#> Error in library(tidyverse): there is no package called 'tidyverse'
Individual Provider
vctrs::vec_rbind(
display_long(providers(pac = 7810891009)) |> tibble::add_column(source = "`providers()`"),
display_long(reassignments(pac = 7810891009)) |> tibble::add_column(source = "`reassignments()`"),
display_long(clinicians(pac = 7810891009)) |> tibble::add_column(source = "`clinicians()`"),
display_long(nppes(npi = 1043245657)) |> tibble::add_column(source = "`nppes()`"),
display_long(order_refer(npi = 1043245657)) |> tibble::add_column(source = "`order_refer()`")) |>
distinct(name, value, .keep_all = TRUE) |>
gt(groupname_col = "source",
row_group_as_column = TRUE, process_md = TRUE) |>
opt_table_font(font = google_font(name = "JetBrains Mono")) |>
tab_options(column_labels.hidden = TRUE,
table.width = px(600),
heading.background.color = "black",
heading.align = "left",
stub_row_group.font.weight = "bold") |>
tab_header(title = md("**PROVIDER**: Mark, K. Fung, M.D.")) |>
opt_horizontal_padding(scale = 2) |>
opt_all_caps()
#> Error in distinct(vctrs::vec_rbind(tibble::add_column(display_long(providers(pac = 7810891009)), : could not find function "distinct"
affiliations(pac = 7810891009) |>
pull(facility_ccn) |>
map_dfr(~hospitals(facility_ccn = .x)) |>
select(-reh_conversion) |>
display_long(cols = !organization) |>
filter(!is.na(value)) |>
gt(groupname_col = "organization",
row_group_as_column = TRUE) |>
opt_table_font(font = google_font(name = "JetBrains Mono")) |>
tab_options(column_labels.hidden = TRUE,
table.width = px(800),
heading.background.color = "black",
heading.align = "left",
stub_row_group.font.weight = "bold") |>
tab_header(title = md("**FACILITY** AFFILIATIONS")) |>
opt_horizontal_padding(scale = 2) |>
opt_all_caps()
#> Error in select(map_dfr(pull(affiliations(pac = 7810891009), facility_ccn), : could not find function "select"
Exploring links between providers can lead to many interesting insights. For example, there is a hospital in New York named Elizabethtown Community Hospital.
providers(organization = "Elizabethtown Community Hospital") |>
gt_preview(top_n = 10) |>
opt_table_font(font = google_font(name = "JetBrains Mono"))
npi | pac | enid | specialty_code | specialty_description | state | organization | |
---|---|---|---|---|---|---|---|
1 | 1891785184 | 3577554138 | O20040521000534 | 12-70 | PART B SUPPLIER - CLINIC/GROUP PRACTICE | NY | ELIZABETHTOWN COMMUNITY HOSPITAL |
2 | 1891785184 | 3577554138 | O20101110000259 | 00-85 | PART A PROVIDER - CRITICAL ACCESS HOSPITAL | NY | ELIZABETHTOWN COMMUNITY HOSPITAL |
3 | 1487923637 | 3577554138 | O20190719002511 | 12-59 | PART B SUPPLIER - AMBULANCE SERVICE SUPPLIER | NY | ELIZABETHTOWN COMMUNITY HOSPITAL |
4 | 1407061591 | 3577554138 | O20220827000145 | 00-85 | PART A PROVIDER - CRITICAL ACCESS HOSPITAL | NY | ELIZABETHTOWN COMMUNITY HOSPITAL |
hospitals(organization = "Elizabethtown Community Hospital") |>
gt_preview(top_n = 10) |>
opt_table_font(font = google_font(name = "JetBrains Mono"))
npi_org | pac_org | enid_org | enid_state | facility_ccn | organization | specialty_code | specialty | incorp_date | incorp_state | structure | address | city | state | zip | location_type | multi_npi | reh_conversion | subgroup | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1891785184 | 3577554138 | O20101110000259 | NY | 331302 | ELIZABETHTOWN COMMUNITY HOSPITAL | 00-85 | PART A PROVIDER - CRITICAL ACCESS HOSPITAL | 1926-05-08 | NY | CORPORATION | 75 PARK ST | ELIZABETHTOWN | NY | 129322300 | OTHER HOSPITAL PRACTICE LOCATION | TRUE | FALSE | None |
