This monthly blog highlights and discuss emerging trends and challenges related to healthcare data and its ever changing life cycle.
By Bob Fuller and Ramu Kalvakuntla
Healthcare organizations manage and analyze patient and member data today in volumes that even ten years ago would have been unimaginable—and will only increase. Wearable tech data, such as from Fitbits and Apple watches, coupled with patient-generated data from apps are primed for integration. And with the desire for interoperability only growing, organizations can anticipate the impending need to process not only their own data, but clinical data as well.
But the architectures in place today are often not sufficient for our changing needs. Systems need to not only process data, but facilitate analysis as well so healthcare organizations can gather insights and make changes to improve patient and member outcomes, increase efficiencies, and reduce costs. This is a tall order for most legacy systems.
Take your average large healthcare organization’s system for example: it likely has multiple data centers, thousands of servers, dozens of business intelligence tools, and multiple data warehouses. Managing a system like this—organizing data, gathering consistent results, implementing successful data governance—is nearly impossible. This is where a modern data architecture is needed.
There are three components of what we refer to as a modern data architecture: consolidating data sources into a data lake, moving to a linear scalable data warehousing solution, and implementing a robust data governance strategy. These steps pave the way for clean, usable data integrated from a variety of sources such as wearables, clinical data, and payer data. Data governance means it stays that way, and a data lake provides access to stakeholders across the organization, not just information technology staff.
Usable data, primed for analysis, is only the starting point. The analysis must result in a heightened awareness of reality—true insight into patient health, organizational processes—and an organization-wide desire to act on that reality. Many refer to this as a “data-driven culture,” a mindset shared by all, allowing data analysis to inform decisions and create competitive edge.
So, what now? Perhaps your organization is still using legacy systems. If so, take stock of your current state, find out how accessible data is, how many teams are pulling reports, what kind of data you can actually find and use. Think about how you could be more efficient or effective; is there a need for a better architecture? What do you expect to see as a result of moving to a modern data architecture?
Or maybe you do have a linear scalable data warehouse, great data governance, and a data lake, but you’re not seeing adoption in use. In this case, maybe what is needed is a cultural change, where each department or team understands how the available data can be leveraged to improve customer experience, risk assessment, or patient care. Regardless of the challenge, there is a solution—and pursuing a modern technical and cultural framework will ensure your organization is ready to use today’s—and tomorrow’s—data.
Bob Fuller is managing partner and Ramu Kalvakuntla is chief technology officer for healthcare at Clarity Insights.