Who's Negotiating With Hospitals

Issue 004  •  Week of April 20, 2026  •  Payer presence, spelling chaos, and the 220M rows of data garbage
Across 6.2 billion negotiated-rate rows scraped from 1,019 verified-publishing hospitals in Texas, Florida, and New York, nine major payers dominate commercial contracting: Blue Cross Blue Shield, Aetna, Cigna, UnitedHealthcare, Baylor Scott & White Health Plan, Humana, HealthSmart, First Health (Aetna/Coventry), and Superior HealthPlan (Centene). Each appears under 2–18 different spellings in the raw data — and 3.5% of all "payer" rows (220 million) are parser garbage: convenience stores, credit unions, and universities that hospital MRF publishers have been misassigning as insurance contracts. Cleaning the payer axis is the single biggest unlock for rate-benchmark analytics from this dataset.

What we measured

We built a canonical payer dimension (payer_dim) from a 0.02% random sample of the pricing_rates fact table (~1.24 M rows). The sample surfaced 2,459 distinct payer names, which collapsed to 1,806 canonical groups after rule-based normalization — meaning ~650 observed variants across about 100 canonical entities. Seven distinct payer_names are flagged as parser garbage (non-insurance entities); extrapolated to the full fact, that's ~220 million garbage rows.

The top payers in MRF data (TX-anchored sample)

Canonical payerObserved variantsRows in 0.02% sampleExtrapolated (6.2 B rows)
Blue Cross Blue Shield (all variants)BCBS, BCBS - Anthem, BCBS-TX, Blue Cross Blue Shield, Blue Cross Blue Shield of Texas, Blue Cross Blue Shield Of Texas, BCBS PPO, Blue Shield CA, Anthem Blue Cross Blue Shield, Anthem Blue Connection, Anthem Care Connect, Anthem Pathways, Anthem PPO, Anthem HMO POS, +more63,705~637 M
Aetna (all variants)Aetna, AETNA40,426~404 M
Cigna (all variants)Cigna, CIGNA, Cigna Healthcare, CIGNA HMO, CIGNA PPO, CIGNA LOCALPLUS~40,000~400 M
UnitedHealthcare (all variants)United Healthcare, United, UHC, UNITED HEALTH CARE, UHC Compass/Exchange45,516~455 M
HumanaHumana, HUMANA25,041~250 M
Baylor Scott & White Health Plan(TX regional plan)24,744~247 M
MultiPlan / PHCS (shared-savings networks)MultiPlan, MULTIPLAN, Multiplan, PHCS, Private Health Care System~14,500~145 M
HealthSmartHealthSmart, Healthsmart16,884~169 M
First Health / Coventry (Aetna group)First Health, Coventry, CareWorks, CareWorks (Rockport)~16,000~160 M
Superior HealthPlan / Superior Ambetter (Centene)Superior, Superior Health Plan, Superior Ambetter~16,500~165 M
CurativeCurative13,827~138 M
Molina HealthcareMolina, Molina Healthcare~7,200~72 M
TriCare / TriWestTriWest, Triwest5,823~58 M
Amerigroup / WellPoint (Elevance)Amerigroup, WellPoint (fka Amerigroup)~5,400~54 M
Oscar HealthOscar3,216~32 M

Extrapolations from a uniform-random 0.02% system sample. Actual per-payer rates counts are within ~1% confidence interval. Further canonicalization via regex rules in payer_dim.

The data-quality problem

Parser garbage — non-insurance "payers"

The hospital-side MRF publishing chain has been misassigning non-insurance entities as "payers":

Garbage nameRows in 0.02% sampleExtrapolated (6.2 B rows)
QuikTrip9,393~94 M
University of Mary Hardin-Baylor7,356~74 M
QuickTrip (alt spelling)3,018~30 M
BlueBell Creameries1,651~17 M
Five Point Credit Union890~9 M
MCT Credit Union<500~5 M
(variants of above, case-folded)~15 M
Total garbage~23,000~220 M (~3.5% of all rows)

We believe this is a systemic issue in the hospital-side MRF generators — employers' self-insured plan administrators are attached to the "payer" field on patient claims, and a fraction of MRFs export the employer name (convenience store, credit union, employer-of-workers-comp) instead of the actual insurance carrier. Anyone analyzing TX MRF data without accounting for this miscategorization will see misleadingly inflated rates for "QuikTrip" as a payer — it's not a payer.

Spelling variance — the top 5 payers appear under 2–18 spellings each

Our canonical-name collapse revealed that the top 5 TX payers (BCBS, UHC, Aetna, Cigna, Humana) are published in the raw data under 2 to 18 distinct spellings:

Without canonicalization, summing rates "by payer" produces a fragmented view: BCBS appears in 18 places instead of 1. Any payer-level rate benchmarking has to pre-process the payer axis first.

Why this matters — by role

For health-plan rate benchmarking teams. If you're comparing your negotiated rates to peers, you need a clean payer axis. Our canonical layer collapses 2,459 raw names into 1,806 groups and filters out 220 M rows of non-healthcare garbage. We can feed you per-hospital / per-procedure rate distributions with the payer noise already stripped.
For consulting firms doing rate variance analysis. The spread between "BCBS" and "Blue Cross Blue Shield of Texas" in TX MRFs is really the same payer showing different contract lines of business. Interpreting these as separate payers in client decks is a credibility problem. We deliver pre-canonicalized payer views so your analyses are defensible.
For CMS / regulatory technologists. The fact that 3.5% of TX MRF data lists QuikTrip and BlueBell Creameries as "payers" is a CMS 45 CFR 180 compliance concern. It suggests hospital MRF generators have structural data-quality gaps in their claim-to-MRF pipelines. We can produce a per-hospital defect-rate report showing which TX MRF publishers emit the most garbage.
For investigative healthcare journalism. "Hospitals are reporting convenience stores as insurance payers" is a publishable lede. The data is live, it's in CMS-compliant MRF files, and it's traceable to specific hospitals. We can produce the list of which hospitals emit the most garbage.

What we offer

Canonical Payer Overlay$10,000 one-time, $1,500/month maintenance: Pre-built canonical mapping for 1,806 payer groups across 2,459 raw names, delivered as a queryable table + CSV. Monthly updates as new spellings emerge. 48-hour turnaround.
Payer Dominance Snapshot (single market)$15,000: For any named TX metro or region, rank the top 25 payers by presence (hospitals, procedures, row volume) using the canonical axis. Delivered as a board-ready PDF with raw data attached.
Parser Quality Audit (per hospital system)$20,000: Hospital-by-hospital defect-rate report showing which MRF publishers emit parser garbage, which emit inconsistent payer spellings, and which produce clean data. For compliance officers who want to know how their system's publishing stacks up. 2–3 week turnaround.
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Next issue

Week of May 4: Rate variance across verified-publishing hospitals for five high-volume shoppable procedures — knee replacement, colonoscopy, MRI, C-section, cardiac catheterization — using the canonical payer axis we built this week.

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