What are the minimum matching criteria recommended for reliable demographic based patient matching? When matching patients based on demographic data are there any recommendations on what fields should match for the patient to be the "Same Patient"?

I know the algorithms will be different for different implementations, I'm just curious if there are any best practices or recommendations around this process.

First Name
Last Name
Date of Birth
SSN
Address
City
State
Zip


etc?
There's this great essay (in spanish, sorry) written by Pablo Pazos, a CS Engineer from Uruguay who has been working on Healthcare IT since 2006 and has made some great contributions to the field, in which he describes an algorithm for doing this.

You can run the article through a translator, but the gist of it is that the basic info to determine a person's identity are their given and family names (both from father and mother), sex, and date of birth. Interestingly enough, he specifically excludes id numbers like SSN from his identity matching algorithms, since "any kind of identifier is NOT part of his identity" (I guess this point could be debatable, though). Also, he excludes attributes like street address, phone numbers, etc. since they aren't really related with the identity of someone, they aren't associated with "who someone actually is".

Also, he assigns different "weights" to each of the former attributes, like this:


First name: 17.5%
Middle name: 17.5%
Family name (father): 17.5%
Family name (mother): 17.5%
Sex: 10%
DOB: 20%


With the matches found on every one of these attributes, he describes a methodology to obtain a composite "concordancy match index" with which comparisons between records can be possible. Also, "partial" matches on the name attributes are possible by using algorithms like Levenshtein's distance.

Good read, IMO. Sorry it's in spanish, but I hope I was able to convey its main ideas.