NPIs, PACs, ENIDs, CCNs, CLIAs and Many More
Individual Provider
pac <- affiliations(pac = 7810891009)#> ✔ Query returned 5 results.
list(
individual = as.data.frame(t(unique(pac[1:6]))),
organization = pac[7:9])#> $individual
#> V1
#> first MARK
#> last FUNG
#> middle K
#> suffix <NA>
#> npi 1043245657
#> pac 7810891009
#>
#> $organization
#> # A tibble: 5 × 3
#> facility_type facility_ccn parent_ccn
#> <chr> <chr> <chr>
#> 1 Hospital 470003 NA
#> 2 Hospital 330250 NA
#> 3 Hospital 331321 NA
#> 4 Hospital 470001 NA
#> 5 Hospital 471307 NA
ccn <- hospitals(ccn = pac$facility_ccn)#> ✔ Query returned 5 results.
ccn |> str()#> tibble [5 × 39] (S3: tbl_df/tbl/data.frame)
#> $ org_name : chr [1:5] "CHAMPLAIN VALLEY PHYSICIANS HOSPITAL MEDICAL CENTER" "ALICE HYDE MEDICAL CENTER" "CENTRAL VERMONT MEDICAL CENTER INC" "UNIVERSITY OF VERMONT MEDICAL CENTER INC" ...
#> $ dba_name : chr [1:5] "THE UNIVERSITY OF VT HEALTH NETWORK - CHAMPLAIN VALLEY PHYSICIANS HOSP" "THE UNIVERSITY OF VERMONT HEALTH NETWORK-ALICE HYDE MEDICAL CENTER" NA "UNIVERSITY OF VERMONT MEDICAL CENTER" ...
#> $ enid : chr [1:5] "O20120110000201" "O20230512000344" "O20050809000650" "O20021111000009" ...
#> $ enid_state : chr [1:5] "NY" "NY" "VT" "VT" ...
#> $ spec : chr [1:5] "00-09" "00-85" "00-09" "00-09" ...
#> $ specialty : chr [1:5] "PART A PROVIDER - HOSPITAL" "PART A PROVIDER - CRITICAL ACCESS HOSPITAL" "PART A PROVIDER - HOSPITAL" "PART A PROVIDER - HOSPITAL" ...
#> $ npi : chr [1:5] "1033270699" "1114954682" "1508845637" "1568419976" ...
#> $ multi : chr [1:5] "N" "N" "N" "N" ...
#> $ ccn : chr [1:5] "330250" "331321" "470001" "470003" ...
#> $ ccn_2 : chr [1:5] NA NA NA NA ...
#> $ pac : chr [1:5] "2769396878" "4082525837" "9335138817" "3779491071" ...
#> $ inc_date : chr [1:5] "1926-01-01" "1905-04-13" "1984-03-01" "1995-01-01" ...
#> $ inc_state : chr [1:5] "NY" "NY" "VT" "VT" ...
#> $ org_type : chr [1:5] "CORPORATION" "CORPORATION" "CORPORATION" "CORPORATION" ...
#> $ org_text : chr [1:5] NA NA NA NA ...
#> $ designation : chr [1:5] "N" "N" "N" "N" ...
#> $ add_1 : chr [1:5] "75 BEEKMAN ST" "133 PARK ST" "130 FISHER RD" "111 COLCHESTER AVE" ...
#> $ add_2 : chr [1:5] NA NA NA NA ...
#> $ city : chr [1:5] "PLATTSBURGH" "MALONE" "BERLIN" "BURLINGTON" ...
#> $ state : chr [1:5] "NY" "NY" "VT" "VT" ...
#> $ zip : chr [1:5] "129011438" "129531244" "56029516" "54011473" ...
#> $ location_type : chr [1:5] "MAIN/PRIMARY HOSPITAL LOCATION" "MAIN/PRIMARY HOSPITAL LOCATION" "MAIN/PRIMARY HOSPITAL LOCATION" "OTHER HOSPITAL PRACTICE LOCATION" ...
#> $ location_text : chr [1:5] NA NA NA "HOSPITAL - GENERAL PRACTICE AND CLINICS" ...
#> $ reh_ind : chr [1:5] "N" "N" "N" "N" ...
#> $ reh_date : chr [1:5] NA NA NA NA ...
#> $ sub_general : chr [1:5] "N" "N" "N" "N" ...
#> $ sub_acute : chr [1:5] "Y" "N" "Y" "Y" ...
