Just pull an extract…That's how the conversation always starts.  

It's claims data.  

They're standard transactions.

How hard can it be?

Harder than anyone anticipates.  Why?  Because there are two constants in health care claims data sets.  1)The data will surprise you, and it is never in a good way! 2) You don’t know what you don’t know about your own data. Experts across the industry agree that obtaining a “clean” healthcare claims extract is notoriously difficult.  Because it is the first step in any engagement or implementation, it immediately extends the agreed upon project plan, sometimes by months.  The reasons behind the data inconsistency vary by organization, but there are a few “universal truths”:

1. The data is not centralized.  Data silo’s are the enemy of credible reporting and analysis.  Merger and acquisition activity is at an all-time high in health care as organizations struggle to attain the necessary size to survive financially.  While that creates a lot of opportunity for efficiency in theory, the reality is that the data is not centralized, integration of multiple systems doesn’t occur, and errors and inefficiency abound.
2.  The data is not curated.  Meaning, there is likely code that was written five years ago by a contract programmer as part of an Epic/Cerner upgrade that is now long gone. No one knows what is or is not being dumped from multiple sources into the data warehouse (if there even is one).  It is hard to test for what is missing.  Are there rigorous data ETL and QA processes running constantly to ensure data integrity?  In many health care systems, the answer is no as it just hasn’t been a priority.
3.  The data is lost in translation.  Highly customized systems frequently define business rules in ways that unintentionally create problems.  Maybe there are data feeds coming from two payers which seem to be the same but are not. Or, thinking about a single payer?  There is likely a Blue Cross HMO product, two different Medicare Advantage plans, a PPO product, a legacy Medigap product, national account business and that is just in one state?  What if you straddle a border and that makes it more complex?  Does your data mapping consider all of those as “Blue Cross” or is it more appropriately reported separately?  Is there a parent/child relationship established with the codes so that reporting and analysis can be performed at either or both levels?  Generally, the answer is sometimes.  The problem is that sometimes+inconsistency=inaccurate assumptions, baselines, reporting and analysis.

Having a trusted partner with years of specific data expertise in effective revenue cycle management is the answer to achieving peak business health.

The volume, variety, and velocity of Big Data makes it impossible for today’s enterprise to see hidden opportunities, root causes of systemic problems, or just better ways to get things done. So our analytics separate the signal from the noise. We simplify complex enterprise data and transform it into insights and actionable workflows, and deliver a user experience that helps every knowledge worker perform at a higher level. Our clients also benefit from the collective experience of industry domain leaders who help them achieve the best financial, operational, and clinical outcomes. So when people ask if our solutions make a difference, we have a simple answer. “You’ll see.” To know more, visit www.visiquate.com or contact sales@visiquate.com. 

Interested in learning more? The team at VisiQuate is focusing on how we can help hospitals optimize their revenue cycle management. Visit our Revenue Cycle Playbook for step-by-step plays to help you stay on top of the ever-changing landscape of healthcare revenue cycle, or contact us to schedule a demo.

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