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.

1. 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. 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. Target Audience: Big Data application developers Telecommunications companies Big data
management companies Healthcare institutions of all types Investors in Big Data infrastructure
table Of Contents:
1.0 Executive Summary 5
2.0 Introduction 6
2.1 What Is Big Data? 6
2.2 Big Data Categories 7
2.2.1 Structured Big Data 7
2.2.2 Un-structured Data 7
2.2.3 Semi-structured Data 8
2.3 Why Is It Important? 8
2.3.1 Pattern Discovery 8
2.3.2 Decision Making 8
2.3.3 Process Invention 9
2.3.4 Increasing Revenue 10
2.4 Big Data Growth Drivers 11
2.5 Big Data Technology 11
2.5.1 Sensors 11
2.5.2 Computer Networks 11
2.5.3 Data Storage 11
2.5.4 Cluster Computer Systems 12
2.5.5 Cloud Computing Facilities 12
2.5.6 Data Analysis Algorithms 12
3.0 Big Data In Healthcare 13
Big Data in Healthcare

2. 3.1 Conceptual Challenges 13
3.1.1 Volume 13
3.1.2 Variety 13
3.1.3 Velocity 14
3.2 Practical Challenges 15
3.2.1 Healthcare As A Technology Laggard 15
3.2.2 Integration 15
3.2.3 Security 16
3.2.4 Standards 16
3.2.5 Real-time Processing 16
3.3 Healthcare Stakeholders 17
3.3.1 Patients 17
3.3.2 Providers 17
3.3.3 Researchers 17
3.3.4 Pharma Companies 17
3.3.5 Medical Devices Companies 18
3.3.6 Payers 18
3.3.7 Governments 18
3.3.8 Software Developers 18
4.0 Big Data Healthcare Business Models And Companies 19
4.1 Genomics Research 19
4.1.1 Important Factors For Genomic Research Solutions 19
4.1.1.1 Long Term Storage 19
4.1.1.2 Strong Processing Power 19
4.1.2 Key Players And Solutions 20
4.1.2.1 Genome Health Solutions 20
4.1.2.2 Gns Healthcare 20
4.2 Healthcare Big Data Analytics 21
4.2.1 Important Factors For Healthcare Data Warehousing Solutions 21
4.2.1.1 Cost 21
4.2.1.2 Flexible Data Operations 21
4.2.1.3 High Quality Reporting Service 21
4.2.1.4 Administration 22
4.2.1.5 Easier Maintenance 22
4.2.2 Key Players And Solutions 22
4.2.2.1 Ibm 22
4.2.2.1.1 Ibm Netezza 22
4.2.2.2 Oracle 23
4.2.2.2.1 Oracle Healthcare Data Warehousing Foundation 24
4.2.2.3 Zanett 24
4.2.2.3.1 The Zanett Real Enterprise Value (rev™) 24
Big Data in Healthcare

3. 4.2.2.4 Explorys 25
4.2.2.4.1 Explorys Platform 25
4.2.2.5 Humedica 26
4.2.2.5.1 Humedica Minedshare 26
4.2.2.6 Predixion Software 26
4.2.2.6.1 Predixion Insight™ 27
4.2.2.7 Health Fidelity 27
4.2.2.7.1 Fidelity Platform 28
4.2.2.8 Practice Fusion 28
4.2.2.9 Athenahealth, Inc 29
4.2.2.9.1 Athenahealth Solutions 30
4.2.2.10 intersystems 30
4.2.2.10.1 healthshare 30
4.2.2.11 pentaho 31
4.2.2.11.1 pentaho Business Analytics 31
4.3 Fraud Detection And Management 34
4.3.1 Important Factors For Healthcare Fraud Detection And Management Solutions 34
4.3.1.1 Multiple Methods Of Analysis 34
4.3.1.2 Social Network Analysis 34
4.3.2 Key Players And Solutions 34
4.3.2.1 Verizon 34
4.3.2.1.1 Verizon Fraud Management 35
4.3.2.2 Pervasive 36
4.3.2.2.1 Pervasive's Datarush 36
4.4 Personalized Medicine 37
4.4.1 Important Factors For Personalized Medicine Solution 37
4.4.1.1 Innovation Protection 37
4.4.1.2 Enhanced Network Infrastructure 38
4.4.2 Key Players And Solutions 38
4.4.2.1 Upmc Health 38
4.5 Mobile-based Healthcare 40
4.5.1 Important Factors On Mobile-based Healthcare Solutions 40
4.5.1.1 Wide Coverage 40
4.5.1.2 Support For Multi-platforms 40
4.5.2 Key Players And Solutions 40
4.5.2.1 Humetrix's Ibluebutton 40
4.5.2.2 Sproxil Inc. 41
4.5.2.3 Welldoc 42
4.5.2.4 Zeo, Inc 43
5.0 Future Outlook 44
5.1 More Research For Big Data Analytics 44
Big Data in Healthcare

4. 5.2 More Towards Personalized Medicine 44
5.3 Potential To Predict - And Hopefully Then Prevent - Disease 44
5.4 More Analytics For Doctors 44
5.5 More Towards Drug Discovery 44
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Big Data in Healthcare