nlm <- function(terms) {
req <- request("https://clinicaltables.nlm.nih.gov/api/npi_idv/v3/search?") |>
req_url_query(
terms = terms,
maxList = 500,
count = 500,
offset = 0L,
.space = "form"
)
resp <- req_perform(req) |> resp_simple_json()
n <- resp[[1]]
if (n > 7500L) {
cli::cli_abort(c(
"Your search returned {.strong {.val {n}}} results.",
"x" = "The NLM API limit is {.strong {.emph 7,500}}."))
}
results <- resp[[4]] |>
as.data.frame() |>
set_names(c("name", "npi", "specialty", "address")) |>
as_tbl()
nreq <- offset_length(n, 500) > 1
cli_n_results_requests(n, 500)
if (false(nreq)) {
return(results)
} else {
results2 <- req_perform_iterative(
req,
next_req = iterate_with_offset(
param_name = "offset",
start = 500,
offset = 500,
resp_complete = is_complete_with_limit(500))) |>
map(
function(x) {
x <- resp_simple_json(x)
x[[4]] |>
as.data.frame() |>
set_names(c("name", "npi", "specialty", "address"))
}) |>
rowbind()
as_tbl(rowbind(results, results2))
}
}8 NPI Registry
NLM
NPPES
- NPPES API Help
- Contact:
mailto:NPIFiles@cms.hhs.gov
convert_epoch <- \(x) {
as.Date(
as.POSIXct.numeric(
as.numeric(x) / 1000,
origin = "1970-01-01"
)
)
}
nppes <- function(npi = NULL,
entity = NULL,
first = NULL,
last = NULL,
organization = NULL,
name_type = NULL,
taxonomy_desc = NULL,
city = NULL,
state = NULL,
zip = NULL,
country = NULL) {
x <- request("https://npiregistry.cms.hhs.gov/api/?version=2.1") |>
req_url_query(
number = npi,
enumeration_type = entity,
first_name = first,
last_name = last,
name_purpose = name_type,
organization_name = organization,
taxonomy_description = taxonomy_desc,
city = city,
state = state,
postal_code = zip,
country_code = country,
limit = 1200L,
skip = 0L
) |>
perform_simple()
list(
count = x$result_count,
results = x$results |> as_tbl()
)
}ex <- nppes(npi = 1528060837)
get_vars(ex$results, is_character) |>
fcompute(
npi = number,
entity = factor_(
val_match(
enumeration_type,
"NPI-1" ~ "I",
"NPI-2" ~ "O",
.default = "X")),
date_created = convert_epoch(created_epoch),
last_updated = convert_epoch(last_updated_epoch)
) |>
add_vars(slt(ex$results$basic, -enumeration_date, -last_updated, -status))# A tibble: 1 × 12
npi entity date_created last_updated first_name last_name middle_name
* <chr> <fct> <date> <date> <chr> <chr> <chr>
1 1528060837 I 2005-06-01 2007-07-09 JOHN SARGEANT B
# ℹ 5 more variables: credential <chr>, sole_proprietor <chr>, sex <chr>,
# name_prefix <chr>, name_suffix <chr># get_vars(ex$results, is_list)
yank(ex$results$addresses) country_code country_name address_purpose address_type address_1
1 US United States MAILING DOM 1939 OLD ANNAPOLIS RD
2 US United States LOCATION DOM 6000 EXECUTIVE BLVD
city state postal_code telephone_number address_2 fax_number
1 WOODBINE MD 217978201 301-854-6748 <NA> <NA>
2 ROCKVILLE MD 208523803 301-816-0020 STE 201 301-816-0334yank(ex$results$taxonomies) code taxonomy_group desc state license primary
1 225100000X Physical Therapist MD 14262 TRUEyank(ex$results$practiceLocations)yank(ex$results$identifiers) code desc issuer identifier state
1 01 Other (non-Medicare) Aetna HMO 0129008 MD
2 01 Other (non-Medicare) DOL/OWCP 146574500 MD
3 01 Other (non-Medicare) United Healthcare 230033 MD
4 01 Other (non-Medicare) MDIPA/Alliance/MLH/OC 38311 MD
5 01 Other (non-Medicare) Cigna 4074069 MD
6 01 Other (non-Medicare) Aetna PPO 4296824 MD
7 01 Other (non-Medicare) MCR Railroad retiremnt 650003825 DC
8 01 Other (non-Medicare) BC/BS Non Provider# k366 MDempty(yank(ex$results$endpoints))[1] TRUEempty(yank(ex$results$other_names))[1] TRUEnpi_kind [1] 1225701881 1174270805 1235702796 1962116806 1013647569 1306500665
[7] 1982296737 1083295638 1841967825 1891390084 1275117269 1992338701
[13] 1891355863 1548743511 1023473279 1861857013 1689182859 1982059275kind <- purrr::map(
npi_kind,
function(x) {
nppes(npi = x)$results
},
.progress = TRUE)
kind[[1]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1627585585000 NPI-2 1707768606000 1225701881 <df [2 × 9]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,10]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[2]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1646266470000 NPI-2 1716222184000 1174270805 <df [2 × 9]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,12]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[3]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1626971928000 NPI-2 1707768904000 1235702796 <df [2 × 10]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,10]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[4]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1672956101000 NPI-2 1716223626000 1962116806 <df [2 × 9]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,13]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[5]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1655341041000 NPI-2 1716221224000 1013647569 <df [2 × 10]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,12]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[6]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1635005507000 