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Data Analytics in Healthcare: What’s the Holdup?

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The field of big data analytics is booming. No surprise there. For years now, we’ve been hearing case after case of organizations in a range of industries—from auto manufacturing and high-tech to retail and social media—successfully leveraging analytics to monetize complex data sets, gain competitive advantage and spur innovation.

What is surprising, however, is that the healthcare sector has been so slow to jump on the big data bandwagon. In the face of so much innovation and success among other industries, why hasn’t healthcare done more to leverage data analytics?

It’s definitely not a question of having enough data. In fact, hospitals have more data today than they know what to do with.

So, if it’s none of the above, what’s the holdup? What’s impeding healthcare’s fast and furious adoption of big data analytics?

It’s not because there’s nothing to gain. Quite the opposite. Locked inside these data sets is information that can be used to elevate quality of care, reduce costs—and save lives. The stakes for healthcare are about as big as they get. According to a report by McKinsey & Company, big data analytics has the potential to enable more than $300 billion in savings per year in U.S. healthcare. And that’s just the monetary side of the equation. Perhaps Doug Fridsma, M.D., Ph.D., president and CEO of the American Medical Informatics Association, put it best when he said, “The opportunity to use this technology to benefit mankind is an advantage that not a lot of other industries have.”

It’s not due to a lack of interest among leadership, either. In fact, according to a 2014 GE and Accenture survey, nearly 90% of c-level healthcare executives reported analytics as the key to their future market shares.

Will healthcare ever be able to leverage its goldmine of data to achieve transformative clinical, organizational and financial outcomes?

Here are two major obstacles:

LACK OF DATA STANDARDIZATION

Healthcare data is all over the place. It’s collected both manually and electronically, structured and unstructured. It comes from hospitals, clinics, insurance companies and patients. It’s hand written by physicians and pharmacists. Collected from monitoring devices, images and scans. Interpreted and recorded by nurses and staff. It comes from patients, providers and payors—and even from social media sites. You get the point. The diversity of healthcare data, both in the way it’s collected and how it’s stored (not to mention the massive volume) makes it uniquely difficult to access and manage.

LACK OF HUMAN RESOURCES

The nation as a whole is suffering from an extreme shortage of analytics experts and data scientists. According to McKinsey Global, the U.S. could face a shortage of between 140,000 to 190,000 data analytics experts by 2018. For companies looking to hire these expert human resources, competition is fierce.

For hospitals and health systems, in particular, the challenge to find qualified data professionals goes even deeper. That’s because to effectively manage healthcare data, the analysts must have expertise in EMR systems and other healthcare technology. And in order to effectively uncover trends in the data and extract actionable insights, they must also have broad knowledge of the healthcare system. For example, they must be familiar with issues surrounding everything from clinical workflow and revenue cycle to regulatory requirements, incentive programs and changing healthcare models.

In fact, according to a 2014 GE and Accenture survey, nearly 90% of c-level healthcare executives reported analytics as the key to their future market shares.

So, what’s the outlook? Will healthcare ever be able to leverage its goldmine of data to achieve transformative clinical, organizational and financial outcomes?

Advances and innovations in technology—such as analytics software and cloud storage solutions as well as the emerging field of Analytics as a Service—are making it easier (or at least possible!) to harness large, diverse sets of structured and unstructured data. To fix the root problem of unwieldy data, however, will require major collaborative efforts from all healthcare stakeholders to improve data sharing and data standardization. Some nascent efforts are finally emerging on this front, but there is much more still to be done.

At the same time, data analytics has become a hot market. As a result, more and more people are getting trained in the field. Still, it’s going to take many years to build up the expert human resources needed to fill the growing demand. Those organizations that do manage to acquire the analytics experts they need, however, will benefit from quick and lasting competitive advantage.

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