Take a second to think over the last year of your life – or your family’s life – from a medical perspective. What doctors did you see? Did you go to your annual physical? Get a COVID-19 test? How about a vaccine? When did you last swing by the pharmacy, or have some routine labs and tests performed? Where did you go for all of that care? Were all the providers in the same health system? Using the same kind of electronic health record (EHR)?
Now imagine that you have to reconstruct that story not only for yourself, but for every patient in the state, or those who share a particular diagnosis. For organizations like health information exchanges (HIEs), clinical data registries (CDRs), and integrated delivery networks (IDNs), that’s exactly the job at hand. And, they’re tasked not just with finding all of this information, but also with aggregating terabytes of clinical documentation in a smart and organized way.
This means that in order to ensure the quality of clinical data, another step – known as data normalization – is needed before any analytics can be performed.
When it comes to this type of patient data standardization, working with a normalization partner can be a big help. But not all solutions are created equal. Having a clinical terminology layer built into any normalization tool helps ensure that codes and terms from different systems are harmonized, which allows for more consistent, high-quality results.
Download our latest insight brief, Enhancing data quality initiatives across the health IT ecosystem, for a closer look at how normalization and terminology go hand-in-hand.