Big Data Analytics Use Cases: R/X For Healthcare

In the US, more money is spent on healthcare – about $3.2 trillion last year – than in any other discrete segment of the economy. By some estimates, that is about $600 billion more than should be spent, given the country’s size and wealth. These costs are pummeling consumers and their employers around the world. This year the global costs of employee healthcare benefits will rise more than 9%, according to recent research. In the US the increase will be 15%. Thus the pressure on healthcare providers to reduce costs is enormous. What’s more, these providers are also tasked with improving patient care and treatment outcomes.

Healthcare providers have made strides in collecting and sharing massive amounts of data in electronic health records and health information exchanges in an effort to streamline patient care and reduce costs. Until recently, these efforts had little impact on costs, or on patient outcomes for that matter.

What had been lacking is the analytics piece of the equation, the missing link to making sense of all this and other data. By one conservative estimate, applying Big Data analytics on a health system-wide basis could reduce healthcare spending in the US by a staggering $300-$450 billion annually. Moreover, there is every reason to believe that patient outcomes will improve as well, perhaps substantially.

Here below are some of the more common use cases for Hadoop and Big Data analytics in healthcare. It is worth noting that in the healthcare environment, more than three quarters of the data available for use is unstructured data. It comes from an ever-growing list of sources, including thousands of devices and sensors, medical staff notes, lab tests, imaging reports, and many outside sources of medical information.

This is data that is the bread and butter for the Hadoop platform. It isn’t just the unstructured nature of most healthcare data that matches up well with Hadoop. It is also the sheer enormity of the data volumes from these and other sources, which would easily overwhelm traditional analytics platforms. Hadoop is capable of acquiring and storing gigantic masses of any kind of data. And Hadoop’s ability to run on inexpensive but very powerful commodity hardware allow the platform to handle just about any number of tasks concurrently.

In the near term, this is arguably the most important use case for Big Data analytics on Hadoop, mainly owing to the tremendous effort in the past decade to collect and store this data. Now it needs be analyzed and broadly shared among clinicians anywhere and everywhere.

The potential cost savings are massive, deriving from benefits like reducing duplication of tests and reducing the number of patient-doctor visits. With Hadoop and Big Data analytics, clinicians can spot links between different diseases to better predict other diseases that may result, and therefore be prevented in some cases.

The fraud potential is obviously very high in an environment where massive sums of money can pass through multiple providers and payees to pay for services billed by scores of groups, individuals, departments and others – at times using multiple currencies. Fraud is also rife today when it comes to proper determination of eligibility in Medicaid, Medicare, other managed care organizations as well as in employer and private health plans.

Hadoop and the analytics tools available today are particularly adept at analyzing various nuances and anomalies across a variety of inputs, including payment data, voluminous patient records, and benchmark data for multiple common procedures.