Data Normalization

Unlocking the Value of Your Data

In today’s healthcare world, patient information is spread across entire communities—each with multiple IT systems. While syntactic standards such as HL7 define messaging structure and aid in communication among disparate healthcare IT systems, there is no single, universally accepted standard for the language in the message. For an integrated healthcare system to "understand" clinical data, the meaning of the data must be unambiguous. Consider the case below, three data feeds from three different sources, all using different terminology content sets.

Clinical Data Normalization

A semantically normalized information model is essential for the use of evidence-based care protocols, quality improvement, and population health management, as well as for HIEs, ACOs, PCMHs, pay-for-performance programs, data warehousing, and other forward-looking initiatives.

How do I measure performance,
gain insights, and support
enterprise interoperability with
information that is semantically
fragmented?