This vignette provides an brief introduction to the npi package.
npi is an R package that allows R users to access the U.S. National Provider
Identifier (NPI) Registry API by the Center for Medicare and
Medicaid Services (CMS).
The package makes it easy to obtain administrative data linked to a specific individual or organizational healthcare provider. Additionally, users can perform advanced searches based on provider name, location, type of service, credentials, and many other attributes.
To explore organizational providers with primary locations in New
York City, we could use the city argument in the
npi_search(). The nyc dataset here finds 10 organizational
providers with primary locations in New York City, since 10 is the
default number of records that are returned in
npi_search(). The response is a tibble that has
high-cardinality data organized into list columns.
nyc <- npi_search(city = "New York City")
#> 10 records requested
#> Requesting records 0-10...
nyc
#> # A tibble: 10 × 11
#> npi enumeration_type basic other_names identifiers taxonomies addresses
#> * <int> <chr> <list> <list> <list> <list> <list>
#> 1 1.21e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 2 1.27e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 3 1.68e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 4 1.98e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 5 1.49e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 6 1.59e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 7 1.94e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 8 1.73e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 9 1.63e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 10 1.64e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> # ℹ 4 more variables: practice_locations <list>, endpoints <list>,
#> # created_date <dttm>, last_updated_date <dttm>Other search arguments for the function include number,
enumeration_type, taxonomy_description,
first_name, last_name,
use_first_name_alias, organization_name,
address_purpose, state,
postal_code, country_code, and
limit.
Additionally, more than one search argument can be used at once.
nyc_multi <- npi_search(city = "New York City", state = "NY", enumeration_type = "org")
#> 10 records requested
#> Requesting records 0-10...
nyc_multi
#> # A tibble: 10 × 11
#> npi enumeration_type basic other_names identifiers taxonomies addresses
#> * <int> <chr> <list> <list> <list> <list> <list>
#> 1 1.77e9 Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 2 1.64e9 Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 3 1.95e9 Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 4 1.00e9 Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 5 1.35e9 Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 6 1.97e9 Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 7 1.23e9 Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 8 1.34e9 Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 9 1.63e9 Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 10 1.73e9 Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> # ℹ 4 more variables: practice_locations <list>, endpoints <list>,
#> # created_date <dttm>, last_updated_date <dttm>Visit the function’s help page via ?npi_search after
installing and loading the package for more details.
The limit argument of npi_search() lets you
set the maximum records to return from 1 to 1200 inclusive, defaulting
to 10 records if no value is specified.
nyc_25 <- npi_search(city = "New York City", limit = 25)
#> 25 records requested
#> Requesting records 0-25...
nyc_25
#> # A tibble: 25 × 11
#> npi enumeration_type basic other_names identifiers taxonomies addresses
#> * <int> <chr> <list> <list> <list> <list> <list>
#> 1 1.21e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 2 1.27e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 3 1.68e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 4 1.98e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 5 1.49e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 6 1.59e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 7 1.94e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 8 1.73e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 9 1.63e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 10 1.64e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> # ℹ 15 more rows
#> # ℹ 4 more variables: practice_locations <list>, endpoints <list>,
#> # created_date <dttm>, last_updated_date <dttm>When using npi_search(), searches with greater than 200
records (for example 300 records) may result in multiple API calls. This
is because the API itself returns up to 200 records per request, but
allows previously requested records to be skipped.
npi_search() will automatically make additional API calls
up to the API’s limit of 1200 records for a unique set of query
parameter values, and will still return a single tibble. However, to
save time, the function only makes additional requests if needed. For
example, if you request 1200 records, and 199 are returned in the first
request, then the function does not need to make a second request
because there are no more records to return.
nyc_300 <- npi_search(city = "New York City", limit = 300)
#> 300 records requested
#> Requesting records 0-200...
#> Requesting records 200-300...
nyc_300
#> # A tibble: 300 × 11
#> npi enumeration_type basic other_names identifiers taxonomies addresses
#> * <int> <chr> <list> <list> <list> <list> <list>
#> 1 1.21e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 2 1.27e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 3 1.68e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 4 1.98e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 5 1.49e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 6 1.59e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 7 1.94e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 8 1.73e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 9 1.63e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 10 1.64e9 Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> # ℹ 290 more rows
#> # ℹ 4 more variables: practice_locations <list>, endpoints <list>,
#> # created_date <dttm>, last_updated_date <dttm>The NPPES API documentation does not specify additional API rate limitations. However, if you need more than 1200 NPI records for a set of search terms, you will need to download the NPPES Data Dissemination File.
npi_summarize() provides a more human-readable overview
of output already obtained through npi_search().
