Big Data in Healthcare Is Promising, But is It Worth the Cost, Effort?

June 6, 2014

Big data might lead to cost-savings in healthcare, but it is very costly to begin with.

Recently, while many have been banking on the Affordable Care Act's (ACA's) projected ability to lower the cost of healthcare, another cost-saving industry shift has been playing out behind the scenes: Big data. 

The Big data phenomenon has promised the healthcare industry superior clinical results and $300 billion to $450 billion in savings, according to global management consulting firm McKinsey & Company. 

From meaningful use and the HITECH Act, to the implementation of ICD-10, to venture capitalists pushing the latest EHR product, the digitization of healthcare systems and medical documentation will inevitably become the standard in healthcare. 

So far, most of this data has improved healthcare administration in the form of leaner budgets, less employee idle time, improved revenue cycle management, and better fraud and abuse prevention.  Data is also poised to increase cost transparency - with side-by-side cost comparisons and/or side-by-side comparisons of services and devices and their success rates -and may even replace more traditional methods of clinical research in the future. For example, upon assessing its own data, Kaiser Permanente’s Southern California hospital reported a 26 percent lower mortality rate compared to other hospitals in the region.

While we are counting on data to improve patient outcomes in the years to come, patients today may be paying the price.  Over the last two years, studies funded by the Agency for Healthcare Research and Quality (AHRQ) and the American Journal of Emergency Medicine showed an average of 30 percent to 40 percent of a physician’s time is spent in front of a computer screen. The same studies report that no one looks at 15 percent to 30 percent of this data. 

Another study conducted by the College of Health Information Management Executives reports that most hospital CIOs recognize the value of big data collection throughout their institutions, but lack the knowledge and resources to effectively analyze it and subsequently develop and implement clinical and cost saving solutions. 

Extracting and analyzing all of this information requires a skill set that healthcare providers do not have, so expensive third-party companies are paid to analyze the data. The National Institutes of Health alone has committed $96 million to fund centers that will analyze and interpret collected information, and the industry is expected to grow. 

This begs the question: Are we merely shifting the cost of healthcare to the cost of data analytics?

In addition to cost, there are some other caveats.  This new information, once extrapolated by a third party, can be used in any way the healthcare provider chooses.  For example, favorable stats on a given diagnostic procedure can be touted to the public to increase business, raising the cost of healthcare where it might otherwise be unnecessary. 

Also as with ACA initiatives, physicians may be slow to participate.  Treatment developed from statistics derived from big data may conflict with an individual provider’s current practice, philosophy, or judgment, rendering the process of big data analytics a very expensive exercise in futility. 

In light of these, and other, obstacles, we hope that the effort being put forth now is commensurate with the solutions that big data promises in the future.  Otherwise, we will have inadvertently created a generation of healthcare providers who are also glorified data entry clerks.