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市場調查報告書

醫療的巨量資料

Big Data in Healthcare 2015 - 2020

出版商 Mind Commerce 商品編碼 269051
出版日期 內容資訊 英文 46 Pages
商品交期: 最快1-2個工作天內
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醫療的巨量資料 Big Data in Healthcare 2015 - 2020
出版日期: 2015年06月22日 內容資訊: 英文 46 Pages
簡介

本報告提供醫療生態系統的巨量資料及技術,促進成長要素,課題,相關利益者相關的機會相關調查分析,各種的經營模式分析,未來展望等彙整資料,為您概述為以下內容。

第1章 摘要整理

第2章 簡介

  • 巨量資料是什麼?
  • 巨量資料的分類
  • 為何資訊科技重要?
  • 巨量資料的促進成長要素
  • 巨量資料技術

第3章 醫療的巨量資料

  • 概念課題
    • 數量
    • 種類
    • 速度
  • 實踐課題
    • 技術疏忽的醫療
    • 整合
    • 安全
    • 標準
    • 即時處理
  • 醫療相關利益者
    • 患者
    • 供應商
    • 研究人員
    • 製藥公司
    • 醫療設備企業
    • 醫療費支付者
    • 政府
    • 軟體開發業者

第4章 巨量資料的醫療經營模式及企業

  • 基因學研究
  • 醫療巨量資料分析
  • 詐欺檢測·管理
  • 個性化醫療
  • 行動醫療

第5章 未來展望

  • 巨量資料分析的進一步研究
  • 個性化醫療的轉移
  • 可預測-可以有效預防的疾病
  • 醫生的進一步分析
  • 藥物研發的進一步轉移

圖表

目錄

Medical data represents a large, rapidly growing, and mostly unstructured data residing in multiple locations including lab and imaging systems, physician notes, medical correspondence, claims, CRM and financial systems. With resizing costs with the healthcare industry, there is an imperative to reduce the cost of care and efficiently manage resources without compromising patient care. Healthcare organizations have the opportunity to leverage big data technology to perform analytics to improve care and profitability.

This report evaluates Big Data in healthcare ecosystem and opportunities including technologies, growth drivers, challenges, and stakeholders. The report analyzes different business models employed by healthcare big data business practices, including key factors affecting each business model, various company approaches and solutions.

Table of Contents

1.0. EXECUTIVE SUMMARY

2.0. INTRODUCTION

  • 2.1. WHAT IS BIG DATA?
  • 2.2. BIG DATA CATEGORIES
    • 2.2.1. Structured Big Data
    • 2.2.2. Un-structured data
    • 2.2.3. Semi-structured data
  • 2.3. WHY IS IT IMPORTANT?
    • 2.3.1. Pattern Discovery
    • 2.3.2. Decision Making
    • 2.3.3. Process Invention
    • 2.3.4. Increasing Revenue
  • 2.4. BIG DATA GROWTH DRIVERS
  • 2.5. BIG DATA TECHNOLOGY
    • 2.5.1. Sensors
    • 2.5.2. Computer networks
    • 2.5.3. Data storage
    • 2.5.4. Cluster computer systems
    • 2.5.5. Cloud computing facilities
    • 2.5.6. Data analysis algorithms

3.0. BIG DATA IN HEALTHCARE

  • 3.1. CONCEPTUAL CHALLENGES
    • 3.1.1. Volume
    • 3.1.2. Variety
    • 3.1.3. Velocity
  • 3.2. PRACTICAL CHALLENGES
    • 3.2.1. Healthcare as a Technology Laggard
    • 3.2.2. Integration
    • 3.2.3. Security
    • 3.2.4. Standards
    • 3.2.5. Real-time Processing
  • 3.3. HEALTHCARE STAKEHOLDERS
    • 3.3.1. Patients
    • 3.3.2. Providers
    • 3.3.3. Researchers
    • 3.3.4. Pharma Companies
    • 3.3.5. Medical Devices Companies
    • 3.3.6. Payers
    • 3.3.7. Governments
    • 3.3.8. Software Developers