NPI-2 1716222487000 1306500665 <df [2 × 9]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,11]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[7]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1612386220000 NPI-2 1612386220000 1982296737 <df [2 × 10]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,12]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[8]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1618524374000 NPI-2 1716221532000 1083295638 <df [2 × 9]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,11]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[9]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1630001926000 NPI-2 1716222950000 1841967825 <df [2 × 9]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,12]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[10]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1607040802000 NPI-2 1716223303000 1891390084 <df [2 × 9]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,13]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[11]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1620327662000 NPI-2 1716222333000 1275117269 <df [2 × 9]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,11]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[12]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1582213960000 NPI-2 1701120143000 1992338701 <df [2 × 9]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,11]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[13]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1561054181000 NPI-2 1582304583000 1891355863 <df [2 × 9]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,11]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[14]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1536946216000 NPI-2 1536946216000 1548743511 <df [2 × 9]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,10]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[15]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1450192387000 NPI-2 1707767268000 1023473279 <df [2 × 11]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,13]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[16]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1450191708000 NPI-2 1707768221000 1861857013 <df [2 × 10]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,13]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[17]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1516648288000 NPI-2 1683135725000 1689182859 <df [2 × 10]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,12]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>
[[18]]
# A tibble: 1 × 11
created_epoch enumeration_type last_updated_epoch number addresses
* <chr> <chr> <chr> <chr> <list>
1 1461596970000 NPI-2 1461596970000 1982059275 <df [2 × 10]>
# ℹ 6 more variables: practiceLocations <list>, basic <df[,11]>,
# taxonomies <list>, identifiers <list>, endpoints <list>, other_names <list>purrr::list_rbind(kind) |>
mtt(npi = number,
entity = factor_(
val_match(
enumeration_type,
"NPI-1" ~ "I",
"NPI-2" ~ "O",
.default = "X")),
date_created = convert_epoch(created_epoch),
last_updated = convert_epoch(last_updated_epoch)) |>
slt(-number, -enumeration_type, -created_epoch, -last_updated_epoch)# A tibble: 18 × 11
addresses practiceLocations basic$organization_n…¹ taxonomies identifiers
<list> <list> <chr> <list> <list>
1 <df [2 × 9]> <list [0]> VIOS FERTILITY INSTIT… <df> <list [0]>
2 <df [2 × 9]> <list [0]> PEACH STATE MEDICAL P… <df> <list [0]>
3 <df [2 × 10]> <list [0]> VIOS FERTILITY INSTIT… <df> <list [0]>
4 <df [2 × 9]> <list [0]> PEACH STATE MEDICAL P… <df> <list [0]>
5 <df [2 × 10]> <list [0]> PEACH STATE MEDICAL P… <df> <list [0]>
6 <df [2 × 9]> <list [0]> PEACH STATE MEDICAL P… <df> <list [0]>
7 <df [2 × 10]> <list [0]> KINDBODY OF FL MEDICA… <df> <list [0]>
8 <df [2 × 9]> <df [2 × 9]> PEACH STATE MEDICAL P… <df> <list [0]>
9 <df [2 × 9]> <list [0]> PEACH STATE MEDICAL P… <df> <list [0]>
10 <df [2 × 9]> <list [0]> PEACH STATE MEDICAL P… <df> <list [0]>
11 <df [2 × 9]> <list [0]> PEACH STATE MEDICAL P… <df> <list [0]>
12 <df [2 × 9]> <list [0]> GARDEN STATE MEDICAL … <df> <list [0]>
13 <df [2 × 9]> <df [3 × 10]> FAHIMEH SASAN,D.O., P… <df> <list [0]>
14 <df [2 × 9]> <list [0]> EMPIRE MEDICAL PRACTI… <df> <list [0]>
15 <df [2 × 11]> <list [0]> VIOS FERTILITY INSTIT… <df> <list [0]>
16 <df [2 × 10]> <list [0]> CENTERS FOR REPRODUCT… <df> <list [0]>
17 <df [2 × 10]> <list [0]> VIOS FERTILITY INSTIT… <df> <list [0]>
18 <df [2 × 10]> <list [0]> VIOS FERTILITY INSTIT… <df> <list [0]>
# ℹ abbreviated name: ¹basic$organization_name
# ℹ 19 more variables: basic$organizational_subpart <chr>,
# $enumeration_date <chr>, $last_updated <chr>, $certification_date <chr>,
# $status <chr>, $authorized_official_first_name <chr>,
# $authorized_official_last_name <chr>,
# $authorized_official_telephone_number <chr>,
# $authorized_official_title_or_position <chr>, …