Open Payments
ex <- open_payments(year = 2021, npi = 1043218118, na.rm = TRUE)
ex
#> # A tibble: 109 × 47
#> program_year npi covered_recipient first last address city state zip
#> <int> <chr> <fct> <chr> <chr> <chr> <chr> <ord> <chr>
#> 1 2021 1043218… Physician Ahad Maho… 6739 G… Zeph… FL 33542
#> 2 2021 1043218… Physician AHAD MAHO… 6739 G… ZEPH… FL 33542
#> 3 2021 1043218… Physician AHAD MAHO… 6739 G… ZEPH… FL 33542
#> 4 2021 1043218… Physician AHAD MAHO… 6739 G… ZEPH… FL 33542
#> 5 2021 1043218… Physician AHAD MAHO… 6739 G… ZEPH… FL 33542
#> 6 2021 1043218… Physician AHAD MAHO… 6739 G… ZEPH… FL 33542
#> 7 2021 1043218… Physician AHAD MAHO… 6739 G… ZEPH… FL 33542
#> 8 2021 1043218… Physician AHAD MAHO… 6739 G… ZEPH… FL 33542
#> 9 2021 1043218… Physician AHAD MAHO… 6739 G… ZEPH… FL 33542
#> 10 2021 1043218… Physician AHAD MAHO… 6739 G… ZEPH… FL 33542
#> # ℹ 99 more rows
#> # ℹ 38 more variables: country <chr>, primary <chr>, specialty <chr>,
#> # license_state <ord>, physician_ownership <lgl>, third_party_payment <chr>,
#> # charity <lgl>, publish_date <date>, publish_delay <lgl>,
#> # publish_dispute <lgl>, related_product <lgl>, payer_id <chr>,
#> # payer_sub <chr>, payer_name <chr>, payer_state <ord>, payer_country <chr>,
#> # pay_form <chr>, pay_nature <chr>, pay_total <dbl>, pay_date <date>, …
payment <- ex |>
select(payer_name,
pay_form,
pay_nature,
row_id,
group_id,
pay_total,
pay_date,
name:category,
ndc.atc_second)
payment
#> # A tibble: 109 × 12
#> payer_name pay_form pay_nature row_id group_id pay_total pay_date name
#> <chr> <chr> <chr> <int> <int> <dbl> <date> <chr>
#> 1 Mobius Therap… Cash or… Compensat… 1 1 2500 2021-05-26 Mito…
#> 2 Johnson & Joh… In-kind… Food and … 2 1 69.9 2021-03-04 Tecn…
#> 3 Horizon Thera… In-kind… Food and … 3 1 108. 2021-08-24 TEPE…
#> 4 Horizon Thera… In-kind… Food and … 4 1 97.1 2021-08-24 TEPE…
#> 5 Horizon Thera… In-kind… Food and … 5 1 23.9 2021-09-30 TEPE…
#> 6 Horizon Thera… In-kind… Food and … 6 1 25.6 2021-11-10 TEPE…
#> 7 EyePoint Phar… In-kind… Food and … 7 1 13.8 2021-12-08 YUTIQ
#> 8 EyePoint Phar… In-kind… Food and … 8 1 16.6 2021-04-15 DEXY…
#> 9 Alcon Vision … In-kind… Food and … 9 1 114. 2021-05-06 Prec…
#> 10 Alcon Vision … In-kind… Food and … 10 1 14.7 2021-10-06 Acry…
#> # ℹ 99 more rows
#> # ℹ 4 more variables: covered <lgl>, type <chr>, category <chr>,
#> # ndc.atc_second <chr>
|
payments |
total |
avg_per_pmt |
| 1 |
92 |
$22,678.50 |
$246.51 |
|
payer_name |
payments |
total |
avg_per_pmt |
| 1 |
Allergan, Inc. |
33 |
$12,454.59 |
$377.41 |
| 2 |
