Access the U.S. National Provider Identifier Registry API
Provide access to the API for the U.S. National Provider Identifier (NPI) Registry Public Search provided by the Center for Medicare and Medicaid Services (CMS): https://npiregistry.cms.hhs.gov/.
This package can be installed directly from this Github repo:
devtools::install_github("frankfarach/npi")
library(npi)search_npi() searches the public NPI Registry using the parameters made available by the API and returns the results as a tidy data frame, or tibble.
# Search for orthopedic individual providers in Atlanta, Georgia
res <- search_npi(provider_type = 1,
taxonomy = "Orthoped*",
city = "Atlanta",
state = "GA")
res
#> # A tibble: 61 x 18
#> number enumeration_type created_epoch last_updated_epoch
#> <int> <chr> <dttm> <dttm>
#> 1 1801876693 NPI-1 2006-01-17 16:00:00 2015-05-12 15:44:26
#> 2 1265409692 NPI-1 2006-03-02 16:00:00 2014-06-02 17:00:00
#> 3 1124088042 NPI-1 2006-03-23 16:00:00 2008-04-07 17:00:00
#> 4 1558321471 NPI-1 2006-03-23 16:00:00 2008-04-07 17:00:00
#> 5 1174583082 NPI-1 2006-03-23 16:00:00 2010-03-17 17:00:00
#> 6 1205896693 NPI-1 2006-03-27 16:00:00 2008-04-07 17:00:00
#> 7 1255392668 NPI-1 2006-03-27 16:00:00 2008-04-07 17:00:00
#> 8 1215998638 NPI-1 2006-03-27 16:00:00 2008-04-07 17:00:00
#> 9 1689637746 NPI-1 2006-04-09 17:00:00 2011-06-01 17:00:00
#> 10 1730144775 NPI-1 2006-04-19 17:00:00 2008-04-07 17:00:00
#> # ... with 51 more rows, and 14 more variables: taxonomies <list>,
#> # addresses <list>, identifiers <list>, status <chr>, credential <list>,
#> # first_name <chr>, last_name <chr>, middle_name <chr>, name <chr>,
#> # gender <chr>, sole_proprietor <chr>, last_updated <chr>,
#> # enumeration_date <chr>, name_prefix <chr>The data returned from search_npi() is organized according to its relationship to the NPI (number column). Data elements with a 1-to-1 relationship with NPI appear in vector columns, whereas elements with a many-to-1 relationship with NPI exist within list columns. Each element of a list column is a list of tibbles.
There are three such columns:
taxonomies: Service classification and licnese informationaddresses: Location and mailing address informationidentifiers: Miscellaneous provider identifiers and credential information
Any of these columns can be extracted as a tidy data frame using dplyr::unnest():
tax <- res %>%
select(number, taxonomies) %>%
unnest()
tax
#> # A tibble: 106 x 7
#> number state code primary license desc taxonomy_group
#> <int> <chr> <chr> <lgl> <chr> <chr> <chr>
#> 1 1801876693 "" 363A00000X F "" Physician … <NA>
#> 2 1801876693 "" 246ZX2200X T "" Specialist… <NA>
#> 3 1265409692 GA 2251X0800X T PT007710 Physical T… <NA>
#> 4 1124088042 GA 2251X0800X F PT001113 Physical T… <NA>
#> 5 1124088042 GA 2251H1200X T PT001113 Physical T… <NA>
#> 6 1558321471 GA 2251X0800X T PT008033 Physical T… <NA>
#> 7 1174583082 GA 2251X0800X T PT002690 Physical T… <NA>
#> 8 1205896693 GA 2251X0800X T PT002287 Physical T… <NA>
#> 9 1255392668 GA 2251X0800X T PT004041 Physical T… <NA>
#> 10 1215998638 GA 2251X0800X T PT007851 Physical T… <NA>
#> # ... with 96 more rowsBy repeating this process with other list columns, you can create multiple tidy data frames and join them to make your own master tidy data frame.
