기가 점점 더 분명해지면서 의료 제공업체와 환자 모두에게 새로운 변화를 가져올 것으로 보입니다.

405개 이상의 기업이 헬스케어 분야 빅데이터 활용을 지원하는 맞춤형 솔루션과 서비스를 제공하고 있으며, 이 중 약 55%가 데이터 관리 및 분석을 위한 데이터 웨어하우스와 데이터 레이크를 제공합니다.

시장 상황은 매우 파편화되어 있고, 지역별로 신규 진출기업과 기존 기업이 모두 존재하며, 그 중 55% 가량이 중견 기업입니다. 다양한 분석 모델이 임상 데이터, 업무 데이터, 재무 데이터에서 인사이트를 도출하고 있습니다. 23%는 예측 분석, 처방 분석, 서술적 분석 등 빅데이터 분석에 대한 종합적인 소프트웨어 제품군을 제공합니다. 클라우드 기반 솔루션 및 서비스 도입 확대에 힘입어 헬스케어 분야 빅데이터 시장은 향후 12년간 연평균 19.06%의 성장률을 보일 것으로 예상됩니다. 고소득 국가들은 업무 관리를 최적화하기 위해 빅데이터 솔루션을 도입하여 헬스케어 업무의 효율성과 효과성을 향상시키는 데 우선순위를 두고 있으며, 이는 시장 수익을 견인하고 있습니다. 원격 의료 서비스 및 개인 맞춤형 의료에 대한 수요가 증가함에 따라, 헬스케어 분야 빅데이터 시장은 다양한 지역에 기반을 둔 진출기업들에게 유리한 기회를 제공합니다.

세계의 헬스케어 분야 빅데이터 시장에 대해 조사했으며, 시장 개요와 함께 구성요소별/하드웨어 유형별/소프트웨어 유형별/서비스 유형별/배포 옵션별/적용 분야별/헬스케어 산업별/최종사용자별/지역별 동향, 진출 기업 프로파일 등의 정보를 정리하여 전해드립니다. 시장 진출기업 프로파일 등의 정보를 제공합니다.

목차

제1장 서문

제2장 조사 방법

제3장 경제 및 기타 프로젝트 특유의 고려사항

제4장 주요 요약

제5장 서론

제6장 시장 구도

제7장 주요 인사이트

제8장 기업 경쟁력 분석

제9장 기업 개요 : 북미의 헬스케어 분야 빅데이터 서비스 제공업체

제10장 기업 개요 : 유럽의 헬스케어 분야 빅데이터 서비스 제공업체

제11장 기업 개요 : 아시아 및 기타 지역의 헬스케어 분야 빅데이터 서비스 제공업체

제12장 시장에 대한 영향 분석 : 성장 촉진요인 및 억제요인, 기회, 과제

제13장 세계의 헬스케어 분야 빅데이터 시장

제14장 헬스케어 분야 빅데이터 시장, 컴포넌트별

제15장 헬스케어 분야 빅데이터 시장, 하드웨어 유형별

제16장 헬스케어 분야 빅데이터 시장, 소프트웨어 유형별

제17장 헬스케어 분야 빅데이터 시장, 서비스 유형별

제18장 헬스케어 분야 빅데이터 시장, 전개 옵션별

제19장 헬스케어 분야 빅데이터 시장, 적용 분야별

제20장 헬스케어 분야 빅데이터 시장, 헬스케어 업계별

제21장 헬스케어 분야 빅데이터 시장, 최종사용자별

제22장 헬스케어 분야 빅데이터 시장, 경제 상황별

제23장 헬스케어 분야 빅데이터 시장, 지역별

제24장 헬스케어 분야 빅데이터 시장, 주요 기업의 매출 예측

• 본 장의 개요
• 주요 전제와 조사 방법
• Microsoft
• Optum
• IBM
• Oracle
• Allscripts

제25장 결론

제26장 주요 인사이트

제27장 부록 I : 표 형식 데이터

제28장 부록 II : 기업 및 단체 리스트

LSH

영문 목차

영문목차

BIG DATA IN HEALTHCARE MARKET: OVERVIEW

As per Roots Analysis, the global 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 (Storage Devices, Servers, and Networking Infrastructure)
• Software (Electronic Health Record, Practice Management Software, Revenue Cycle Management Software, and Workforce Management Software)
• Services (Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and 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

Economic Status

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

End User

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

Geography

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

BIG DATA IN HEALTHCARE MARKET: GROWTH AND TRENDS

The integration of big data analytics in the healthcare domain holds immense potential for revolutionizing the industry and unlocking lucrative business opportunities for service providers. The ability to aggregate, analyze, and derive actionable insights from vast amounts of data can enhance clinical decision-making, optimize resource allocation, and improve patient outcomes. Moreover, the integration of predictive analytics and machine learning algorithms with big data can enable early detection of diseases, personalized treatment plans, and precision medicine. This paradigm shift offers service providers the chance to develop innovative solutions, such as cloud-based platforms for real-time monitoring systems, and data-driven diagnostics. Collectively, these advancements have the potential to drastically reduce healthcare costs, enhance operational efficiency, and enable the delivery of higher quality care. As the healthcare industry continues to embrace big data analytics, the magnitude of the transformative impact and the vast business opportunities will become increasingly evident, revolutionizing the landscape for both providers and patients.

BIG DATA IN HEALTHCARE MARKET: KEY INSIGHTS

The report delves into the current state of the big data in healthcare market and identifies potential growth opportunities within the 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.

• 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.
• 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.
• 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.

