Big Data in Healthcare
Big Data in Healthcare
Product Code:
1200000063
Publication Date:
Apr 2013
Publisher:
Mind Commerce Publishing
Pages:
45
  • Executive Summary
  • Table of Contents
  • List of Tables
  • List of Figures
  • Companies covered

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 Content

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
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
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
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
 

List Of Tables
List Of Figures

Figure 1 - Expansion of Data        7
Figure 2 - Effectiveness of Critical Data in Decision Making            9
Figure 3 - Big Data Revenue 2012-2017   10

Companies Covered
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