Physician utilization by provider and service
Centers for Medicare & Medicaid Services
In a RepVector build, this dataset names the individual practitioners and organisations you can actually call.
| Clinician | Credentials | Specialty | City | ZIP | NPI |
|---|---|---|---|---|---|
| Neda Heidari | MD | Neurology | Oxnard | 93030 | 1003028556 |
| Aaron Jeng | MD, MPH | Internal Medicine | San Gabriel | 91776 | 1003053851 |
| Srinivas Peddi | MD | Diagnostic Radiology | Valencia | 91355 | 1003024191 |
| Aaron Jeng | MD, MPH | Internal Medicine | San Gabriel | 91776 | 1003053851 |
| Tarek Alasil | MD | Ophthalmology | Los Angeles | 90045 | 1003072786 |
| Jackson Penry | MD | Diagnostic Radiology | Mission Viejo | 92691 | 1003055401 |
| Srinivas Peddi | MD | Diagnostic Radiology | Valencia | 91355 | 1003024191 |
| Quazi Al-Tariq | M.D. | Diagnostic Radiology | Stanford | 94305 | 1003059395 |
What this dataset is
What each individual Medicare-billing clinician actually did: their specialty, city, and for every billing code they used, how many beneficiaries they treated, how many services they delivered, and average submitted, allowed and paid amounts. Named-clinician volume, not a state rollup — the basis for identifying high-volume practitioners for a given procedure or drug. It is published by Centers for Medicare & Medicaid Services and can be queried at state and place level, refreshed annual. Each row is an individual entity — an organisation, site or practitioner — so results can be filtered, routed and turned directly into a call list.
Why it is useful
- Yields names, specialties, practice addresses and, where published, phone numbers.
- Supports specialty and taxonomy filtering so a call list matches what you sell.
- Feeds the tiering engine that ranks who to see first and explains why.
Questions it helps answer
- Who specifically should I see in this territory this week?
- Which specialties are dense enough here to build a route around?
- Which named targets are new since my last pull?
How different verticals use it
RepVector is vertical-agnostic: this dataset is selected by what your question asks for, not by industry. These are common ways teams put it to work.
Builds named call lists with specialty, address and phone straight from the registry.
Finds counsellors, prescribers and programmes eligible for referral partnerships.
Targets practices by size and specialty for platform or EHR adjacent sales.
Segments practice owners as commercial banking or insurance prospects.
How RepVector uses it
- You describe a market in plain language. The geography resolver turns the place you named into the exact identifiers this dataset needs — city to county to state, plus DMA where relevant — so a query at state runs without you knowing any codes.
- The planner selects Physician utilization by provider and service only when your question matches what it actually reports. It is never included to pad a result.
- Returned rows are normalised into the shared metric layer, so figures from Centers for Medicare & Medicaid Services line up with every other source in the same dashboard instead of sitting in an isolated table.
- Quality checks score coverage and recency. If Centers for Medicare & Medicaid Services suppresses or omits a value for your geography, the gap is reported as a gap — nothing is estimated or filled in.
- The result renders as charts, KPIs and, where the rows are entities, prioritised targets you can export to PDF, Excel, CSV or JSON.
What it does not tell you
- Registry contact details reflect what the publisher holds; they age and are verified against other sources rather than trusted alone.
| Field | Key | Type |
|---|---|---|
| Clinician | clinician | string |
| Credentials | credentials | string |
| Specialty | specialty | string |
| City | city | geo |
| ZIP | zip | string |
| NPI | npi | string |
| Code | hcpcs | string |
| Service | service | string |
| Drug code | isDrug | string |
| Setting | setting | string |
| Beneficiaries | beneficiaries | number |
| Services delivered | services | number |
| Avg allowed amount | avgAllowed | currency |
| Avg Medicare payment | avgPaid | currency |
How it fits with the rest of the catalog
Provider rosters answer who; need and spending data answer why that person is worth the visit. RepVector queries every selected dataset against the same resolved geography and time window, then normalises the results into one metric layer so they can be charted side by side. These datasets are the most common companions to Physician utilization by provider and service:
Other providers datasets
Common questions
What does Physician utilization by provider and service contain?
What each individual Medicare-billing clinician actually did: their specialty, city, and for every billing code they used, how many beneficiaries they treated, how many services they delivered, and average submitted, allowed and paid amounts. Named-clinician volume, not a state rollup — the basis for identifying high-volume practitioners for a given procedure or drug. It is published by Centers for Medicare & Medicaid Services at state and place grain.
How often is it refreshed?
Centers for Medicare & Medicaid Services publishes on a annual cycle. RepVector re-reads the source on that cadence and keeps every prior capture, so you can see what changed.
Do I need an account to use it?
No. Browsing the catalog and the free lookup tools needs no account. Running a full market build across every county in a territory is the paid product.
Build the list for one county and see whether the names are right.
Ask in plain language. RepVector finds the universe, enriches each target from its own website, scores it 0–100, and hands you a ranked call list with phones, addresses and named contacts.
3 free runs — no card required.