The matching algorithm that is utilized for the health information exchange is very conservative and errs on the side of not matching. First and foremost, INPC is a clinical tool. It is utilized by providers to review more complete past histories on their patients. Because of the clinical mission, INPC’s algorithm is conservative to avoid negative clinical outcomes. For example, you would not want to match “John Smith” with diabetes to “John Smith” who does not have diabetes because it would likely alter how a provider treats that patient. If the algorithm is not certain it is the same person, it creates a new ID. The matching process does continue to run, so if a new piece of information becomes available that convinces the algorithm it is the same person, it merges the IDs. However, we suspect there is a decent number of individuals who should be matched, but are not.
This also explains why some people may only have limited clinical data — they might have more, but it hasn’t matched for some reason. If increased match rates is important for your research project (and your research can tolerate a more aggressive matching strategy), there are other matching strategies that RDS can employ.
Ultimately, if data depth is important, it will be critical to define your cohort well up front. For more about how to do that, check out the short video embedded on this page.