Record-Matching Integrity: An Algorithm Primer
One patient can have multiple identifiers within a single organization, e.g. medical record number, billing/patient account number, order number, requisition number, etc. More identifiers come into play when hospitals have multiple locations offering different types of services.
One patient can have multiple identifiers within a single organization, e.g. medical record number, billing/patient account number, order number, requisition number, etc. More identifiers come into play when an organization has multiple locations offering different types of services. When these identifiers flow into an HIE or ACO, strong algorithms must be in place to identify with pinpoint accuracy which records belong to which patient so they can be linked into a single record with a single unique identifier for use across the initiative.
These algorithms are typically embedded in a hospital’s or information exchange’s system. However, they are not all the same. How well an algorithm performs depends upon which of the following three categories it falls into:
* Basic Algorithms: The simplest technique for matching records, basic algorithms make comparisons based on selected data elements, typically name, birth date, Social Security number and gender. They typically utilize exact match or deterministic matching tools, the latter of which is slightly more sophisticated in that partial matches or matches from phonetic encoding systems may also be used. Basic algorithms also deploy wild-card linking techniques, which return every record that matches a limited number of characters entered into a search string as well as any other data element specified to refine the search.
Regardless of the strength of the algorithm used, false positives and false negatives will always occur. Even the most sophisticated algorithms cannot be solely relied upon to make record-matching decisions, as “auto-linking” routines can create errors. Among the most common errors are linking two closely related people with similar names and birth dates who live near each other or two individuals with the same name and birth date who share an address, such as can happen in large apartment complexes or other multi-family residential buildings.
This is why results must always be verified using well-established record-matching validity procedures. Skipping this critical step could result in overlaid records, potentially violating privacy laws and, more significantly, impacting care coordination, quality and safety.
Beth Haenke Just (firstname.lastname@example.org) is CEO and president of Just Associates, a data integrity consulting firm.