2 | 1407061591 | 3577554138 | O20220827000145 | NY | 33Z302 | ELIZABETHTOWN COMMUNITY HOSPITAL | 00-85 | PART A PROVIDER - CRITICAL ACCESS HOSPITAL | 1926-05-08 | NY | CORPORATION | 75 PARK ST | ELIZABETHTOWN | NY | 129322300 | OTHER HOSPITAL PRACTICE LOCATION | FALSE | FALSE | None |
The Hospital Enrollment API includes only Medicare
Part A (hospital) providers, so we only get two rows back, but those
include a new data point: two facility CCNs. Plugging those into the
Facility Affiliations API, we can retrieve information
on the individual providers practicing at this hospital. First, the
all-numeric CCN (331302
):
affiliations(facility_ccn = 331302) |>
gt_preview(top_n = 20) |>
opt_table_font(font = google_font(name = "JetBrains Mono"))
npi | pac | first | middle | last | suffix | facility_type | facility_ccn | |
---|---|---|---|---|---|---|---|---|
1 | 1003815184 | 4082693676 | ARMIN | NA | AFSAR KESHMIRI | NA | Hospital | 331302 |
2 | 1023076643 | 5698798452 | JOHN | N | HENRY | NA | Hospital | 331302 |
3 | 1023377843 | 6901115278 | LINDSEY | B | WILHELM | NA | Hospital | 331302 |
4 | 1043245657 | 7810891009 | MARK | K | FUNG | NA | Hospital | 331302 |
5 | 1043397656 | 4183764558 | ANTHONY | F | TRAMONTANO | NA | Hospital | 331302 |
6 | 1043630510 | 2365749389 | RYAN | NA | WOLFE | NA | Hospital | 331302 |
7 | 1043672140 | 7214229350 | VANESSA | NA | FIORINI FURTADO | NA | Hospital | 331302 |
8 | 1053596122 | 5193802213 | TANYA | JEAN | FINCH | NA | Hospital | 331302 |
9 | 1053863100 | 8729362207 | MATTHEW | D | FARNSWORTH | NA | Hospital | 331302 |
10 | 1063536886 | 0941434435 | ELENA | NA | BOLAND | NA | Hospital | 331302 |
11 | 1063824894 | 2668690512 | JASMINE | N | TUCKER | NA | Hospital | 331302 |
12 | 1063851673 | 1557630613 | TERRENCE | NA | O CONNOR | NA | Hospital | 331302 |
13 | 1073099172 | 0547519381 | BROOKE | A | MAGGY | NA | Hospital | 331302 |
14 | 1073133484 | 7214328921 | CASEY | M | O'BRIEN | NA | Hospital | 331302 |
15 | 1073585055 | 0749217313 | TODD | J | WHITMAN | NA | Hospital | 331302 |
16 | 1073660684 | 1759418007 | GAYLEN | M | BIGELOW | NA | Hospital | 331302 |
17 | 1083052252 | 9931491792 | AHMED | A | HARHASH | NA | Hospital | 331302 |
18 | 1083148837 | 0042564882 | NEIL | A | KRULEWITZ | NA | Hospital | 331302 |
19 | 1083771927 | 7012087018 | JOSEPH | C | MIHINDUKULASURYIA | NA | Hospital | 331302 |
20 | 1093796898 | 5597848077 | ANDREW | C | MAHONEY | NA | Hospital | 331302 |
21..174 | ||||||||
175 | 1992261887 | 4486981958 | SERGIO | NA | APUZZO | NA | Hospital | 331302 |
That returns individual providers affiliated with the hospital. Now
to search the alphanumeric CCN (33Z302
):
affiliations(facility_ccn = "33Z302") |>
gt_preview(top_n = 10) |>
opt_table_font(font = google_font(name = "JetBrains Mono"))
npi | pac | first | middle | last | facility_type | facility_ccn | parent_ccn | |
---|---|---|---|---|---|---|---|---|
1 | 1396989059 | 8921259557 | MARY | K | HALLORAN | Nursing home | 33Z302 | 331302 |
2 | 1538173869 | 0547299091 | IL | JUN | CHON | Nursing home | 33Z302 | 331302 |
3 | 1801893318 | 3577568724 | ROB | L | DEMURO | Nursing home | 33Z302 | 331302 |
That returns more affiliated individual providers that practice in the Hospital’s nursing home..
An alphanumeric CCN represents a sub-unit of the hospital, here a nursing home. We would get the same result if we’d set the
parent_ccn
argument to the numeric CCN, i.e.affiliations(parent_ccn = 331302)