Which algorithm is best at identifying the highest percentage of potential duplicates in an MPI?

The probabilistic algorithm is particularly effective in identifying the highest percentage of potential duplicates in a Master Patient Index (MPI). This approach utilizes statistical methods to assess the likelihood that records are duplicates based on the similarities and differences between data fields. By assigning weights to various attributes (like names, dates of birth, and addresses) according to their importance and the likelihood of being erroneous, the probabilistic algorithm can handle data variations and inconsistencies more adeptly than other methods.

For instance, it can account for typographical errors, variations in name spelling, and other discrepancies that may exist in patient records, making it robust in real-world applications where data quality can vary. This results in a higher recall rate, meaning that it successfully identifies more true duplicates than deterministic or rules-based methods, which may rely on exact matches or pre-defined criteria that can overlook ambiguous cases.

The EMPI, or Enterprise Master Patient Index, is more of a system or architecture that may employ various algorithms, including probabilistic and deterministic methods, but it is not an algorithm on its own. Deterministic approaches typically only work well when data quality is high and when fields can be matched exactly, while rules-based approaches are limited to predefined rules that may not cover all scenarios.