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.
Install npi directly from Github using the devtools package:
devtools::install_github("frankfarach/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 fromnpi_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 organizational providers with primary
locations in New York City:
nyc <- npi_search(city = "New York City")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>
#> # … with 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,
provider_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 providerother_names: Other names used by the provideridentifiers: Other provider identifiers and credential informationtaxonomies: Service classification and license informationaddresses: Location and mailing address informationpractice_locations: Provider’s practice locationsendpoints: Details about provider’s endpoints for health information exchange
If you’re comfortable working with list
columns, this may be all you
need from the package. But let’s not stop just yet, because npi
provides convenience functions to summarize and extract the data you
need.
Run npi_summarize() on your results to see a more human-readable
overview of what we’ve got:
npi_summarize(nyc)
#> # A tibble: 10 × 6
#> npi name enumeration_type primary_practic… phone primary_taxonomy
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1194276360 ALYSSA C… Individual 5 E 98TH ST FL … 212-… Physician Assis…
#> 2 1306849641 MARK MOH… Individual 16 PARK PL, NEW… 212-… Orthopaedic Sur…
#> 3 1639173065 SAKSHI D… Individual 10 E 102ND ST, … 212-… Internal Medici…
#> 4 1346604592 SARAH LO… Individual 1335 DUBLIN RD … 614-… Occupational Th…
#> 5 1558362566 AMY TIER… Individual 1176 5TH AVE, N… 212-… Internal Medici…
#> 6 1790786416 NOAH GOL… Individual 140 BERGEN STRE… 973-… Obstetrics & Gy…
#> 7 1558713628 ROBYN NO… Individual 9 HOPE AVE STE … 781-… Nurse Practitio…
#> 8 1962983775 LENOX HI… Organization 100 E 77TH ST, … 212-… Nurse Anestheti…
#> 9 1427454529 YONGHONG… Individual 34 MAPLE ST, NO… 203-… Psychiatry & Ne…
#> 10 1326403213 RAJEE KR… Individual 12401 E 17TH AV… 347-… Nurse Practitio…Suppose we just want the basic and taxonomy information for each NPI in the result in a flattened data frame:
npi_flatten(nyc, c("basic", "taxonomies"))
#> # A tibble: 20 × 26
#> npi basic_first_name basic_last_name basic_credential basic_sole_prop…
#> <int> <chr> <chr> <chr> <chr>
#> 1 1194276360 ALYSSA COWNAN PA NO
#> 2 1306849641 MARK MOHRMANN MD NO
#> 3 1306849641 MARK MOHRMANN MD NO
#> 4 1326403213 RAJEE KRAUSE AGPCNP-C NO
#> 5 1326403213 RAJEE KRAUSE AGPCNP-C NO
#> 6 1326403213 RAJEE KRAUSE AGPCNP-C NO
#> 7 1346604592 SARAH LOWRY OTR/L YES
#> 8 1346604592 SARAH LOWRY OTR/L YES
#> 9 1427454529 YONGHONG TAN <NA> NO
#> 10 1558362566 AMY TIERSTEN M.D. YES
#> 11 1558713628 ROBYN NOHLING FNP-BC, RD, LDN… YES
#> 12 1558713628 ROBYN NOHLING FNP-BC, RD, LDN… YES
#> 13 1558713628 ROBYN NOHLING FNP-BC, RD, LDN… YES
#> 14 1558713628 ROBYN NOHLING FNP-BC, RD, LDN… YES
#> 15 1558713628 ROBYN NOHLING FNP-BC, RD, LDN… YES
#> 16 1558713628 ROBYN NOHLING FNP-BC, RD, LDN… YES
#> 17 1639173065 SAKSHI DUA M.D. YES
#> 18 1639173065 SAKSHI DUA M.D. YES
#> 19 1790786416 NOAH GOLDMAN M.D. NO
#> 20 1962983775 <NA> <NA> <NA> <NA>
#> # … with 21 more variables: 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>, …Or we can flatten the whole thing and prune back later:
npi_flatten(nyc)
#> # A tibble: 48 × 42
#> npi basic_first_name basic_last_name basic_credential basic_sole_prop…
#> <int> <chr> <chr> <chr> <chr>
#> 1 1194276360 ALYSSA COWNAN PA NO
#> 2 1194276360 ALYSSA COWNAN PA NO
#> 3 1306849641 MARK MOHRMANN MD NO
#> 4 1306849641 MARK MOHRMANN MD NO
#> 5 1306849641 MARK MOHRMANN MD NO
#> 6 1306849641 MARK MOHRMANN MD NO
#> 7 1326403213 RAJEE KRAUSE AGPCNP-C NO
#> 8 1326403213 RAJEE KRAUSE AGPCNP-C NO
#> 9 1326403213 RAJEE KRAUSE AGPCNP-C NO
#> 10 1326403213 RAJEE KRAUSE AGPCNP-C NO
#> # … with 38 more rows, and 37 more variables: 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>, …Now we’re ready to do whatever else we need to do with this data. Under
the hood, npi_flatten() has done a lot of data wrangling for us:
- unnested the specified list columns
- avoided potential naming collisions by prefixing the unnested names by their originating column name
- joined the data together by NPI
Use npi_is_valid() to check whether each element of a vector of
candidate numbers is a validly constructed NPI number:
# Validate off NPIs
npi_is_valid(1234567893)
#> [1] TRUE
npi_is_valid(1234567898)
#> [1] FALSENote that this function doesn’t check whether the NPI numbers are activated or deactivated (see #22).
By default, all request headers include a user agent that references
this repository. You can customize the user agent by setting the
npi_user_agent option:
options(npi_user_agent = "my_awesome_user_agent")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 project is released with a Contributor Code of Conduct. By participating in 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.
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