Bausch & Lomb, a division of Bausch Health US, LLC |
2 |
$3,116.93 |
$1,558.46 |
| 3 |
Mobius Therapeutics, LLC |
1 |
$2,500.00 |
$2,500.00 |
| 4 |
Ivantis, Inc |
7 |
$1,804.85 |
$257.84 |
| 5 |
Iridex Corporation |
3 |
$1,034.87 |
$344.96 |
| 6 |
Horizon Therapeutics plc |
4 |
$254.57 |
$63.64 |
| 7 |
Sight Sciences, Inc. |
5 |
$250.27 |
$50.05 |
| 8 |
SUN PHARMACEUTICAL INDUSTRIES INC. |
5 |
$175.53 |
$35.11 |
| 9 |
Mallinckrodt Hospital Products Inc. |
4 |
$156.91 |
$39.23 |
| 10 |
NEW WORLD MEDICAL,INC. |
2 |
$156.86 |
$78.43 |
| 11 |
Kala Pharmaceuticals, Inc. |
7 |
$138.46 |
$19.78 |
| 12 |
Alcon Vision LLC |
2 |
$128.93 |
$64.47 |
| 13 |
Regeneron Healthcare Solutions, Inc. |
1 |
$125.01 |
$125.01 |
| 14 |
Alimera Sciences, Inc. |
5 |
$89.25 |
$17.85 |
| 15 |
Johnson & Johnson Surgical Vision, Inc. |
1 |
$69.90 |
$69.90 |
| 16 |
Ocular Therapeutix, Inc. |
2 |
$69.27 |
$34.63 |
| 17 |
Novartis Pharmaceuticals Corporation |
3 |
$61.57 |
$20.52 |
| 18 |
EyePoint Pharmaceuticals US, Inc. |
2 |
$30.49 |
$15.25 |
| 19 |
Beaver-Visitec International, Inc. |
1 |
$25.02 |
$25.02 |
| 20 |
Dompe US, Inc. |
1 |
$20.48 |
$20.48 |
| 21 |
Checkpoint Surgical, Inc |
1 |
$14.74 |
$14.74 |
payment |>
select(group_id,
pay_total,
name,
type,
category) |>
group_by(type, category, name) |>
summarise(payments = n(),
total = sum(pay_total, na.rm = TRUE),
avg_per_pmt = total / payments) |>
arrange(desc(total)) |>
gt::gt_preview(40) |>
gt::fmt_currency(columns = c(total, avg_per_pmt)) |>
gt::opt_all_caps()
|
type |
category |
name |
payments |
total |
avg_per_pmt |
| 1 |
Device |
GLAUCOMA |
XEN GLAUCOMA TREATMENT SYSTEM |
24 |
$11,859.00 |
$494.12 |
| 2 |
Device |
Ophthalmology |
STELLARIS |
1 |
$3,000.00 |
$3,000.00 |
| 3 |
Drug |
Ophthamology |
Mitosol |
1 |
$2,500.00 |
$2,500.00 |
| 4 |
Device |
Ophthalmic Surgery |
Hydrus Microstent |
7 |
$1,804.85 |
$257.84 |
| 5 |
NA |
NA |
Blank |
4 |
$1,059.89 |
$264.97 |
| 6 |
Drug |
GLAUCOMA |
DURYSTA |
8 |
$522.35 |
$65.29 |
| 7 |
Drug |
TEPEZZA |
TEPEZZA |
4 |
$254.57 |
$63.64 |
| 8 |
Drug |
Ophthalmology |
Cequa |
5 |
$175.53 |
$35.11 |
| 9 |
Biological |
IMMUNOLOGY |
ACTHAR |
4 |
$156.91 |
$39.23 |
| 10 |
Device |
Ophthalmology |
Ahmed Glaucoma Valve |
2 |
$156.86 |
$78.43 |
| 11 |
Device |
Ophthalmology |
OMNI(R) SURGICAL SYSTEM (US) |
4 |
$153.97 |
$38.49 |
| 12 |
Drug |
Ocular |
INVELTYS |
7 |
$138.46 |
$19.78 |
| 13 |
Biological |
OPHTHALMOLOGY |
EYLEA |
1 |
$125.01 |
$125.01 |
| 14 |
Device |
Ophthalmology |
CRYSTALENS |
1 |
$116.93 |
$116.93 |
| 15 |
Device |
Ophthalmology |
