Thank you @ericaVoss @mdewilde @Azza_Shoaibi for sharing this valuable lesson. A couple big ‘a has’ from me from this exercise: 1- the tremendous value of getting the OHDSI community to participate in running ConceptPrevalence. If we could get all data partners to share record counts of concepts in their CDM, then that collective community resource could be used in tools like PHOEBE and ATLAS during the cohort development process to ensure that we don’t miss concepts that are relevant which are used across our network (but we didn’t know). 2- in absence of OHDSI community-wide ConceptPrevalence data, my next favorite ‘trick’ is to look at source code vocabulary mappings into standards. And indeed, if there are source vocab concepts, it’s good to check on them and feel like you’ve got good coverage of the source (as @ericaVoss highlighted here, ICD9CM and ICD10CM). But, for all databases that map local source codes that AREN’T in the OHDSI vocabularies straight into the standard concept space (in this case, like IPCI mapping their ICPC codes direct to SNOMED), you can’t rely on source->standard mappings. So, remember those standard concepts with 0 counts and 0 source code mapped could still be floating around somewhere in the community.