BIG DATA IN HEALTHCARE MARKET: KEY SEGMENTS

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

Based on the type of component, the global market is segmented into hardware, services and software. At present, hardware holds the maximum share of the big data in healthcare market. It is worth highlighting that the market for software is likely to grow at a relatively higher CAGR.

Storage Devices are Likely to Dominate the Big Data in Healthcare Market During the Forecast Period

Based on the type of hardware, the global market is segmented into storage devices, networking infrastructure and servers. At present, storage devices hold the maximum share of the big data in healthcare market. This trend is unlikely to change in the near future.

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

Based on the type of software, the global market is segmented into electronic health record (EHR), practice management software, revenue cycle management software and workforce management software. At present, electronic health record holds the maximum share of the big data in healthcare market. This trend is unlikely to change in the foreseeable future. The increasing demand for EHRs can be attributed to the growing adoption of digital health technologies and the global efforts towards interoperability in healthcare systems.

Diagnostic Analytics is Likely to Dominate the Big Data in Healthcare Market

Based on the type of service, the global market is segmented into descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. It is worth highlighting that, at present, diagnostic analytics holds a larger share of the big data in healthcare market. However, the global market for prescriptive analytics is likely to grow at relatively higher CAGR.

Cloud-based Deployment Captures a Considerable Proportion of the Big Data in Healthcare Market During the Forecast Period

Based on the deployment option, the global market is segmented into cloud-based and on-premises. At present, cloud-based deployment holds the maximum share of the big data in healthcare market. This trend is unlikely to change in the near future.

Operational Management Occupies the Largest Share of the Big Data in Healthcare Market

Based on the application area, the global market is segmented into clinical data management, financial management, operational management and population health management. It is worth highlighting that majority of the current big data in healthcare market is captured by the operational management segment.

Medical Devices are the Fastest Growing Segment of the Big Data in Healthcare Market During the Forecast Period

Based on the healthcare vertical, the global market is segmented into healthcare services, medical devices, pharmaceuticals, and other verticals. It is worth highlighting that, at present, healthcare services hold a larger portion of the big data in healthcare market. However, the global market for medical devices is likely to grow at a relatively higher CAGR.

Hospitals are Likely to Dominate the Big Data in Healthcare Market During the Forecast Period

Based on the end-users, the global market is segmented into clinics, health insurance agencies, hospitals and other end users. At present, hospitals hold the maximum share of the big data in healthcare market. This trend is unlikely to change in the near future.

High Income Countries Occupy the Largest Share of the Big Data in Healthcare Market

Based on the economic status, the global market is segmented into high income countries, upper-middle income countries, and lower-middle income countries. It is worth highlighting that the current big data in healthcare market is likely to be driven by revenues generated in high income countries.

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. Majority share is expected to be captured by players based in North America. It is worth highlighting that, over the years, the market in Asia 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 held with the following industry stakeholders:

• Chief Executive Officer and Founder, Emorphis Technologies
• Chief Executive Officer and Co-Founder, DataToBiz
• Chief People Officer and Co-Founder, Estenda Solutions
• Vice President, Marketing, Growth Acceleration Partners
• Business Head, OrangeMantra
• Senior IT Inside Sales Lead, Soulpage IT Solutions
• Senior Manager, Business Development, TechMango
• Delivery Manager, W2S Solutions
• Strategy, Research and Analyst Relations Manager, Tata Elxsi
• Business Development Manager, OpenXcell
• Business Development Associate, ThirdEye Data
• Business Development Specialist Advisor, NTT Data Services
• Business Development Executive, CodeRiders
• Business Consultant, Xenon Stack

BIG DATA IN HEALTHCARE MARKET: RESEARCH COVERAGE

• Market Sizing and Opportunity Analysis: The report features an in-depth analysis of the 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] geographical regions.
• Market Landscape: A comprehensive evaluation of service providers involved in the big data in healthcare market, considering various parameters, such as [A] year of establishment, [B] company size (in terms of the number of employees), [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.
• Key Insights: A detailed analysis, encompassing the contemporary big data in healthcare market trends, based on relevant parameters, such as [A] company size and location of headquarters; [B] company size and business model; [C] type of offerings and location of headquarters; [D] type of big data storage solution offered and deployment option; [E] type of big data analytics offered and application area; [F] company size, application area and 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 key industry players 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, [D] recent developments and [E] an informed future outlook.
• Market Impact Analysis: The report analyzes various factors such as drivers, restraints, opportunities, and challenges affecting the 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 are the factors that 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
• 10% Free Content Customization
• Detailed Report Walkthrough Session with Research Team
• Free Updated report if the report is 6-12 months old or older

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. 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 Large Service 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, 2018, 2023 and 2035
  • 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, 2018, 2023 and 2035
  • 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, 2018, 2023 and 2035
  • 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, 2018, 2023 and 2035
  • 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, 2018, 2023 and 2035
  • 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, 2018, 2023 and 2035
  • 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, 2018, 2023 and 2035
  • 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, 2018, 2023 and 2035
  • 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, 2018, 2023 and 2035
  • 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, 2018, 2023 and 2035
  • 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 FY 2018 Onwards
• 24.4. Optum: Revenue Generated from Big Data in Healthcare Offerings FY 2018 Onwards
• 24.5. IBM: Revenue Generated from Big Data in Healthcare Offerings FY 2018 Onwards
• 24.6. Oracle: Revenue Generated from Big Data in Healthcare Offerings FY 2018 Onwards
• 24.7. Allscripts: Revenue Generated from Big Data in Healthcare Offerings FY 2018 Onwards

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

관련자료