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A Hole in Healthcare Analytics: Where’s All the Talent?

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August 3, 2017
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Big Data Is Improving Healthcare, but Progress Slows Without Analytics Expertise

Big Data Has Made a Massive Impact on Healthcare, But Securing Big Data Talent Isn’t Easy.

Big data continues to make a serious impact on the healthcare industry, and information is coming from a diverse range of directions: internet-connected medical devices and IoT initiatives, research and development data collected by pharmaceutical companies, clinical trials data, and more, McKinsey & Company reported. Big data analytics in healthcare could potentially predict epidemics, help patients avoid preventable diseases, reduce treatment costs, and improve quality of life overall. Full-scale implementation and usage are still in process, but big data has definitely become a major initiative.

While big data and healthcare analytics are gaining steam, talent remains an issue. According to HealthITAnalytics.com, 50% of organizations are looking for data science experts to support, lead, and provide expertise, but haven’t been able to bring on the staff they need to get big data analytics programs up and running.

Why Big Data Talent is So Hard to Find

So why is big data talent so hard to find in healthcare? It could be a combination of factors:

  • Qualified data scientists are few and far between as it is.

    Data scientists are in demand throughout every vertical. McKinsey predicted that by 2018, the number of data science jobs in the U.S. will exceed 490,000, but there will be fewer than 200,000 available data scientists to fill these positions. Simply put, there’s a lot of work to do, and not enough people to do it.

  • Big data professionals with healthcare-specific experience are in incredibly high demand.

    There’s already a shortage of big data scientists overall—finding big data scientists with healthcare expertise, or at the very least, experience, is a tall order. It’s not enough to know big data. In the healthcare industry, organizations are looking for a unique variety of an already unique skillset. Expertise with data mining and analysis is critical, but so is understanding its context. Healthcare nomenclature is industry-specific, and data types aren’t the same as other industries. The ideal data analytics professional for a hospital will have to be familiar with healthcare concepts and know how to interact with clinicians, HealthITAnalytics.com commented.

  • Big data initiatives require advanced technology, but many healthcare organizations run legacy technology.

    Hospitals may desire talent to help make an ongoing big data analytics project successful, but still have legacy systems in place that were born in the mainframe era, HealthITAnalytics.com explained. Data scientists possess advanced skillsets; they’re good at math, possess programming skills, understand business intelligence, and are used to open data APIs and open source software. It’s hard to attract individuals who have developed their professional skills in an innovative environment to a hospital with old technology platforms.

  • 31% of healthcare organizations don’t have a clear picture of where to start with their healthcare analytics initiatives. This makes putting together an analytics department more important and more difficult at the same time.

    Hospitals and providers are facing difficulties with workflows, health information exchange, interoperability, and more. The barriers are making it hard to define a concrete big data analytics program, which makes it hard to bring in experts, even though experts are the key to breaking the barriers and getting programs organized.

What Should Healthcare Do About It?

There’s no easy fix, but there are steps healthcare organizations can take to make positions more attractive and begin to fill the skills gap. It might be an uphill battle for a while, but hospitals and providers have options.

  • If hiring experts from outside of the organization isn’t preferred, there’s always the option of training internal tech personnel. With a solid training program, mentoring options, and an infrastructure that will foster growth, it would be possible to use the expertise of one or two healthcare-seasoned data scientists to build a flourishing big data analytics team.

  • Healthcare organizations are under pressure to put together strong big data analytics programs and strategies, particularly in the face of advancing in front of competition. It sounds counterintuitive, but it will pay (literally) to take the time to carefully plan out how to mine and operationalize actionable insights. If healthcare analytics goals are clear, it will be easier to secure data science and infrastructure architectural expertise that’s needed to support a healthy, enduring program.

Peak 10 completed our most recent healthcare IT study in 2016. Part of our findings defined the following fundamental conclusions related to the ongoing growth of IoT, telemedicine, and big data:

  • Application and device choice are key concerns. CIOs choose to integrate new technologies with caution due to security worries, but often have little control.

  • Devices and applications are producing increasing amounts of data. The majority of organizations are still figuring out what to do with it and how to best leverage it.

  • Big data is emerging as a major initiative with “population health” and connected devices, but implementation & usage is not yet strong.

To review the healthcare IT study findings, visit the Peak 10 Industry Spotlight: Healthcare IT website. We talked with healthcare IT decision makers about their plans for the future, including their thoughts on big data analytics programs.

If your IT team has questions about the infrastructural changes you may need to make to support your big data program, contact us at www.peak10.com/contact-us or (866) 473-2510 to speak with one of our experts.

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