Access the U.S. National Provider Identifier Registry API

Use R to access the U.S. National Provider Identifier (NPI) Registry API (v2.1) by the Center for Medicare and Medicaid Services (CMS): https://npiregistry.cms.hhs.gov/. Obtain rich administrative data linked to a specific individual or organizational healthcare provider, or perform advanced searches based on provider name, location, type of service, credentials, and many other attributes. npi provides convenience functions for data extraction so you can spend less time wrangling data and more time putting data to work.

Analysts working with healthcare and public health data frequently need to join data from multiple sources to answer their business or research questions. Unfortunately, joining data in healthcare is hard because so few entities have unique, consistent identifiers across organizational boundaries. NPI numbers, however, do not suffer from these limitations, as all U.S. providers meeting certain common criteria must have an NPI number in order to be reimbursed for the services they provide. This makes NPI numbers incredibly useful for joining multiple datasets by provider, which is the primary motivation for developing this package.

There are three ways to install the npi package:

1. Install from CRAN:

install.packages("npi")
library(npi)

1. Install from R-universe:

install.packages("npi", repos = "https://ropensci.r-universe.dev")
library(npi)

1. Install from GitHub using the devtools package:

devtools::install_github("ropensci/npi")
library(npi)

npi exports four functions, all of which match the pattern “npi_*“:

• npi_search(): Search the NPI Registry and return the response as a tibble with high-cardinality data organized into list columns.
• npi_summarize(): A method for displaying a nice overview of results from npi_search().
• npi_flatten(): A method for flattening one or more list columns from a search result, joined by NPI number.
• npi_is_valid(): Check the validity of one or more NPI numbers using the official NPI enumeration standard.

npi_search() exposes nearly all of the NPPES API’s search parameters. Let’s say we wanted to find up to 10 providers with primary locations in New York City:

nyc <- npi_search(city = "New York City")

# Your results may differ since the data in the NPPES database changes over time
nyc
#> # A tibble: 10 × 11
#>       npi enumeration_type basic    other_names identifiers taxonomies addresses
#>  *  <int> <chr>            <list>   <list>      <list>      <list>     <list>   
#>  1 1.19e9 Individual       <tibble> <tibble>    <tibble>    <tibble>   <tibble> 
#>  2 1.31e9 Individual       <tibble> <tibble>    <tibble>    <tibble>   <tibble> 
#>  3 1.64e9 Individual       <tibble> <tibble>    <tibble>    <tibble>   <tibble> 
#>  4 1.35e9 Individual       <tibble> <tibble>    <tibble>    <tibble>   <tibble> 
#>  5 1.56e9 Individual       <tibble> <tibble>    <tibble>    <tibble>   <tibble> 
#>  6 1.79e9 Individual       <tibble> <tibble>    <tibble>    <tibble>   <tibble> 
#>  7 1.56e9 Individual       <tibble> <tibble>    <tibble>    <tibble>   <tibble> 
#>  8 1.96e9 Organization     <tibble> <tibble>    <tibble>    <tibble>   <tibble> 
#>  9 1.43e9 Individual       <tibble> <tibble>    <tibble>    <tibble>   <tibble> 
#> 10 1.33e9 Individual       <tibble> <tibble>    <tibble>    <tibble>   <tibble> 
#> # ℹ 4 more variables: practice_locations <list>, endpoints <list>,
#> #   created_date <dttm>, last_updated_date <dttm>

The full search results have four regular vector columns, npi, enumeration_type, created_date, and last_updated_date and seven list columns. Each list column is a collection of related data:

• basic: Basic profile information about the provider
• other_names: Other names used by the provider
• identifiers: Other provider identifiers and credential information
• taxonomies: Service classification and license information
• addresses: Location and mailing address information
• practice_locations: Provider’s practice locations
• endpoints: Details about provider’s endpoints for health information exchange

A full list of the possible fields within these list columns can be found on the NPPES API Help page.

If you’re comfortable working with list columns, this may be all you need from the package. However, npi also provides functions that can help you summarize and transform your search results.

npi has two main helper functions for working with search results: npi_summarize() and npi_flatten().