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 1.21e9 MOHA… Individual 506 LENOX AVENUE, HAR… 212-… Student in an O…
#> 2 1.27e9 MARK… Individual 1090 AMSTERDAM AVENUE… 212-… Psychiatry & Ne…
#> 3 1.68e9 JUDI… Individual 425 RIVERSIDE DR #8C,… 212-… Student in an O…
#> 4 1.98e9 UNKN… Individual WESTCHESTER MEDICAL C… 914-… Internal Medici…
#> 5 1.49e9 RAKS… Individual JACOBI MEDICAL CENTER… 718-… Student in an O…
#> 6 1.59e9 DANI… Individual <NA> 212-… Social Worker
#> 7 1.94e9 AJAN… Individual <NA> 718-… Social Worker, …
#> 8 1.73e9 SAI … Individual 1545 ATLANTIC AVENUE,… 718-… Student in an O…
#> 9 1.63e9 MOHA… Individual <NA> 212-… Student in an O…
#> 10 1.64e9 MIRI… Individual 325 EAST 80TH ST #1C,… 212-… Student in an O…
#> # ℹ abbreviated name: ¹primary_practice_addressAdditionally, users can flatten all the list columns using
npi_flatten().
npi_flatten(nyc)
#> # A tibble: 28 × 56
#> npi basic_first_name basic_last_name basic_middle_name basic_credential
#> <int> <chr> <chr> <chr> <chr>
#> 1 1.21e9 MOHAMED ABDELGADER SATI SHAMPOOL M.B.B.S
#> 2 1.21e9 MOHAMED ABDELGADER SATI SHAMPOOL M.B.B.S
#> 3 1.27e9 MARK ABROMS <NA> <NA>
#> 4 1.27e9 MARK ABROMS <NA> <NA>
#> 5 1.49e9 RAKSHEETH AGARWAL <NA> M.D.
#> 6 1.49e9 RAKSHEETH AGARWAL <NA> M.D.
#> 7 1.59e9 DANISH AHMAD A M.D.
#> 8 1.59e9 DANISH AHMAD A M.D.
#> 9 1.59e9 DANISH AHMAD A M.D.
#> 10 1.59e9 DANISH AHMAD A M.D.
#> # ℹ 18 more rows
#> # ℹ 51 more variables: basic_sole_proprietor <chr>, basic_sex <chr>,
#> # basic_enumeration_date <chr>, basic_last_updated <chr>, basic_status <chr>,
#> # basic_certification_date <chr>, basic_name_prefix <chr>,
#> # basic_name_suffix <chr>, other_names_type <chr>, other_names_code <chr>,
#> # other_names_first_name <chr>, other_names_last_name <chr>,
#> # other_names_middle_name <chr>, other_names_prefix <chr>, …Alternatively, individual columns can be flattened for each npi by
using the cols argument. Only the columns specified will be
flattened and returned with the npi column by default.
npi_flatten(nyc, cols = c("basic", "taxonomies"))
#> # A tibble: 12 × 19
#> npi basic_first_name basic_last_name basic_middle_name basic_credential
#> <int> <chr> <chr> <chr> <chr>
#> 1 1.21e9 MOHAMED ABDELGADER SATI SHAMPOOL M.B.B.S
#> 2 1.27e9 MARK ABROMS <NA> <NA>
#> 3 1.49e9 RAKSHEETH AGARWAL <NA> M.D.
#> 4 1.59e9 DANISH AHMAD A M.D.
#> 5 1.59e9 DANISH AHMAD A M.D.
#> 6 1.63e9 MOHAMMED AKHTAR ZEESHAN MD PhD
#> 7 1.64e9 MIRIAM AKINS F LCSW
#> 8 1.64e9 MIRIAM AKINS F LCSW
#> 9 1.68e9 JUDITH ADELSON D LCSW
#> 10 1.73e9 SAI ANUSHA AKELLA <NA> M.D
#> 11 1.94e9 AJANG AJZACHI <NA> DMD
#> 12 1.98e9 UNKNOWN ADILA <NA> <NA>
#> # ℹ 14 more variables: basic_sole_proprietor <chr>, basic_sex <chr>,
#> # basic_enumeration_date <chr>, basic_last_updated <chr>, basic_status <chr>,
#> # basic_certification_date <chr>, basic_name_prefix <chr>,
#> # basic_name_suffix <chr>, taxonomies_code <chr>,
#> # taxonomies_taxonomy_group <chr>, taxonomies_desc <chr>,
#> # taxonomies_primary <lgl>, taxonomies_state <chr>, taxonomies_license <chr>