# Create tidy data frame of identifiers
ids <- res %>%
select(number, identifiers) %>%
unnest()
ids
#> # A tibble: 9 x 6
#> number code issuer state identifier desc
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1265409692 01 RR MEDICARE GA P00785970 Other
#> 2 1689637746 05 "" GA 591304637A MEDICAID
#> 3 1689637746 05 "" GA 591304637C MEDICAID
#> 4 1689637746 05 "" GA 591304637B MEDICAID
#> 5 1790714434 01 BCBS AUSTELL LOCATION GA 52198163006 Other
#> 6 1790714434 01 BCBS WOODSTOCK LOCATION GA 52198163004 Other
#> 7 1790714434 01 BCBS MARIETTA LOCATION GA 52198163002 Other
#> 8 1790714434 01 BCBS DOUGLASVILLE LOCATIO GA 52198163008 Other
#> 9 1720167083 01 STATE BOARD OF P.T. GA PT007055 Other
# Join taxonomies and identifiers by `number`
left_join(tax, ids, by = "number")
#> # A tibble: 113 x 12
#> number state.x code.x primary license desc.x taxonomy_group code.y
#> <int> <chr> <chr> <lgl> <chr> <chr> <chr> <chr>
#> 1 1.80e⁹ "" 363A0… F "" Physician… <NA> <NA>
#> 2 1.80e⁹ "" 246ZX… T "" Specialis… <NA> <NA>
#> 3 1.27e⁹ GA 2251X… T PT0077… Physical … <NA> 01
#> 4 1.12e⁹ GA 2251X… F PT0011… Physical … <NA> <NA>
#> 5 1.12e⁹ GA 2251H… T PT0011… Physical … <NA> <NA>
#> 6 1.56e⁹ GA 2251X… T PT0080… Physical … <NA> <NA>
#> 7 1.17e⁹ GA 2251X… T PT0026… Physical … <NA> <NA>
#> 8 1.21e⁹ GA 2251X… T PT0022… Physical … <NA> <NA>
#> 9 1.26e⁹ GA 2251X… T PT0040… Physical … <NA> <NA>
#> 10 1.22e⁹ GA 2251X… T PT0078… Physical … <NA> <NA>
#> # ... with 103 more rows, and 4 more variables: issuer <chr>,
#> # state.y <chr>, identifier <chr>, desc.y <chr>By default, search_npi() will return at most 200 records per request, which is the maximum set by the API. To return additional records, specify the number of records to skip using the skip argument.
# Return records 200-400
search_npi(
city = "San Francisco",
state = "CA",
skip = 200
)
#> # A tibble: 200 x 33
#> number enumeration_type created_epoch last_updated_epoch
#> <int> <chr> <dttm> <dttm>
#> 1 1487646758 NPI-1 2005-08-21 17:00:00 2013-03-03 16:00:00
#> 2 1427040732 NPI-1 2005-08-21 17:00:00 2018-03-19 17:00:00
#> 3 1417940685 NPI-1 2005-08-22 17:00:00 2016-06-06 12:55:07
#> 4 1003808338 NPI-1 2005-08-22 17:00:00 2008-12-10 16:00:00
#> 5 1609868934 NPI-1 2005-08-22 17:00:00 2007-07-07 17:00:00
#> 6 1942293071 NPI-2 2005-08-23 17:00:00 2007-07-07 17:00:00
#> 7 1891172219 NPI-1 2015-05-04 17:00:00 2015-05-05 15:07:36
#> 8 1639162514 NPI-1 2005-08-24 17:00:00 2012-09-17 17:00:00
#> 9 1245223031 NPI-1 2005-08-25 17:00:00 2007-07-07 17:00:00
#> 10 1689667495 NPI-1 2005-08-25 17:00:00 2014-03-18 17:00:00
#> # ... with 190 more rows, and 29 more variables: taxonomies <list>,
#> # addresses <list>, identifiers <list>, status <chr>, credential <list>,
#> # first_name <chr>, last_name <chr>, last_updated <chr>, name <chr>,
#> # gender <chr>, sole_proprietor <chr>, name_prefix <chr>,
#> # enumeration_date <chr>, middle_name <chr>, name_suffix <chr>,
#> # authorized_official_telephone_number <chr>,
#> # authorized_official_middle_name <chr>,
#> # authorized_official_last_name <chr>, organization_name <chr>,
#> # organizational_subpart <chr>, authorized_official_name_prefix <chr>,
#> # authorized_official_title_or_position <chr>,
#> # authorized_official_first_name <chr>, deactivation_date <chr>,
#> # reactivation_date <chr>, authorized_official_credential <chr>,
#> # parent_organization_legal_business_name <chr>,
#> # parent_organization_ein <chr>, authorized_official_name_suffix <chr>Use is_valid_npi() to check whether a candidate number is a valid NPI number:
# Validate one-off NPIs
is_valid_npi(1234567893)
#> [1] TRUE
is_valid_npi(1234567898)
#> [1] FALSEPlease note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
MIT (c) Frank Farach