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Big Data in Healthcare

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.

Big Data in Healthcare 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.

Target Audience:

  • Big Data application developers
  • Telecommunications companies
  • Big data management companies
  • Healthcare institutions of all types
  • Investors in Big Data infrastructure


General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.


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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