Understanding Matches
This page explains how RosterGuard decides two people are the same, what the three confidence levels mean, how accurate the matching is (with the numbers behind it), and where the boundaries are.
Why name matching at all
Only about 1 in 10 OIG LEIE records carries an NPI (roughly 8,600 of 83,000 in the current exclusion list), so the check most teams write first — roster NPI against the list — is blind to about 90% of it. Among excluded nurses the coverage is worse: under 1% carry an NPI. Closing that gap means matching on name and date of birth — and doing it defensibly means published accuracy, published false‑positive behavior, and per‑hit evidence, which is what the rest of this page is.
Names are compared after the same cleanup on both sides: case and punctuation removed, suffixes dropped, and hyphenated or multi‑part surnames split so each surname still matches on its own (which is how married‑name and hyphenation changes still line up). Date of birth is the precision anchor — it divides the chance‑collision space by roughly 15,000, which is what lets name‑based matching run at a usable false‑positive rate.
The three confidence levels
Strong
Exact NPI match against an exclusion record
An identifier match — the highest‑evidence hit
Likely
Same name and date of birth
Strong evidence; review the per‑hit fields and confirm
Possible
Same name and state, no date‑of‑birth corroboration
Advisory only — a research signal, never auto‑confirmed
A Strong hit whose exclusion NPI actually belongs to an organization (not the person) is held back for review rather than treated as a confirmed match. Every hit carries per‑field evidence: which fields matched exactly, which agreed after cleanup, which conflict, and the exclusion's authority with a plain‑language description of what it was for. Likely and Possible results are potential matches — verify via OIG's online SSN check (the LEIE search / SSN verification at oig.hhs.gov/exclusions) before adverse action.
How accurate it is
These figures are measured against a validation set and re‑measured on every release; every screen also shows the exact source vintages it ran against.
Excluded providers found by name + DOB (Likely)
~95%
~7,000‑provider validation set
Found once the wider name variant is included
~98%
same validation set
Exclusion records carrying an NPI
~10%
current exclusion list
Excluded nurses carrying an NPI
~0.7%
current exclusion list
In practice, an organization running monthly screens of a ~2,500‑provider roster should expect roughly one strong‑evidence false positive every six years — the matching is tuned to be quiet. The Possible tier is deliberately noisier (a name + state coincidence surfaces a handful of research rows per screen) and is never treated as a match.
The validation set: it was built from providers whose exclusion records carry an NPI (so the right answer is knowable) and it over‑represents physicians. The accuracy figures describe that set, not every excluded provider — a heavily nurse‑weighted roster, for example, isn't separately measured.
What this is not
Not SAM.gov or state Medicaid list screening. RosterGuard screens against the OIG LEIE plus CMS enrollment‑derived signals. It complements — it does not replace — your SAM.gov and state Medicaid screening program.
No point‑in‑time / historical claims. RosterGuard screens against the current exclusion list. It does not reconstruct "was this provider excluded as of {date}."
Possible matches are advisory. A name + state match with no date of birth is a research signal. Acting on it without secondary‑identifier verification is outside the product's intended use.
Determinations are yours. The app records dispositions; it never makes them.
RosterGuard is a screening aid. Match dispositions are determinations made by your organization's reviewers. Likely and Possible results are potential matches — verify via OIG's online SSN check (oig.hhs.gov/exclusions) before adverse action.
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