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“Big Data” & the U.S. Healthcare Crisis

How can “Big Data” and cloud-based BI help avert the U.S. healthcare crisis?



Many research studies have shown that increased U.S. healthcare spending and debt–and the decline in the value received per healthcare dollar spent– are having crippling effects on the U.S. economy.

Typically, part of the strategy for attacking very large, complex problems like health care and its effect on the economy includes the use of business intelligence (BI) and analytics to dive deeply into the underlying data, examine it from different perspectives and model various alternative solutions.

However, the amount of healthcare data to be studied–including information related to patient care, prescriptions, disease prevention, clinical research and insurance payments–exists on a scale beyond the capabilities of today’s standard BI and analytical tools.

About Big Data

These types of massive datasets are referred to as “Big Data,” which is defined very simply as datasets that are too large and complex to cost-effectively manage with conventional database systems and analytical tools.  According to Wikipedia, Big Data sizes are a constantly moving target currently ranging from a few dozen terabytes to many petabytes of data in a single data set.  Big Data grows in volume (amount of data), velocity (speed of data in/out) and variety (range of data types and sources).”

The McKinsey Global Institute, in its 2011 report on healthcare, estimates that Big Data, if used effectively by the healthcare industry, could provide a value of $300 billion a year, mostly in the reduction of expenditures. This report describes several of the ways that Big Data can help resolve the U.S. healthcare crisis:

  • Preventing under or over-treatment of patients by combining and analyzing data on treatment costs and outcomes across large geographical and patient demographical datasets. This is also known as comparative effectiveness research (CER).
  • Using predictive analysis on patient data to identify high-risk markers, enabling providers to offer better preventative care and get individuals the resources necessary to make needed lifestyle changes.
  • Identifying, in a timelier manner, R&D clinical trials that are not achieving desired results.  This would create a significant cost savings by shifting funds to drugs and therapies with better efficacy and safety.

Other examples of how faster and better analysis of Big Data might yield tangible benefits to the U.S. healthcare system include:

  • Early detection and isolation of public health threats, such as bird flu, through analysis of hospital data and social media. Data analysis would help identify elevated levels of flu and estimate the severity of the disease in a specific area.
  • Analysis of medical insurance claims data for financial analysis, fraud detection and preferred patient treatment plans.

One of the most effective ways to scale Business Intelligence capabilities to meet the challenges of Big Data in healthcare (and other industries) is by integrating BI with massively scalable cloud computing technologies.  Cloud-based BI can help to:

  • Consolidate disparate data sources in one secure location.
  • Provide access from anywhere to anywhere.
  • Permit rapid, timely and cost effective deployment of additional hardware, such as processing power, memory and storage on an “as needed” basis.

Perhaps most significantly, cloud-based BI enables organizations to procure BI solutions that are pre-configured and integrated with the necessary information technology infrastructure.  These pre-configured solutions reduce the upfront cost and time-to-launch of the BI initiative, enabling organizations to focus their resources on getting the information they need to make faster, more informed decisions.

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