Top Trends for Big Data in Healthcare

Sets of facts and figures may not seem like much to the naked eye—but big data in healthcare is providing organizations with a roadmap for improving patient outcomes , improving operations and opening up new healthcare models.

“It’s a foundation for a lot of things,” said Julius Bogdan, North American vice president of analytics for HIMSS. “Real-time analytics means we’ve begun rethinking how we’ve done things traditionally as data is becoming much more complex and growing exponentially.”

When it comes to operations, big data in healthcare helps with analyzing workforce needs, financials and directing resources. For patient care, clinicians can provide better care for high-risk patients through tracking symptoms and creating preventive care programs.

Bogdan noted data is also driving innovation in healthcare. “With the ability to analyze more data, it opens up the possibility for innovation at a scale we have not had in the past.”

One example of this was the U.S. Centers for Medicaid and Medicare Services finding or preventing $210 million in improper payments through its fraud prevention system.

There is no denying that data will continue to be an important aspect of healthcare moving forward. Here are some of the top trends we’ve identified for big data in healthcare.

Traditionally, big data has been categorized by three Vs: volume, velocity and variety, but those categories expanded to five to accommodate additional capabilities. It now also addresses value and veracity.

Here’s how each of the “Vs” is impacting healthcare today:

With the vast amounts of data coming from a variety of sources, data lakes are key to storing and sorting big data.

Data lakes allow multiple points of access and collection but maintain original raw data. “It’s a structure that allows you to capture data in its raw form and then process that data in a variety of ways,” Bogdan explained. “It’s starting to pick up steam in healthcare because we have a lot of data that exists outside of the EHR.”

Other types of data include insurance claims, imaging, socio-economic demographics, genetic and environmental.

“Without a construct to allow us to capture that data and analyze it, we’d be missing a big chunk of our ability to treat patients,” Bogdan noted.

One of the biggest trends for big data in healthcare is its use for predictive analytics. Healthcare organizations are using this information to make decisions not only for patient care, but for their operations throughout their system.

“There is huge cost-saving potential and improving outcomes for patients, that’s why it’s such a big topic for healthcare now,” Bogdan said.

Predictive analytics are being used by clinicians, finance departments, human resources, and almost every team within a healthcare organization.

Some examples of how hospital systems might use predictive analytics include:

In the past, a lot of these decisions would be done as a reaction to what has already happened. However, predictive analytics provides a different approach.

“Organizations are trying to move toward planning based on what’s going to happen,” Bogdan said.

An important topic in healthcare right now is equality of care—and data has a role in inclusive solutions.  One of the benefits of big data in healthcare is that it is diverse, coming from multiple sources and so many different places. And there is so much of it.

Big data provides more information, including socioeconomic and social determinants, to inform good health.

“These are things that impact health but are not in control of the healthcare system,” Bogdan said. “This data allows us to view the patient more holistically and come up with a treatment path that will have more of an impact.”

Clinical teams will be able to predict whether patients are at risk for certain things such as lack of food and security, and connect them with resources to help.

“Instead of just focusing on clinical, they can find ways to help the patient’s wellness and not just treat their symptoms,” Bogdan said.

Big data in healthcare is the foundation of effective AI, a technology growing rapidly within the industry.

“In order for AI to be effective, it needs as much data as you can throw at it,” Bogdan said. “The biggest challenges in healthcare are fragmented data and data quality.”

He noted that big data allows healthcare organizations to create clean and aggregated data pipelines for AI developers.

“With that foundation layer of data in hand, you can create some pretty impactive algorithms,” Bogdan said.

One of the hurdles, however, centers around technology. Not all healthcare systems have the means to collect and process complex data yet. According to Bogdan, interoperability between healthcare systems and integrations of different types of healthcare data are areas for improvement when it comes to big data in healthcare.