Built on the most complete
view of healthcare pricing
Why this matters
Healthcare pricing data is fragmented, inconsistent, and difficult to interpret:
Payer files show negotiated rates, but not whether they're used.
Hospital disclosures can show gross charges, but not how those are discounted.
Claims show payments, but lack important context.
Decisions made on any one dataset alone can lead to missed opportunities.
Serif connects all three—so you can see what care should cost, what it actually costs, and how it varies across the market.
What data is included —
and how often it updates
Serif brings together the core datasets that define healthcare pricing:
Data Source
Payer transparency
What it Covers
In-network negotiated rates across commercial plans
Scale
- 200+ payers
- 600+ networks
- 90% of commercial lives
- 350+ billion rates processed
Refresh
Monthly
Data Source
Hospital transparency
What it Covers
Chargemasters, negotiated rates, and cash prices
Scale
- 4,700+ hospitals
- 19.3 billion rates processed
Refresh
Monthly monitoring
Data Source
Claims data
What it Covers
Paid amounts, utilization, referrals, diagnoses
Scale
- 9B+ claims
- 250M lives
- 500+ plans
Refresh
Monthly
Data Source
Practice affiliations
What it Covers
Provider – practice – payer relationships
Scale
- 4M+ providers
- 1M+ practices
Refresh
Monthly
| Data source | What it covers | Scale | Refresh |
|---|---|---|---|
| Payer transparency | In-network negotiated rates across commercial plans |
| Monthly |
| Hospital transparency | Chargemasters, negotiated rates, and cash prices |
| Monthly monitoring |
| Claims data | Paid amounts, utilization, referrals, diagnoses |
| Monthly |
| Practice affiliations | Provider – practice – payer relationships |
| Monthly |
See exactly which payers and networks are included
Browse Serif's payer inventory, including available files, network types, and data quality scorecards.
Not all pricing data is defined the same way. We standardize it.
Serif normalizes pricing to the most comparable unit available:
Facility vs. professional billing
Inpatient (DRG, per diem, case rates) vs. outpatient (CPT/HCPCS)
Percent-of-billed and other non-standard contract structures
Site-of-care differences (hospital, ASC, office, etc.)
Imputed anesthesia rates, including time units and conversion factors
This ensures that when you compare rates, you're comparing like-for-like— not mixing fundamentally different pricing constructs.
A complete view of healthcare pricing—not partial datasets
Across all sources, Serif standardizes pricing into a consistent structure:
Facility (inpatient & outpatient)
Professional (all specialties)
Non-hospital sites of care
Ancillary services
Claims context
Raw data doesn't answer the question. This is what makes it usable.
Ingestion
We ingest and maintain the full universe of price transparency data:
- Payer machine-readable files (MRFs) across all lines of business
- Hospital standard charge files
- Nationwide commercial, Medicare, and Medicaid claims
- Provider directory and affiliation datasets
A single month of payer data alone contains 350B+ rate records.
Quality Assurance
Most price transparency data is not decision-ready. We systematically remove noise and errors.
- Filter out clinically invalid or irrelevant rates ("zombie rates") using claims validation
- Validate hospital disclosures against payer and claims data
- Standardize inconsistent formats across thousands of source files
Normalization
Built for apples-to-apples comparisons — pricing is normalized across billing class, code systems, and contract structures:
- Standardize CPT/HCPCS and DRG mappings across sources
- Separate facility and professional billing components
- Normalize payer and plan naming conventions
- Resolve provider identity across NPI, EIN, and organizational hierarchies
This ensures accurate comparisons across providers, payers, and markets.
Enrichment
We transform raw filings into a structured, analyzable dataset:
- Attach provider details (name, specialty, location)
- Map provider–practice–payer relationships
- Add benchmarks like percent of Medicare
- Extract contract terms (e.g., stop-loss, carve-outs, outliers) from unstructured data
- Crosswalk and validate payer and hospital records for a given procedure
- Add additional enrichments (e.g., rate licensure tier) to reduce ambiguity
Validation
Where most datasets stop, Serif goes further. We cross-check published rates against real-world claims data:
- Compare negotiated rates vs. actual paid amounts
- Identify discrepancies and reliability patterns
- Provide context on utilization, referral patterns, and volume
Claims data turns transparency into confidence, not just visibility.
Time Series & Reliability
Built for trend analysis — not just point-in-time views.
- Monthly refresh cadence across all major datasets
- Historical pricing data tracked back to early 2023
- Consistent normalization enables reliable trend analysis over time
This allows you to track rate changes, monitor market shifts, and evaluate contract performance.
Three datasets. One coherent view of the market.
Most vendors give you one of these:
Serif connects all three—so you can answer:
Data source coverage
| Hospital Data Hospital | Payer Data Payer | Claims Data Claims | Practice Affiliations Practice Aff. | |
|---|---|---|---|---|
| Update Frequency | Annually | Monthly | Monthly | Monthly |
| Public Medicaid | E | E | C | |
| Managed Medicaid | N | C | ||
| Public Medicare | E | E | C | |
| Medicare Advantage | N | C | ||
| Commercial Individual | N | N | C | N |
| Commercial Group | N | N | C | N |
| ACA Individual | N | N | C | N |
| Cash Pay | N |
Used by 200+ organizations across providers, payers, and benefits.
Used by 200+ organizations across providers, payers, and benefits.
See the data for yourself
Explore a sample dataset and see how pricing looks across payers, providers, and markets.