Can You Trust the Integrity of Your Healthcare Data?

Posted by Jeff Gasser on January 23, 2020

When making decisions for the health of a population, it's essential to have analytics you can rely on

Data quality blog image

When I was fresh out of college, I entered the workforce as a Certified Public Accountant (CPA) at a local firm in Tampa, Florida. For obvious reasons, the accounting profession emphasizes the importance of quality control. Organizations like the AICPA issue Statements on Quality Control Standards (SQCSs) and set auditing standards for private companies and nonprofit organizations, as well as federal, state, and local governments. Correct numbers are a "must," and accuracy is paramount.  

During my employment at the Tampa firm, it was made clear to me that nothing ever went out the door before being reviewed by at least two other people. In those days, everything was done by hand, and my work was always reviewed first by a man named Manuel Carmona. If I had made a mistake, Manuel would attach a paper clip next to the incorrect number. He never told me what my mistake had been—that was up to me to figure out. I'd make the edits, and once all the paper clips were gone, I'd run it through a final review with a second person. It is a simple principle—the person who does the work can never be the person who reviews it. Expecting someone to catch their own mistakes is a risky business, one all too often fraught with error.

This brings me to the health analytics business of today. The same rigorous level of quality control should be applied to this part of the industry. With the large number of data inputs and the level and type of calculations being computed in our business, there's plenty of room for error and misinterpretation. There are often dozens of data feeds that come from a variety of sources, and there are various people who work on ingesting that data. In addition to that, the cost of mistakes is high, often making the difference in whether the right decision is made or not.

The question is, who is performing quality control on this work? Who is paper-clipping the mistakes? If data quality is important to you (and I would hope that it is), you need to know the answers to these questions. What is the point of analyzing the wrong numbers? In the absence of an organization that issues standards for this sector, you must be the one responsible for ensuring that your vendor has adequate quality controls in place.

Have you asked your vendor(s) how they maintain quality standards and what types of processes they have in place? One simple way to check the level of importance they place on quality control is by visiting their company's LinkedIn page and pulling up a list of their employees. You should be able to find several current employees with a "QC" title next to their name. If you do, you are likely in decent shape. If you don’t, it might be wise to dig a little deeper to understand how much you can truly trust the accuracy of your data. 

At Deerwalk, quality control employees make up approximately 25% of our 121-person Data Operations team. We also have defined quality control processes that we follow during every new client implementation and regular refreshes of the data. We perform rigorous testing using control standards as well as testing to ensure the data holds up against itself and reported numbers fall within expected ranges. Once the data surpasses these tests and can be processed without failing any predetermined thresholds, we communicate any potential "warning" areas to the client. This ensures quality control and allows everyone to rest assured that they are making instrumental planning and strategic decisions using data they can trust.


Schedule a demo to learn more about how to use Deerwalk Plan Analytics and the latest features.


Subscribe to our blog or follow us on Facebook, Twitter or LinkedIn to join the conversation.