U.S. Healthcare Data | Medicare | National | Practitioner level
Verified Data Provider| # | INDEX |
NPI |
IND_PAC_ID |
IND_ENRL_ID |
LST_NM |
FRST_NM |
MID_NM |
SUFF |
GNDR |
CRED |
MED_SCH |
GRD_YR |
PRI_SPEC |
SEC_SPEC_1 |
SEC_SPEC_2 |
SEC_SPEC_3 |
SEC_SPEC_4 |
SEC_SPEC_ALL |
ORG_NM |
ORG_PAC_ID |
NUM_ORG_MEM |
ADR_LN_1 |
ADR_LN_2 |
LN_2_SPRS |
CTY |
ZIP |
PHN_NUMBR |
HOSP_AFL_1 |
HOSP_AFL_LBN_1 |
HOSP_AFL_2 |
HOSP_AFL_LBN_2 |
HOSP_AFL_3 |
HOSP_AFL_LBN_3 |
HOSP_AFL_4 |
HOSP_AFL_LBN_4 |
HOSP_AFL_5 |
HOSP_AFL_LBN_5 |
IND_ASSGN |
GRP_ASSGN |
ADRS_ID |
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx |
| 2 | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx |
| 3 | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx |
| 4 | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx |
| 5 | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx |
| 6 | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx |
| 7 | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx |
| 8 | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx |
| 9 | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxx |
| 10 | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx | xxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxxx |
| ... | xxxxxx | Xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxxx |
Data Dictionary
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
INDEX
|
|||
|
NPI
|
Integer | 1225238900 | |
|
IND_PAC_ID
|
Integer | 4587756820 | |
|
IND_ENRL_ID
|
String | I20190220001628 | |
|
LST_NM
|
String | WENZEL | |
|
FRST_NM
|
String | ABBY | |
|
MID_NM
|
String | LOUISE | |
|
SUFF
|
|||
|
GNDR
|
Boolean | f | |
|
CRED
|
|||
|
MED_SCH
|
String | OTHER | |
|
GRD_YR
|
Integer | 2004 | |
|
PRI_SPEC
|
String | NURSE PRACTITIONER | |
|
SEC_SPEC_1
|
|||
|
SEC_SPEC_2
|
|||
|
SEC_SPEC_3
|
|||
|
SEC_SPEC_4
|
|||
|
SEC_SPEC_ALL
|
|||
|
ORG_NM
|
String | CARVER COUNTY | |
|
ORG_PAC_ID
|
Integer | 8527041961 | |
|
NUM_ORG_MEM
|
Integer | 13 | |
|
ADR_LN_1
|
String | 540 E 1ST ST | |
|
ADR_LN_2
|
|||
|
LN_2_SPRS
|
|||
|
CTY
|
String | WACONIA | |
| String | MN | State Name | |
|
ZIP
|
Integer | 553871601 | |
|
PHN_NUMBR
|
Integer | 9524424437 | |
|
HOSP_AFL_1
|
|||
|
HOSP_AFL_LBN_1
|
|||
|
HOSP_AFL_2
|
|||
|
HOSP_AFL_LBN_2
|
|||
|
HOSP_AFL_3
|
|||
|
HOSP_AFL_LBN_3
|
|||
|
HOSP_AFL_4
|
|||
|
HOSP_AFL_LBN_4
|
|||
|
HOSP_AFL_5
|
|||
|
HOSP_AFL_LBN_5
|
|||
|
IND_ASSGN
|
Boolean | t | |
|
GRP_ASSGN
|
Boolean | t | |
|
ADRS_ID
|
String | MN553871601WA540XXSTXX400 |
Description
Geography
Volume
| 2.38 million | records |
Pricing
| License | Starts at |
|---|---|
| One-off purchase |
$5,000 / purchase |
| Monthly License | Available |
| Yearly License | Available |
| Usage-based | Not available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
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Frequently asked questions
What is U.S. Healthcare Data Medicare National Practitioner level?
We provide data on centers for medicare and medicaid health coverage for more than 100 million people through Medicare, Medicaid, the Children’s Health Insurance Program, and the Health Insurance Marketplace.
What is U.S. Healthcare Data Medicare National Practitioner level used for?
This product has 1 key use case. RateSpot recommends using the data for Nursing/rehab/retirement homes contacts. Global businesses and organizations buy Healthcare Professionals Data from RateSpot to fuel their analytics and enrichment.
Who can use U.S. Healthcare Data Medicare National Practitioner level?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Healthcare Professionals Data. Get in touch with RateSpot to see what their data can do for your business and find out which integrations they provide.
Which countries does U.S. Healthcare Data Medicare National Practitioner level cover?
This product includes data covering 1 country like USA. RateSpot is headquartered in United States of America.
How much does U.S. Healthcare Data Medicare National Practitioner level cost?
Pricing for U.S. Healthcare Data Medicare National Practitioner level starts at USD5,000 per purchase. Connect with RateSpot to get a quote and arrange custom pricing models based on your data requirements.
How can I get U.S. Healthcare Data Medicare National Practitioner level?
Businesses can buy Healthcare Professionals Data from RateSpot and get the data via S3 Bucket, SFTP, and Email. Depending on your data requirements and subscription budget, RateSpot can deliver this product in .csv and .txt format.
What is the data quality of U.S. Healthcare Data Medicare National Practitioner level?
RateSpot has reported that this product has the following quality and accuracy assurances: 95% match rate. You can compare and assess the data quality of RateSpot using Datarade’s data marketplace. RateSpot has received 1 review from clients. RateSpot appears on selected Datarade top lists ranking the best data providers, including Best 5 Mortgage APIs to use in 2023.
What are similar products to U.S. Healthcare Data Medicare National Practitioner level?
This Tabular Data has 3 related products. These alternatives include Healthcare Data- Healthcare Contact Data, Healthcare Professionals Data, Scrape All Publicly Available Healthcare Related Data’s 50% Cost Saving, Healthcare Contact Data Hospital Industry Data Global Coverage Doctors, Nurses 200M+ Contacts (Verified E-mail, Direct Dails) , and Grepsr Health Care Provider (HCP) Data Physicians Data, Hospital Data Global Coverage. You can compare the best Healthcare Professionals Data providers and products via Datarade’s data marketplace and get the right data for your use case.
