A confidence you can route on
You've got a hundred thousand addresses to reconcile. Two databases, the same clinics and providers scattered across both, each one spelled a dozen ways none.
4 posts tagged with "Record matching"
The geocode-first record matcher — blocking, scoring, clustering, and entity resolution.
View All TagsThe provider registry meets the Universal Service Fund
Three public datasets land on your desk. The national provider registry — NPPES, every NPI in the country. A federal telecom-funding file from the FCC's Rural Health Care program, one part of the Universal Service Fund. A state list of licensed nursing facilities from Texas HHSC. You want to know which records describe the same provider, and not one of the three shares an identifier with the other two. The NPI is internal to NPPES. The funding file keys on its own SPIN. The state list has its own facility ID. There's no crosswalk, because none of the three publishers ever built one.
Same building. Different company. Now what?
Suppose you have a set of records and no key to join them on. A clinic appears in the federal provider registry, again in a state licensing export, and a third time in a funding-program spreadsheet that someone typed by hand. The files share no identifier. The provider number is internal to one publisher, and the facility ID is internal to another. You must judge which records describe the same place for each pair, repeated a few million times.
Match where it is rather than how it's spelled
Here are two addresses. Tell me if they're the same place.