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:
install.packages("npi")
library(npi)install.packages("npi", repos = "https://ropensci.r-universe.dev")
library(npi)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 enume…¹ basic other_…² identi…³ taxono…⁴ addres…⁵ practi…⁶ endpoi…⁷
#> * <int> <chr> <list> <list> <list> <list> <list> <list> <list>
#> 1 1.19e9 Indivi… <tibble> <tibble> <tibble> <tibble> <tibble> <tibble> <tibble>
#> 2 1.31e9 Indivi… <tibble> <tibble> <tibble> <tibble> <tibble> <tibble> <tibble>
#> 3 1.64e9 Indivi… <tibble> <tibble> <tibble> <tibble> <tibble> <tibble> <tibble>
#> 4 1.35e9 Indivi… <tibble> <tibble> <tibble> <tibble> <tibble> <tibble> <tibble>
#> 5 1.56e9 Indivi… <tibble> <tibble> <tibble> <tibble> <tibble> <tibble> <tibble>
#> 6 1.79e9 Indivi… <tibble> <tibble> <tibble> <tibble> <tibble> <tibble> <tibble>
#> 7 1.56e9 Indivi… <tibble> <tibble> <tibble> <tibble> <tibble> <tibble> <tibble>
#> 8 1.96e9 Organi… <tibble> <tibble> <tibble> <tibble> <tibble> <tibble> <tibble>
#> 9 1.43e9 Indivi… <tibble> <tibble> <tibble> <tibble> <tibble> <tibble> <tibble>
#> 10 1.33e9 Indivi… <tibble> <tibble> <tibble> <tibble> <tibble> <tibble> <tibble>
#> # … with 2 more variables: created_date <dttm>, last_updated_date <dttm>, and
#> # abbreviated variable names ¹enumeration_type, ²other_names, ³identifiers,
#> # ⁴taxonomies, ⁵addresses, ⁶practice_locations, ⁷endpointsThe 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
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 exchangeA 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):
npi_summarize(nyc)
#> # A tibble: 10 × 6
#> npi name enume…¹ prima…² phone prima…³
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1194276360 ALYSSA COWNAN Indivi… 5 E 98… 212-… Physic…
#> 2 1306849641 MARK MOHRMANN Indivi… 16 PAR… 212-… Orthop…
#> 3 1639173065 SAKSHI DUA Indivi… 10 E 1… 212-… Nurse …
#> 4 1346604592 SARAH LOWRY Indivi… 1335 D… 614-… Occupa…
#> 5 1558362566 AMY TIERSTEN Indivi… 1176 5… 212-… Psychi…
#> 6 1790786416 NOAH GOLDMAN Indivi… 140 BE… 973-… Intern…
#> 7 1558713628 ROBYN NOHLING Indivi… 9 HOPE… 781-… Nurse …
#> 8 1962983775 LENOX HILL MEDICAL ANESTHESIOLOGY, … Organi… 100 E … 212-… Intern…
#> 9 1427454529 YONGHONG TAN Indivi… 34 MAP… 203-… Obstet…
#> 10 1326403213 RAJEE KRAUSE Indivi… 12401 … 347-… Nurse …
#> # … with abbreviated variable names ¹enumeration_type,
#> # ²primary_practice_address, ³primary_taxonomyAs 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:
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_fi…¹ basic…² basic…³ basic…⁴ basic…⁵ basic…⁶ basic…⁷ basic…⁸
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1194276360 ALYSSA COWNAN PA NO F 2016-1… 2018-0… A
#> 2 1194276360 ALYSSA COWNAN PA NO F 2016-1… 2018-0… A
#> 3 1306849641 MARK MOHRMA… MD NO M 2005-0… 2019-0… A
#> 4 1306849641 MARK MOHRMA… MD NO M 2005-0… 2019-0… A
#> 5 1306849641 MARK MOHRMA… MD NO M 2005-0… 2019-0… A
#> 6 1306849641 MARK MOHRMA… MD NO M 2005-0… 2019-0… A
#> 7 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> 8 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> 9 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> 10 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> # … with 38 more rows, 33 more variables: 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>,
#> # basic_authorized_official_middle_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_fi…¹ basic…² basic…³ basic…⁴ basic…⁵ basic…⁶ basic…⁷ basic…⁸
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1194276360 ALYSSA COWNAN PA NO F 2016-1… 2018-0… A
#> 2 1306849641 MARK MOHRMA… MD NO M 2005-0… 2019-0… A
#> 3 1306849641 MARK MOHRMA… MD NO M 2005-0… 2019-0… A
#> 4 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> 5 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> 6 1326403213 RAJEE KRAUSE AGPCNP… NO F 2015-1… 2019-0… A
#> 7 1346604592 SARAH LOWRY OTR/L YES F 2016-0… 2018-0… A
#> 8 1346604592 SARAH LOWRY OTR/L YES F 2016-0… 2018-0… A
#> 9 1427454529 YONGHONG TAN <NA> NO F 2014-1… 2018-1… A
#> 10 1558362566 AMY TIERST… M.D. YES F 2005-0… 2019-0… A
#> 11 1558713628 ROBYN NOHLING FNP-BC… YES F 2016-0… 2018-0… A
#> 12 1558713628 ROBYN NOHLING FNP-BC… YES F 2016-0… 2018-0… A
#> 13 1558713628 ROBYN NOHLING FNP-BC… YES F 2016-0… 2018-0… A
#> 14 1558713628 ROBYN NOHLING FNP-BC… YES F 2016-0… 2018-0… A
#> 15 1558713628 ROBYN NOHLING FNP-BC… YES F 2016-0… 2018-0… A
#> 16 1558713628 ROBYN NOHLING FNP-BC… YES F 2016-0… 2018-0… A
#> 17 1639173065 SAKSHI DUA M.D. YES F 2005-0… 2019-0… A
#> 18 1639173065 SAKSHI DUA M.D. YES F 2005-0… 2019-0… A
#> 19 1790786416 NOAH GOLDMAN M.D. NO M 2005-0… 2018-0… A
#> 20 1962983775 <NA> <NA> <NA> <NA> <NA> 2018-0… 2018-0… A
#> # … with 17 more variables: 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>,
#> # basic_authorized_official_middle_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] FALSENote 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 (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.
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