#> $ sub_drug : chr [1:5] "N" "N" "N" "N" ...
#> $ sub_child : chr [1:5] "N" "N" "N" "N" ...
#> $ sub_long : chr [1:5] "N" "N" "N" "N" ...
#> $ sub_psych : chr [1:5] "N" "N" "N" "N" ...
#> $ sub_rehab : chr [1:5] "N" "N" "N" "N" ...
#> $ sub_short : chr [1:5] "N" "N" "N" "N" ...
#> $ sub_swing : chr [1:5] "N" "N" "N" "N" ...
#> $ sub_psych_unit: chr [1:5] "N" "N" "N" "N" ...
#> $ sub_rehab_unit: chr [1:5] "N" "N" "N" "N" ...
#> $ sub_specialty : chr [1:5] "N" "N" "N" "N" ...
#> $ sub_other : chr [1:5] "N" "N" "N" "Y" ...
#> $ sub_otext : chr [1:5] NA NA NA "ORGAN TRANSPLANT PROGRAM" ...Organizational Provider
Exploring links between providers can lead to many interesting insights. For example, there is a hospital in New York named Elizabethtown Community Hospital.
providers(org_name = "Elizabethtown Community Hospital")#> ✔ Query returned 9 results.#> # A tibble: 9 × 11
#> first middle last org_name state spec specialty npi multi pac enid
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 NA NA NA ELIZABETHTOW… NY 12-70 PART B S… 1053… Y 3577… O200…
#> 2 NA NA NA ELIZABETHTOW… NY 00-85 PART A P… 1891… Y 3577… O201…
#> 3 NA NA NA ELIZABETHTOW… NY 12-59 PART B S… 1487… N 3577… O201…
#> 4 NA NA NA ELIZABETHTOW… NY 00-85 PART A P… 1407… N 3577… O202…
#> 5 NA NA NA ELIZABETHTOW… CT 12-70 PART B S… 1053… N 3577… O202…
#> 6 NA NA NA ELIZABETHTOW… FL 12-70 PART B S… 1053… N 3577… O202…
#> 7 NA NA NA ELIZABETHTOW… VT 12-70 PART B S… 1053… N 3577… O202…
#> 8 NA NA NA ELIZABETHTOW… NH 12-70 PART B S… 1053… N 3577… O202…
#> 9 NA NA NA ELIZABETHTOW… CO 12-70 PART B S… 1053… N 3577… O202…
hospitals(org_name = "Elizabethtown Community Hospital")#> ✔ Query returned 2 results.#> # A tibble: 2 × 39
#> org_name dba_name enid enid_state spec specialty npi multi ccn ccn_2
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ELIZABETHTO… NA O201… NY 00-85 PART A P… 1891… Y 3313… NA
#> 2 ELIZABETHTO… NA O202… NY 00-85 PART A P… 1407… N 33Z3… NA
#> # ℹ 29 more variables: pac <chr>, inc_date <chr>, inc_state <chr>,
#> # org_type <chr>, org_text <chr>, designation <chr>, add_1 <chr>,
#> # add_2 <chr>, city <chr>, state <chr>, zip <chr>, location_type <chr>,
#> # location_text <chr>, reh_ind <chr>, reh_date <chr>, sub_general <chr>,
#> # sub_acute <chr>, sub_drug <chr>, sub_child <chr>, sub_long <chr>,
#> # sub_psych <chr>, sub_rehab <chr>, sub_short <chr>, sub_swing <chr>,
#> # sub_psych_unit <chr>, sub_rehab_unit <chr>, sub_specialty <chr>, …
clinicians(facility_name = "Elizabethtown Community Hospital")#> ✔ Query returned 60 results.#> # A tibble: 60 × 25
#> first middle last suffix gender cred school year specialty spec_other
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 JEFFREY NA KLOTZ NA M MD OTHER 2022 FAMILY P… NA
#> 2 RYAN NA WALSH NA M MD OTHER 2007 DIAGNOST… NA
#> 3 RYAN NA WALSH NA M MD OTHER 2007 DIAGNOST… NA
#> 4 RUSSELL NA MEYER NA M MD UNIVE… 2009 DIAGNOST… NA
#> 5 RUSSELL NA MEYER NA M MD UNIVE… 2009 DIAGNOST… NA
#> 6 JAMES NA KENNEY NA M MD UNIVE… 1992 DIAGNOST… NA
#> 7 BENJAMIN NA BAMFORD NA M MD UNIVE… 1992 DIAGNOST… NA
#> 8 BENJAMIN NA BAMFORD NA M MD UNIVE… 1992 DIAGNOST… NA
