The promises offered by Machine Learning, Population Health, Analytics, and Care Management platforms are numerous and compelling, or that’s what health IT marketers want you to believe. However, all of these tools require a substrate of good data on which to operate. Developers, purchasers, and implementers frequently overlook the effort required to clean and semantically normalize data prior to using these tools, and are frequently disappointed when “garbage in” leads to “garbage out.”  In this Learning Showcase, IMO will share results on the normalization of clinical data using a clinical interface terminology by contrast to standard code-to-code cross-maps, for the purpose of quality reporting and risk identification.