A hole in healthcare analytics: Where’s the talent?

August 3, 2017

Big data is crucial in healthcare IT, but getting the people to run it isn't easy

Big data continues to make a serious impact on the healthcare industry, with internet-connected medical devices and IoT initiatives, research and development data collected by pharmaceutical companies, clinical trials data, and more. Big-data analytics could potentially predict epidemics, help patients avoid preventable diseases, reduce treatment costs and improve quality of life overall. The full-scale implementation and usage of these things are still in process, but big data is a major initiative for most healthcare organizations.

Despite these advances, big-data talent remains an issue. According to, 50 percent of organizations have looked for data science experts to support, lead and provide expertise, but haven't been able to hire the staff they need for big-data analytics programs.

Talent is hard to find
Why is big data talent so hard to find in healthcare? There are several factors at play:

  • Qualified data scientists are few and far between.
    Data scientists are in demand in every industry. McKinsey noted that in 2018, fewer than 200,000 data scientists are available to fill 490,000 data science jobs. There's a lot of work to do, and not enough people to do it.
  • Big data professionals with healthcare-specific experience are in high demand.
    There's already a shortage of big data scientists overall, so it's a tall order to find ones with healthcare expertise, or even experience. In the healthcare industry, organizations need 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 the data types aren't the same as in 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, noted.
  • Big-data initiatives require advanced technology, but many healthcare organizations run legacy technology.
    Many hospitals still have legacy systems in place that were born in the mainframe era. Modern 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 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 both more important and more difficult.
    Hospitals and providers are facing difficulties with workflows, health information exchange, interoperability and more. These barriers make it hard to define a concrete big data analytics program, which makes it hard to bring in experts ‑ even though those experts are the key to breaking the barriers and getting programs organized.

What can your hospital do about it?
There's no easy fix, but there are steps healthcare organizations can take to make these 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 aren't available, 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 can 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 big data strategies, particularly in the face of advancing competition. It sounds counterintuitive, but it will pay (literally) to take the time to carefully plan out how to mine and operationalize your actionable insights. If healthcare analytics goals are clear, it will be easier to secure the data science and infrastructure architectural expertise that's needed to support a healthy, enduring program.

At Flexential, we updated our healthcare study in 2018, with these conclusions related to the ongoing growth of IoT, telemedicine and big data:

  • Application and device choice are key concerns. Because of security worries, CIOs choose to integrate new technologies with caution, but often have little control over these things.
  • Devices and applications are producing increasing amounts of data. The majority of organizations are still figuring out how best to leverage it.
  • Big data is emerging as a major initiative with “population health” and connected devices, but implementation and usage is not yet strong.

If your IT team has questions about the infrastructural changes you may need to make to support your big data program, contact us at to speak with one of our experts.