Run npi_summarize() on your results to see a more human-readable overview of your search results. Specifically, the function returns the NPI number, provider’s name, enumeration type (individual or organizational provider), primary address, phone number, and primary taxonomy (area of practice). If no taxonomy is marked as primary for a record, the first taxonomy listed for that NPI is returned:

npi_summarize(nyc)
#> # A tibble: 10 × 6
#>         npi name  enumeration_type primary_practice_add…¹ phone primary_taxonomy
#>       <int> <chr> <chr>            <chr>                  <chr> <chr>           
#>  1   1.19e9 ALYS… Individual       5 E 98TH ST FL SREET4… 212-… Physician Assis…
#>  2   1.31e9 MARK… Individual       16 PARK PL, NEW YORK,… 212-… Orthopaedic Sur…
#>  3   1.64e9 SAKS… Individual       10 E 102ND ST, NEW YO… 212-… Nurse Practitio…
#>  4   1.35e9 SARA… Individual       1335 DUBLIN RD STE 20… 614-… Occupational Th…
#>  5   1.56e9 AMY … Individual       1176 5TH AVE, NEW YOR… 212-… Psychiatry & Ne…
#>  6   1.79e9 NOAH… Individual       140 BERGEN STREET LEV… 973-… Internal Medici…
#>  7   1.56e9 ROBY… Individual       9 HOPE AVE STE 500, W… 781-… Nurse Practitio…
#>  8   1.96e9 LENO… Organization     100 E 77TH ST, NEW YO… 212-… Internal Medici…
#>  9   1.43e9 YONG… Individual       34 MAPLE ST, NORWALK,… 203-… Obstetrics & Gy…
#> 10   1.33e9 RAJE… Individual       12401 E 17TH AVE, AUR… 347-… Nurse Anestheti…
#> # ℹ abbreviated name: ¹​primary_practice_address

As seen above, the data frame returned by npi_search() has a nested structure. Although all the data in a single row relates to one NPI, each list column contains a list of one or more values corresponding to the NPI for that row. For example, a provider’s NPI record may have multiple associated addresses, phone numbers, taxonomies, and other attributes, all of which live in the same row of the data frame.

Because nested structures can be a little tricky to work with, the npi includes npi_flatten(), a function that transforms the data frame into a flatter (i.e., unnested and merged) structure that’s easier to use. npi_flatten() performs the following transformations:

• unnest the list columns
• prefix the name of each unnested column with the name of its original list column
• left-join the data together by NPI

npi_flatten() supports a variety of approaches to flattening the results from npi_search(). One extreme is to flatten everything at once:

npi_flatten(nyc)
#> # A tibble: 48 × 42
#>           npi basic_first_name basic_last_name basic_credential
#>         <int> <chr>            <chr>           <chr>           
#>  1 1194276360 ALYSSA           COWNAN          PA              
#>  2 1194276360 ALYSSA           COWNAN          PA              
#>  3 1306849641 MARK             MOHRMANN        MD              
#>  4 1306849641 MARK             MOHRMANN        MD              
#>  5 1306849641 MARK             MOHRMANN        MD              
#>  6 1306849641 MARK             MOHRMANN        MD              
#>  7 1326403213 RAJEE            KRAUSE          AGPCNP-C        
#>  8 1326403213 RAJEE            KRAUSE          AGPCNP-C        
#>  9 1326403213 RAJEE            KRAUSE          AGPCNP-C        
#> 10 1326403213 RAJEE            KRAUSE          AGPCNP-C        
#> # ℹ 38 more rows
#> # ℹ 38 more variables: basic_sole_proprietor <chr>, basic_gender <chr>,
#> #   basic_enumeration_date <chr>, basic_last_updated <chr>, basic_status <chr>,
#> #   basic_name <chr>, basic_name_prefix <chr>, basic_middle_name <chr>,
#> #   basic_organization_name <chr>, basic_organizational_subpart <chr>,
#> #   basic_authorized_official_credential <chr>,
#> #   basic_authorized_official_first_name <chr>, …

However, due to the number of fields and the large number of potential combinations of values, this approach is best suited to small datasets. More likely, you’ll want to flatten a small number of list columns from the original data frame in one pass, repeating the process with other list columns you want and merging after the fact. For example, to flatten basic provider and provider taxonomy information, supply the corresponding list columns as a vector of names to the cols argument:

