I agree with Christian. Based on your examples, it looks like this field is not using any controlled vocabulary – that new strings are added w/o any tie to a coding system. If so, then you have it exactly right: “powering through 1,700 string searches and adjudication among potential SNOMED matches”. Of course you should do a distribution of counts over those 1,700 strings and perhaps pick those that cover 90% of the uses and perhaps just not bother with the very long tail that the last 10% will represent. I bet a small fraction of the 1,700 strings are in the top 80%-90%
Chris Knoll posted his advice how to do this, which was contrary to what I was telling my team to do (use source_to_concept_map table). But after reading Chris’ approach, his approach is much cleaner so I had to eat crow with the Colorado Health Data Compass team… Chris’ link below.