README.md
In frankfarach/npi: Access the U.S. National Provider Identifier Registry API
npi 
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.
Installation
There are three ways to install the npi package:
- Install from CRAN:
install.packages("npi")
library(npi)
- Install from R-universe:
install.packages("npi", repos = "https://ropensci.r-universe.dev")
library(npi)
- Install from GitHub using the
devtools package:
devtools::install_github("ropensci/npi")
library(npi)
Usage
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.
Search the registry
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, ⁷endpoints
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 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
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.
Working with search results
npi has two main helper functions for working with search results:
npi_summarize() and npi_flatten().
Summarizing results
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_taxonomy
Flattening results
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_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>, …
Validating NPIs
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.
Set your own user agent
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")
Package Website
npi has a website with release
notes, documentation on all user functions, and examples showing how the
package can be used.
Reporting Bugs
Did you spot a bug? I’d love to hear about it at the issues
page.
Code of Conduct
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.
Contributing
Interested in learning how you can contribute to npi? Head over to the
contributor guide—and
thanks for considering!
How to cite this package
For the latest citation, see the Authors and
Citation page on the
package website.
License
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.
frankfarach/npi documentation built on Nov. 17, 2022, 11:15 p.m.
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npi
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.
Installation
There are three ways to install the npi package:
- Install from CRAN:
install.packages("npi")
library(npi)
- Install from R-universe:
install.packages("npi", repos = "https://ropensci.r-universe.dev")
library(npi)
- Install from GitHub using the
devtoolspackage:
devtools::install_github("ropensci/npi")
library(npi)
Usage
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.
Search the registry
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, ⁷endpoints
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 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
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.
Working with search results
npi has two main helper functions for working with search results:
npi_summarize() and npi_flatten().
Summarizing results
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_taxonomy
Flattening results
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_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>, …
Validating NPIs
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.
Set your own user agent
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")
Package Website
npi has a website with release
notes, documentation on all user functions, and examples showing how the
package can be used.
Reporting Bugs
Did you spot a bug? I’d love to hear about it at the issues page.
Code of Conduct
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.
Contributing
Interested in learning how you can contribute to npi? Head over to the contributor guide—and thanks for considering!
How to cite this package
For the latest citation, see the Authors and Citation page on the package website.
License
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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