CDPHP
Last updated: February 2026
Data Version
Price Transparency Machine-Readable Files Snapshot
Overview
6
Plans
16K
Providers (NPIs)
73
Tax IDs
13K
Billing Codes
2.5M
Data Rows
+100% vs prevData Breakdown
Plans Type (HIOS vs EIN)
EIN | 6 |
NPIs
Individual | 15K |
Group | 640 |
Tax IDs
EIN | 73 |
Billing Code Type
| Codes | Rows | |
|---|---|---|
CPT | 9.5K | 2M |
HCPCS | 3.2K | 580K |
CDT | 70 | 560 |
RC | 96 | 280 |
MS-DRG | 14 | 28 |
Billing Class
| Rows | |
|---|---|
Professional | 2.3M |
Institutional | 220K |
Negotiated Type
| Rows | |
|---|---|
Fee Schedule | 2.5M |
Negotiated | 65K |
Percentage | 7.1K |
Per Diem | 2.8K |
Top States by # NPIs
New York | 10K |
Vermont | 1.2K |
Massachusetts | 1.1K |
Florida | 370 |
Pennsylvania | 330 |
Top Taxonomies by # NPIs
Physician Assistant | 1.5K |
Family Nurse Practitioner | 1.3K |
Internal Medicine Physician | 1.1K |
Family Medicine Physician | 1K |
Emergency Medicine Physician | 820 |
Monthly Metrics
| Month | Plans | NPIs | Tax IDs | Billing Codes | Data Rows |
|---|---|---|---|---|---|
February 2026 | 6 | 16K | 73 | 13K | 2.5M |
January 2026 | 6 | 16K | 73 | 13K | 1.3M |
December 2025 | 6 | 15K | 73 | 13K | 1.3M |
November 2025 | 6 | 15K | 73 | 13K | 1.3M |
October 2025 | 6 | 15K | 73 | 0 | 0 |
Data Coverage Scorecard
Professional & Institutional Rates
Data includes both professional and institutional billing class rates
Valid Tax IDs
More than 80% of Tax IDs are EIN or NPI (Organization) type
HIOS Plan
Data does not include any HIOS-identified plans
Low Derived/Percentage Rates
Less than 10% of negotiation entries are derived or percentage type
Standard Code Coverage
Sufficient CPT and HCPCS code coverage with low proportion of LOCAL entries
Common Taxonomies
NPIs in the dataset cover at least 50 distinct provider taxonomies
Methodology
The scorecard evaluates each payer's monthly price transparency data against six quality criteria:
If 5–6 criteria pass, the overall rating is Good.
If 3–4 criteria pass, the rating is Acceptable.
If 0–2 criteria pass, the rating is Poor.
Behavioral, RX, and other specialty networks may not pass all criteria but can still contain complete and usable data.