#> 9 STEVEN E DESO NA M MD BOSTO… 2010 DIAGNOST… INTERVENT…
#> 10 LAURENT NA GILLOT… NA M MD OTHER 1998 DIAGNOST… NA
#> # ℹ 50 more rows
#> # ℹ 15 more variables: facility_name <chr>, npi <chr>, pac <chr>, enid <chr>,
#> # org_pac <chr>, org_mems <chr>, add_1 <chr>, add_2 <chr>, city <chr>,
#> # state <chr>, zip <chr>, phone <chr>, ind <chr>, grp <chr>, tele <chr>
reassignments(org_name = "Elizabethtown Community Hospital")#> ✔ Query returned 356 results.#> # A tibble: 356 × 14
#> first last state specialty ind_assoc npi pac enid org_name org_assign
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Nathal… Abis… NY Family P… 1 1336… 5294… I202… Elizabe… 186
#> 2 Jose Acos… NY Medical … 5 1013… 5890… I201… Elizabe… 186
#> 3 Jose Acos… NY Medical … 5 1013… 5890… I201… Elizabe… 114
#> 4 Aderon… Aden… NY Cardiova… 3 1407… 8820… I201… Elizabe… 186
#> 5 Dmitriy Akse… NY Diagnost… 4 1629… 4385… I202… Elizabe… 186
#> 6 Dmitriy Akse… VT Diagnost… 6 1629… 4385… I201… Elizabe… 33
#> 7 Vlada Alex… NY Pathology 3 1952… 5092… I201… Elizabe… 186
#> 8 Anel Alex… NY Emergenc… 6 1720… 3678… I201… Elizabe… 186
#> 9 Anel Alex… NY Emergenc… 6 1720… 3678… I201… Elizabe… 114
#> 10 Loren Allen NY Nurse Pr… 2 1770… 1658… I201… Elizabe… 186
#> # ℹ 346 more rows
#> # ℹ 4 more variables: org_pac <chr>, org_enid <chr>, org_state <chr>,
#> # type <chr>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):
ccn <- affiliations(facility_ccn = 331302)#> ✔ Query returned 206 results.
list(
organization = as.data.frame(t(unique(ccn[7:9]))),
individual = unique(ccn[1:6])
)#> $organization
#> V1
#> facility_type Hospital
#> facility_ccn 331302
#> parent_ccn <NA>
#>
#> $individual
#> # A tibble: 206 × 6
#> first last middle suffix npi pac
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 LAURA GREENE A NA 1003845272 1759384035
#> 2 DEBORAH KAMPSCHROR M NA 1013141860 8022069558
#> 3 NAROD VASSILIAN NA NA 1013539584 9133544109
#> 4 EMILY TRIPLETT NA NA 1013595560 3375947401
#> 5 JOSE ACOSTAMADIEDO M NA 1013910256 5890719371
#> 6 LINDSEY WILHELM B NA 1023377843 6901115278
#> 7 VANESSA FIORINI FURTADO NA NA 1043672140 7214229350
#> 8 JOHN YOUNG NA NA 1063420891 9436051687
#> 9 DELANEY OSBORN NA NA 1073073177 0547593097
#> 10 ANGAD GILL NA NA 1073133435 6800318452
#> # ℹ 196 more rowsThat returns individual providers affiliated with the hospital. Now
to search the alphanumeric CCN (33Z302):
ccn2 <- affiliations(facility_ccn = "33Z302")#> ✔ Query returned 4 results.
list(
organization = as.data.frame(t(unique(ccn2[7:9]))),
individual = unique(ccn2[1:6])
)#> $organization
#> V1
#> facility_type Nursing home
#> facility_ccn 33Z302
#> parent_ccn 331302
#>
#> $individual
#> # A tibble: 4 × 6
#> first last middle suffix npi pac
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 JEFFREY KLOTZ NA NA 1073258398 3870095805
#> 2 MARY HALLORAN K NA 1396989059 8921259557
#> 3 IL CHON JUN NA 1538173869 0547299091
#> 4 DRAGOS BANU NA NA 1558659367 6709004682That 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_ccnargument to the numeric CCN, i.e.affiliations(parent_ccn = 331302)