Patient data has a multitude of uses – both at the point of care and for secondary, downstream applications. This is perhaps especially true for information about laboratory tests and prescribed medications.

But when it comes to labs and meds, ensuring accuracy in interoperability is even more challenging than usual. That’s because data about labs and meds is particularly detailed. What’s more, while coding systems – like LOINC^® and RxNORM^® – are focused on standardizing this data, they aren’t universally used. That means ad-hoc solutions like code crosswalks are frequently employed, creating more interoperability challenges.

So, what’s the solution? In our latest white paper, Labs, meds, and data quality: Taming complexity through normalization, we take a look at the specifics of why this data is so tricky to use and how a normalization solution grounded in a foundational clinical terminology can help.