In a world of electronic data exchange and hurried data entry, effective patient de-duplication is the first step to ensuring that records within your system are complete, accurate, and current. While most systems have some form of de-duplication, even the best algorithms can yield thousands or even hundreds of thousands of possible matches that require manual review. It may also be difficult to determine whether your system is merging records that it should not (‘over-deduping’), or not merging records that it ought to be (‘under-deduping’).
Through over a decade of work implementing IT and healthcare systems infrastructure, SSG discovered the challenges faced in accurately matching and de-duplicating critical records. We know that records are sent from different sources, sent at different points in time, and have varying degrees of completeness. We also know the problems with recognizing and identifying gaps in traditional de-duplication algorithms. We’ve seen first hand how it can be a ‘hit-or-miss’ proposition often initiated by chance. These data discrepancies continue to prove difficult for both organizations and the people they serve.