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.
Search registry
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
#> * <chr> <chr> <list> <list> <list> <list> <list>
#> 1 12658… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 2 16796… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 3 19822… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 4 14873… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 5 15889… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 6 19420… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 7 17309… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 8 16297… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 9 16391… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 10 14579… 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
#> * <chr> <chr> <list> <list> <list> <list> <list>
#> 1 17706… Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 2 16398… Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 3 19520… Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 4 10034… Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 5 13465… Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 6 19728… Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 7 12251… Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 8 13369… Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 9 16292… Organization <tibble> <tibble> <tibble> <tibble> <tibble>
#> 10 17302… 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.
Increasing number of records returned
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
#> * <chr> <chr> <list> <list> <list> <list> <list>
#> 1 12658… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 2 16796… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 3 19822… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 4 14873… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 5 15889… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 6 19420… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 7 17309… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 8 16297… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 9 16391… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 10 14579… 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
#> * <chr> <chr> <list> <list> <list> <list> <list>
#> 1 12658… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 2 16796… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 3 19822… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 4 14873… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 5 15889… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 6 19420… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 7 17309… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 8 16297… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 9 16391… Individual <tibble> <tibble> <tibble> <tibble> <tibble>
#> 10 14579… 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.
Obtaining more human-readable output
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
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1265829… MARK… Individual 1090 AMSTERDAM AVENUE… 212-… Psychiatry & Ne…
#> 2 1679656… JUDI… Individual 425 RIVERSIDE DR #8C,… 212-… Student in an O…
#> 3 1982278… UNKN… Individual WESTCHESTER MEDICAL C… 914-… Student in an O…
#> 4 1487341… RAKS… Individual JACOBI MEDICAL CENTER… 718-… Emergency Medic…
#> 5 1588940… DANI… Individual 1 GUSTAVE LEVY PLACE … 212-… Student in an O…
#> 6 1942059… AJAN… Individual NA 718-… Social Worker
#> 7 1730931… SAI … Individual 1545 ATLANTIC AVENUE,… 718-… Social Worker, …
#> 8 1629701… MOHA… Individual NA 212-… Student in an O…
#> 9 1639133… MIRI… Individual 325 EAST 80TH ST #1C,… 212-… Student in an O…
#> 10 1457926… MAHM… Individual NEW YORK PRESBYTERIAN… 718-… 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_sole_proprietor basic_sex
#> <chr> <chr> <chr> <chr> <chr>
#> 1 1265829667 MARK ABROMS NO M
#> 2 1265829667 MARK ABROMS NO M
#> 3 1457926974 MAHMOOD AL-ORPHALY NO M
#> 4 1457926974 MAHMOOD AL-ORPHALY NO M
#> 5 1487341442 RAKSHEETH AGARWAL NO M
#> 6 1487341442 RAKSHEETH AGARWAL NO M
#> 7 1588940472 DANISH AHMAD NO M
#> 8 1588940472 DANISH AHMAD NO M
#> 9 1588940472 DANISH AHMAD NO M
#> 10 1588940472 DANISH AHMAD NO M
#> # ℹ 18 more rows
#> # ℹ 51 more variables: basic_enumeration_date <chr>, basic_last_updated <chr>,
#> # basic_certification_date <chr>, basic_status <chr>,
#> # basic_middle_name <chr>, basic_credential <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_sole_proprietor basic_sex
#> <chr> <chr> <chr> <chr> <chr>
#> 1 1265829667 MARK ABROMS NO M
#> 2 1457926974 MAHMOOD AL-ORPHALY NO M
#> 3 1487341442 RAKSHEETH AGARWAL NO M
#> 4 1588940472 DANISH AHMAD NO M
#> 5 1588940472 DANISH AHMAD NO M
#> 6 1629701396 MOHAMMED AKHTAR NO M
#> 7 1639133937 MIRIAM AKINS YES F
#> 8 1639133937 MIRIAM AKINS YES F
#> 9 1679656128 JUDITH ADELSON YES F
#> 10 1730931452 SAI ANUSHA AKELLA NO F
#> 11 1942059605 AJANG AJZACHI YES M
#> 12 1982278313 UNKNOWN ADILA NO F
#> # ℹ 14 more variables: basic_enumeration_date <chr>, basic_last_updated <chr>,
#> # basic_certification_date <chr>, basic_status <chr>,
#> # basic_middle_name <chr>, basic_credential <chr>, basic_name_prefix <chr>,
#> # basic_name_suffix <chr>, taxonomies_code <chr>,
#> # taxonomies_taxonomy_group <chr>, taxonomies_desc <chr>,
#> # taxonomies_state <chr>, taxonomies_license <chr>, taxonomies_primary <lgl>