4.0. BIG DATA HEALTHCARE BUSINESS MODELS AND COMPANIES

  • 4.1. GENOMICS RESEARCH
    • 4.1.1. Important Factors for Genomic Research Solutions
      • 4.1.1.1. Long Term Storage
      • 4.1.1.2. Strong Processing Power
    • 4.1.2. Key Players and Solutions
      • 4.1.2.1. Genome Health Solutions
      • 4.1.2.2. GNS Healthcare
  • 4.2. HEALTHCARE BIG DATA ANALYTICS
    • 4.2.1. Important Factors for Healthcare Data Warehousing Solutions
      • 4.2.1.1. Cost
      • 4.2.1.2. Flexible Data Operations
      • 4.2.1.3. High Quality Reporting Service
      • 4.2.1.4. Administration
      • 4.2.1.5. Easier Maintenance
    • 4.2.2. Key Players and Solutions
      • 4.2.2.1. IBM
        • 4.2.2.1.1. IBM Netezza
      • 4.2.2.2. Oracle
        • 4.2.2.2.1. Oracle Healthcare Data Warehousing Foundation
      • 4.2.2.3. Zanett
        • 4.2.2.3.1. The Zanett Real Enterprise Value (REV™)
      • 4.2.2.4. Explorys
        • 4.2.2.4.1. Explorys platform
      • 4.2.2.5. Humedica
        • 4.2.2.5.1. Humedica MinedShare
      • 4.2.2.6. Predixion Software
        • 4.2.2.6.1. Predixion Insight™
      • 4.2.2.7. Health Fidelity
        • 4.2.2.7.1. Fidelity Platform
      • 4.2.2.8. Practice Fusion
      • 4.2.2.9. athenahealth, Inc
        • 4.2.2.9.1. Athenahealth Solutions
      • 4.2.2.10. InterSystems
        • 4.2.2.10.1. HealthShare
      • 4.2.2.11. Pentaho
        • 4.2.2.11.1. Pentaho Business Analytics
  • 4.3. FRAUD DETECTION AND MANAGEMENT
    • 4.3.1. Important Factors for Healthcare Fraud Detection and Management Solutions
      • 4.3.1.1. Multiple methods of analysis
      • 4.3.1.2. Social network analysis
    • 4.3.2. Key Players and Solutions
      • 4.3.2.1. Verizon
        • 4.3.2.1.1. Verizon Fraud Management
      • 4.3.2.2. Pervasive
        • 4.3.2.2.1. Pervasive's DataRush
  • 4.4. PERSONALIZED MEDICINE
    • 4.4.1. Important Factors for Personalized Medicine Solution
      • 4.4.1.1. Innovation Protection
      • 4.4.1.2. Enhanced Network Infrastructure
    • 4.4.2. Key Players and Solutions
      • 4.4.2.1. UPMC Health
  • 4.5. MOBILE-BASED HEALTHCARE
    • 4.5.1. Important Factors on Mobile-based Healthcare Solutions
      • 4.5.1.1. Wide Coverage
      • 4.5.1.2. Support for Multi-Platforms
    • 4.5.2. Key Players and Solutions
      • 4.5.2.1. Humetrix's iBlueButton
      • 4.5.2.2. Sproxil Inc.
      • 4.5.2.3. Welldoc
      • 4.5.2.4. ZEO, Inc

5.0. FUTURE OUTLOOK

  • 5.1. MORE RESEARCH FOR BIG DATA ANALYTICS
  • 5.2. MORE TOWARDS PERSONALIZED MEDICINE
  • 5.3. POTENTIAL TO PREDICT - AND HOPEFULLY THEN PREVENT - DISEASE
  • 5.4. MORE ANALYTICS FOR DOCTORS
  • 5.5. MORE TOWARDS DRUG DISCOVERY

Figures

  • Figure 1 - Expansion of Data
  • Figure 2 - Effectiveness of Critical Data in Decision Making
  • Figure 3 - Big Data Revenue 2012-2017
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