How Healthcare Payers can breakdown their Data Silos

According to the U.S. Census Bureau in 2020, 91.4% of people had health insurance, whether it was private or public.

That is approximately 300 million customers and exponentially more data that Healthcare Payers had an opportunity to accumulate. That’s a lot of data! And, as we are all witnessing, legacy data platforms are buckling under the pressure to support such massive workloads, causing internal processes to slow down to a point where organizations are missing out on valuable insights that can improve member experiences and quality of care.

Frustrated with missed opportunities, departments within these organizations are taking data management into their own hands creating copious amounts of data silos. Data silos within Healthcare Payer organizations hinders collaboration and can cause serious downstream issues affecting multiple departments such as enrollment, claims and reimbursement.

This data fragmentation stops the Payer’s ability to get a 360° view of their members, making it difficult to provide a more personalized experience. If the practice of data silos isn’t properly addressed, it can:

  • Hurt business outcomes by draining Payers’ premium pools and investments
  • Curb the innovation of new technologies (Artificial Intelligence & Machine Learning) and approaches to care management
  • Exhaust valuable resources like time and money by wasting funds on expensive technology that provide little ROI

More recently, Payers have begun adopting an analytics-based methodology to support the transition from fee-for-service (FFS) to value-based care (VBC). However, if the data needed to produce a full picture from these analysis tools is siloed in disparate places with different levels of permissions and data governance, reports can quickly become unreliable and lose value.

To gain full value of your data assets there must be a plan in place for data consolidation and elimination of data silos. This means taking effective steps toward the digital transformation of your current data management process.

A common misnomer is the idea that newer and flashier technology will somehow magically solve all data management problems. Unfortunately, we’ve seen many organizations try and fail with this method. And while a technology solution is a good start, our experience working with insurance companies has taught us that there needs to be a more holistic approach that combines data governance and management with people and processes.

What do I mean by this? Well, for the data and technology component, all data must be centralized using modern cloud-based data warehousing techniques. This will allow for all types of data, regardless of its source or format, to be housed securely in a single repository devoid of any capacity issues. Also, data governance will allow clear and concise data traceability and definitions. Only when data is unified can an organization enjoy the value of a single version of truth.

Regarding the people and processes component, key stakeholders must be convinced that this change isn’t just for the sake of change. They must be presented proof of concepts, a governance model and a detailed deployment roadmap that will confirm the benefits of such an initiative. This will give them the confidence that any short-term failures will give way to long-term successes.

By utilizing proper data management policies and program designs, Payer organizations will break down data silos allowing them to have a stronger understanding of their member's journey from claims to payout. It will also improve alignment of reimbursements and care outcomes.

Eliminating siloed data has the power to spark innovation that can help reduce costs, lower readmission rates, and improving quality scores. But, more importantly, it can build a healthier world. Reach out now and speak to one of our Healthcare experts for a quick discovery session. 

Written by Mike Sargo


Co-Founder & Chief Data and Analytics Officer at Data Ideology


Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.

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