HRSA 340B Covered Entity Registry (OPAIS)

What it is

1. 1The HRSA 340B covered-entity registry: the official roster of every health-care provider site enrolled in (or terminated from) the federal 340B Drug Pricing Program, which lets eligible safety-net providers buy outpatient drugs at deeply discounted ceiling prices.
2. 2One row = one registered 340B site (hospital, FQHC, grantee clinic, etc.) with its identity, type, address, officials, and participation status.
3. 3In a cost-data catalog it is the entity/eligibility dimension that flags which providers get 340B drug pricing (vs WAC/ASP) — a key driver of drug-acquisition cost and of payer 340B carve-out/duplicate-discount logic.

Grain

How to read it

1. 1Count active 340B sites by state and entity type: SELECT street_address_state, entity_type, count(*) FROM drug_silver.dim_hrsa_covered_entity WHERE participating = true GROUP BY 1,2 ORDER BY 3 DESC.
2. 2Answers 'how many currently-active 340B-eligible provider sites of each type are in each state' — i.e. which providers get 340B discounted drug acquisition cost (vs WAC/ASP), the basis for 340B carve-out and duplicate-discount analysis.
3. 3To roll up to organizations, group on regexp_replace(id_340b,'-[0-9A-Za-z]+$','') instead.

Traps

1. 1NOT an active-providers list: 31% of rows (28,352) are participating=false (terminated/decertified or pending). You MUST filter participating=true to count or join only active 340B sites; the table deliberately retains inactive entities.
2. 2id_340b is the SITE grain, not the organization grain. One parent organization registers many child sites via the -NN suffix (73,861 parents → 92,814 sites; Cleveland Clinic alone = 498 sites). To analyze at the org level, strip the trailing -[alnum]+ suffix (regexp_replace(id_340b,'-[0-9A-Za-z]+$','')) and aggregate. Counting distinct id_340b counts sites, not organizations.
3. 3Single static snapshot only (observed_period/asserted_at/effective_period_start/effective_period_end/sha256_content_hash/bronze_object_key all = one value, 2026-05-15). All the temporal, vintage, and provenance columns are constants here — they exist for the cross-table snapshot/versioning framework, not for time-series analysis within this table. is_current=true on every row, so it currently de-dupes nothing.
4. 4effective_period_end EQUALS effective_period_start (both 2026-05-15) — this is a point-in-time observation, NOT an SCD-2 validity interval. Do not use [effective_period_start, effective_period_end] as a date range.
5. 5Nulls carry meaning and code-conditional presence: grant_number is null for ~all DSH/RRC/CAH/SCH/PED hospital types (they qualify via the DSH adjustment, not a grant) and populated for grantee types (CH/FP/TB/STD/RW*) — its presence is driven by entity_type. certified_decertified_date is null for 8,550 entities that never had a certify/decertify event (null ≠ inactive). sub_name is null for 9,164 sites with no DBA. drug_source_version_id is 100% null.
6. 6ce_id looks like a tidy key but is a sparse internal HRSA sequence (1–117,822 with gaps) — use id_340b as the durable business key for joins; ce_id can be reassigned/non-portable across HRSA vintages. Also: name is free-text and inconsistently cased (UPPER vs Title case for the same org) — normalize before name-based matching; the leading letters of id_340b usually but not always match entity_type (4,243 mismatches).

Columns (33)

Query it

describe_data_source(source_fqn="drug_silver.dim_hrsa_covered_entity")     # the card, live
query_substrate(sql="SELECT * FROM drug_silver.dim_hrsa_covered_entity WHERE id_340b = '<value>' LIMIT 100")