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Big Data in Healthcare 2015 - 2020

1.0 EXECUTIVE SUMMARY
2.0 INTRODUCTION
2.1 PERSONAL HEALTH CARE EXPENDITURES
2.2 US GOVERNMENT SPENDING ON HEALTHCARE 2010 - 2020
2.3 US HEALTHCARE BUDGET ALLOCATION IN 2015
3.0 BIG DATA IN HEALTHCARE
3.1 BIG DATA AS BASIS FOR INSIGHTFUL ACTION
3.2 CLINICAL AND ADVANCED ANALYTICS
3.3 STEPS TO BECOMING A DATA-DRIVEN HEALTHCARE ORGANIZATION
3.3.1 Determine Quality Metrics
3.3.2 Data source Integration
3.3.3 Data Security Management
3.4 UNSTRUCTURED DATA IN HEALTHCARE
3.4.1 Comprehensive Healthcare Systems
3.4.2 Improved Collaboration among Key Players
3.4.3 Efficient Access to Healthcare
3.4.4 Healthcare and Big Data Treatment
3.5 ADVANTAGES OF MANAGING BIG DATA IN HEALTHCARE
3.5.1 Big Data for earlier Disease Detection
3.5.2 Big Data for Fraud Detection
3.5.3 Healthcare as Vulnerable Target
3.5.4 Big Data defers Prescription Abuse
3.5.5 Big Data for Precision Medicine
3.5.6 Customized Healthcare
3.5.7 Population Health Management
4.0 IMPACT OF TRENDS
4.1 NEED TO LEVERAGE BIG DATA
4.1.1 Data Government Framework
4.1.2 Healthcare Provider Collaboration
4.1.3 Tailored Solutions
5.0 BIG DATA HEALTH CARE SOLUTIONS
5.1 DUE NORTH ANALYTICS
5.2 EXPLORYS
5.3 HUMEDICA
5.4 INTERSYSTEMS
5.5 PERVASIVE
5.6 CLINICAL QUERY
5.7 GNS HEALTHCARE
5.8 OMEDARX
5.9 TRUVEN HEALTH ANALYTICS
5.10 SOGETI HEALTHCARE
6.0 FUTURE OUTLOOK
6.1 MORE RESEARCH BIG DATA ANALYTICS R&D
6.2 MORE TOWARDS PERSONALIZED MEDICINE
6.3 POTENTIAL TO PREDICT AND PREVENT DISEASE
6.4 MORE ANALYTICS FOR DOCTORS
6.5 MORE TOWARDS DRUG DISCOVERY
7.0 CONCLUSIONS
Tables
Table 1: Personal Health Care Expenditures by Source of Funds 2015 - 2020
Table 2: Government Spending on Healthcare in United States 2010 - 2020
Table 3: US Medical Health Care Allocation in 2015
Table 4: Platforms for Big Data in Healthcare
Table 5: Due North Analytics
Table 6: Explorys
Table 7: Humedica
Table 8: InterSystems
Table 9: Pervasive
Table 10: Clinical Query
Table 11: GNS Healthcare
Table 12: OmedaRX
Table 13: TRUVEN Health Analytics
Table 14: Sogeti Healthcare
Figures
Figure 1: US Healthcare Spending 2010 - 2020
Figure 2: US Healthcare Budget Allocation 2015
Figure 3: Conceptual Framework of Big Data in Healthcare Analytics
Figure 4: Healthcare and Big Data Leverage 2015 - 2020
Figure 5: Healthcare Big Data Sources
Figure 6: Healthcare versus Fraud 2015 - 2020
Figure 7: Healthcare Fraud 2015 - 2020
Figure 8: Overdose Deaths from Select Prescription and Illicit Drugs 2010 - 2020
Figure 9: Overdose Death Mitigation via Big Data 2015 - 2020

Big Data in Healthcare 2015 - 2020

The global healthcare industry faces three key challenges. First is the requirement for cost reduction without jeopardizing the quality and management of healthcare services for everyone. The second challenge involves the significant increase in healthcare concerns, ranging from rising rate of chronic diseases, aging population and the dynamic consumer demands, including accessibility and health care expectations. The third is the shifting paradigm in healthcare characterized by reactive healthcare system to proactive health care system.

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 research evaluates the healthcare landscape and the factors which are facilitating the need for incorporation of big data technologies and solutions. This research also evaluates the need for a data-driven healthcare organization as a new paradigm to determine quality metrics, data source integration, and data security management. The report assesses big data relative to organizational goals for achieving a comprehensive healthcare system.

The report also focuses on the opportunities to leverage big data in healthcare for improved healthcare operations in terms of cost reduction including fraud detection. Mind Commerce evaluates key players including Explorys, Humedica, InterSystems, Pervasive, Clinical Query, GNS Healthcare, OmedaRX, and Sogeti Healthcare. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:

Data analytics companies
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.


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