How alternate names change organization NPI matching

Our data

We computed this report from 27,965 NPI records in 13 existing legal-organization profiles. The downloadable dataset contains 13 rows at the legal organization level. The cohort consists of these selected legal-name profiles; each retains its own source boundaries.

Download the computed CSV · JSON dataset · Input checksums and provenance · Reproduction method

Coverage and variety answer different questions

National Vision Inc. has other-name values on 957 of 1,283 NPI records, but only 35 distinct strings in the retained field. CVS Pharmacy Inc. has 1,211 covered records and 1,128 distinct strings. Those are different matching workloads. The first profile concentrates many records under a smaller vocabulary; the second exposes a much larger set of values to preserve and inspect.

The distinct count measures source strings. A source value may contain a location label, spelling variation, historical name or other registration text. The calculation deliberately leaves those distinctions unresolved. It tells you how much vocabulary exists before a reviewer decides which values represent the same commercial identity.

Sparse fields supply fewer matching candidates

Genoa Healthcare LLC has 48 records with other names among 831 NPIs, covering 5.78% of its extract. Its seven distinct strings describe the seven values retained in this field. The result says that this source field supplies little additional naming information for most records in this selected legal-name slice.

Conversely, Luxottica of America Inc. has 1,851 covered records out of 1,855 and 2,129 distinct strings. The number of strings exceeds the number of covered records because the retained field can hold multiple values. A workflow that keeps only the first value loses information even when its coverage metric initially looks excellent.

Preserve aliases in a separate relationship table

A practical account table should keep the original legal name and original NPI intact. Store each candidate alternate name in a related table with its source file, source NPI and snapshot date. That makes the relationship inspectable and allows a reviewer to reject one alias without overwriting the identity of the underlying registration.

Replacing the legal name with a familiar brand may make a screen easier to read, but it removes useful evidence. It also makes later reconciliation harder when another dataset uses the legal registration name. A display-name field can serve the interface while the source legal name and alias table remain available for matching and audit.

Use names to generate candidates, then verify them

An exact other-name match can place two records in the same review queue. Keep that queue separate from accepted account matches. Compare the practice address, taxonomy and any current official organization evidence before deciding that they belong to one operational account. Shared words and retail labels can occur across separate registrations.

A useful review sample includes both repeated aliases and values found on only one NPI. Repeated strings reveal where a broad name might connect many locations; singleton strings expose spelling and location-specific variants. Keep accepted, rejected and unresolved matches separately so a later import preserves the reviewed decisions.

A denominator for measuring the next enrichment step

This report measures availability of one retained NPPES-derived field. If a team adds aliases from current organization websites, it can compare the number of newly supported NPI-to-name relationships against the baseline. It must keep that independently gathered evidence separate from the original source values.

Measure improvement through reviewed matching outcomes alongside string counts. Adding many speculative variants increases volume without demonstrating better matching. A stronger measure records how many reviewed account matches gained support, how many wrong candidates were rejected, and which identities remain unresolved. This dataset provides the starting coverage counts for that evaluation.

What the download enables

The 13-row comparison file is small enough to inspect in a spreadsheet. Sort by low field coverage to find profiles where this source offers little help. Sort by high distinct-string counts to find profiles where a name dictionary needs more careful handling. Both priorities can matter, and they require different work.

For record-level investigation, follow the linked organization profile and download its underlying CSV. Preserve semicolon-delimited values during import, and avoid treating the whole multi-value string as a single alias. The exact strings remain in those original files; this aggregate download intentionally contains counts rather than republishing contact details.

Method and reproducibility

Read other_name from each retained organization CSV. A record has naming coverage when the trimmed field is nonempty and is not the literal missing-value marker <UNAVAIL>. For distinct-name counts, split the field on semicolons, trim each value, exclude empty values and that missing marker, and count exact remaining strings within the legal-name profile. Coverage divides records with a usable field by all NPI records in that profile. No fuzzy match, external ownership inference or name correction is applied.

Limitations: An other-name value supports a matching investigation but does not establish that a business currently uses the name, owns another entity, has an active website or is a separate company. A sparse field is not a complete inventory of public-facing identities. Exact string counts are sensitive to punctuation, spelling and the source extraction. Multiple names can belong to one NPI, and one name can appear on many NPIs. Distinct counts summed across profiles are not globally deduplicated. The comparison does not measure commercial enrichment success or downstream matching outcomes.

This analysis and its prose were prepared with AI assistance. The 13 input CSV files are the downloads already attached to the organization profiles below. We read identifiers and ZIP codes as text, preserve legal names, and calculate each organization independently. The provenance file records every input SHA256 and row count. The analysis code is included with the reviewable source; the public CSV and JSON contain identical rows. The derived downloads contain aggregate organization-level counts and source-field summaries.

Underlying source: CMS NPPES dissemination files, identified in each profile as the September 2026 monthly release. The public CMS index was under maintenance when this analysis was prepared; we used the retained extracts and their recorded provenance, using the original recorded source vintage. CMS explains the NPI standard.

Source profiles and state drill-downs

• COSTCO WHOLESALE CORPORATION profile, source records and method
• CVS PHARMACY INC profile, source records and method
• GENOA HEALTHCARE LLC profile, source records and method
• LUXOTTICA OF AMERICA INC profile, source records and method
• NATIONAL VISION INC profile, source records and method
• NEW YORK CITY HEALTH AND HOSPITALS CORPORATION profile, source records and method
• NOVANT HEALTH MEDICAL GROUP, LLC profile, source records and method
• PUBLIX SUPER MARKETS INC profile, source records and method
• TOTAL RENAL CARE INC profile, source records and method
• WALGREEN CO profile, source records and method
• WALMART INC. profile, source records and method
• WAL-MART STORES EAST LP profile, source records and method
• WAL-MART STORES, INC profile, source records and method

Each profile links to its existing state page and filtered source records. Those pages provide the next level of detail; use them to inspect the records behind a legal-name distribution. Start with CVS Pharmacy Inc. in Texas, Novant Health Medical Group in North Carolina, or the organization research directory.