# Flatten basic provider info and provider taxonomy, preserving the relationship
# of each to NPI number and discarding other list columns.
npi_flatten(nyc, cols = c("basic", "taxonomies"))
#> # A tibble: 20 × 26
#>           npi basic_first_name basic_last_name basic_credential    
#>         <int> <chr>            <chr>           <chr>               
#>  1 1194276360 ALYSSA           COWNAN          PA                  
#>  2 1306849641 MARK             MOHRMANN        MD                  
#>  3 1306849641 MARK             MOHRMANN        MD                  
#>  4 1326403213 RAJEE            KRAUSE          AGPCNP-C            
#>  5 1326403213 RAJEE            KRAUSE          AGPCNP-C            
#>  6 1326403213 RAJEE            KRAUSE          AGPCNP-C            
#>  7 1346604592 SARAH            LOWRY           OTR/L               
#>  8 1346604592 SARAH            LOWRY           OTR/L               
#>  9 1427454529 YONGHONG         TAN             <NA>                
#> 10 1558362566 AMY              TIERSTEN        M.D.                
#> 11 1558713628 ROBYN            NOHLING         FNP-BC, RD, LDN, MSN
#> 12 1558713628 ROBYN            NOHLING         FNP-BC, RD, LDN, MSN
#> 13 1558713628 ROBYN            NOHLING         FNP-BC, RD, LDN, MSN
#> 14 1558713628 ROBYN            NOHLING         FNP-BC, RD, LDN, MSN
#> 15 1558713628 ROBYN            NOHLING         FNP-BC, RD, LDN, MSN
#> 16 1558713628 ROBYN            NOHLING         FNP-BC, RD, LDN, MSN
#> 17 1639173065 SAKSHI           DUA             M.D.                
#> 18 1639173065 SAKSHI           DUA             M.D.                
#> 19 1790786416 NOAH             GOLDMAN         M.D.                
#> 20 1962983775 <NA>             <NA>            <NA>                
#> # ℹ 22 more variables: basic_sole_proprietor <chr>, basic_gender <chr>,
#> #   basic_enumeration_date <chr>, basic_last_updated <chr>, basic_status <chr>,
#> #   basic_name <chr>, basic_name_prefix <chr>, basic_middle_name <chr>,
#> #   basic_organization_name <chr>, basic_organizational_subpart <chr>,
#> #   basic_authorized_official_credential <chr>,
#> #   basic_authorized_official_first_name <chr>,
#> #   basic_authorized_official_last_name <chr>, …

Just like credit card numbers, NPI numbers can be mistyped or corrupted in transit. Likewise, officially-issued NPI numbers have a check digit for error-checking purposes. Use npi_is_valid() to check whether an NPI number you’ve encountered is validly constructed:

# Validate NPIs
npi_is_valid(1234567893)
#> [1] TRUE
npi_is_valid(1234567898)
#> [1] FALSE

Note that this function doesn’t check whether the NPI numbers are activated or deactivated (see #22). It merely checks for the number’s consistency with the NPI specification. As such, it can help you detect and handle data quality issues early.

A user agent is a way for the software interacting with an API to tell it who or what is making the request. This helps the API’s maintainers understand what systems are using the API. By default, when npi makes a request to the NPPES API, the request header references the name of the package and the URL for the repository (e.g., ‘npi/0.2.0.9000 (https://github.com/ropensci/npi)’). If you want to set a custom user agent, update the value of the npi_user_agent option. For example, for version 1.0.0 of an app called “my_app”, you could run the following code:

options(npi_user_agent = "my_app/1.0.0")

npi has a website with release notes, documentation on all user functions, and examples showing how the package can be used.

Did you spot a bug? I’d love to hear about it at the issues page.

Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Interested in learning how you can contribute to npi? Head over to the contributor guide—and thanks for considering!

For the latest citation, see the Authors and Citation page on the package website.

MIT (c) Frank Farach

This package’s logo is licensed under CC BY-SA 4.0 and co-created by Frank Farach and Sam Parmar. The logo uses a modified version of an image of the Rod of Asclepius and a magnifying glass that is attributed to Evanherk, GFDL.