Precision 1 |
1 |
$114.20 |
$114.20 |
| 16 |
Device |
Ophthalmology |
TearCare SmartLid |
1 |
$96.30 |
$96.30 |
| 17 |
Drug |
OPHTHALMOLOGY |
ILUVIEN |
5 |
$89.25 |
$17.85 |
| 18 |
NA |
NA |
NA |
1 |
$73.24 |
$73.24 |
| 19 |
Device |
Optics |
Tecnis IOL |
1 |
$69.90 |
$69.90 |
| 20 |
Drug |
Corticosteroid intracanalicular insert |
DEXTENZA |
2 |
$69.27 |
$34.63 |
| 21 |
Drug |
OPHTHALMOLOGY |
BEOVU |
3 |
$61.57 |
$20.52 |
| 22 |
Drug |
SOLUTION/ DROPS |
OXERVATE |
1 |
$20.48 |
$20.48 |
| 23 |
Drug |
Postoperative Inflammation |
DEXYCU |
1 |
$16.64 |
$16.64 |
| 24 |
Device |
Surgery |
Checkpoint Stimulators |
1 |
$14.74 |
$14.74 |
| 25 |
Device |
Ophthalmology |
AcrySof IQ VIVITY IOL |
1 |
$14.73 |
$14.73 |
| 26 |
Drug |
Chronic Non-infectious Posterior Segment Uveitis |
YUTIQ |
1 |
$13.85 |
$13.85 |
| 27 |
Device |
Ophthalmology |
Crystalens Accommodating IOL (AT-52AO) |
1 |
$0.00 |
$0.00 |
| 28 |
Device |
Ophthalmology |
Kahook Dual Blade |
2 |
$0.00 |
$0.00 |
| 29 |
Device |
Ophthalmology |
TEARCARE(TM) (2.2) SYSTEM KIT |
1 |
$0.00 |
$0.00 |
| 30 |
Device |
Ophthalmology |
Trulign IOL |
1 |
$0.00 |
$0.00 |
| 31 |
Drug |
Ocular |
EYSUVIS |
7 |
$0.00 |
$0.00 |
| 32 |
Drug |
Ophthalmology |
BromSite |
3 |
$0.00 |
$0.00 |
| 33 |
Drug |
Ophthalmology |
Xelpros |
2 |
$0.00 |
$0.00 |
payment |>
filter(row_id == "72")
#> # A tibble: 3 × 12
#> payer_name pay_form pay_nature row_id group_id pay_total pay_date name
#> <chr> <chr> <chr> <int> <int> <dbl> <date> <chr>
#> 1 Bausch & Lomb,… Cash or… Consultin… 72 1 3000 2021-01-04 STEL…
#> 2 Bausch & Lomb,… Cash or… Consultin… 72 2 NA 2021-01-04 Crys…
#> 3 Bausch & Lomb,… Cash or… Consultin… 72 3 NA 2021-01-04 Trul…
#> # ℹ 4 more variables: covered <lgl>, type <chr>, category <chr>,
#> # ndc.atc_second <chr>
Fuzzy Joins
op <- ex |>
filter(type == "Drug") |>
select(year = program_year,
pay_total,
group_id,
brand_name = ndc.brand_name,
generic_name = ndc.drug_name) |>
mutate(brand_name = stringr::str_to_title(brand_name),
generic_name = stringr::str_to_title(generic_name))
op
#> # A tibble: 50 × 5
#> year pay_total group_id brand_name generic_name
#> <int> <dbl> <int> <chr> <chr>
#> 1 2021 2500 1 Mitosol Mitomycin 0.2 Mg/Ml Ophthalmic Solution …
#> 2 2021 108. 1 Tepezza Teprotumumab-Trbw 500 Mg Injection [Tepe…
#> 3 2021 97.1 1 Tepezza Teprotumumab-Trbw 500 Mg Injection [Tepe…
#> 4 2021 23.9 1 Tepezza Teprotumumab-Trbw 500 Mg Injection [Tepe…
#> 5 2021 25.6 1 Tepezza Teprotumumab-Trbw 500 Mg Injection [Tepe…
#> 6 2021 13.8 1 Yutiq Fluocinolone Acetonide 0.18 Mg Drug Impl…
#> 7 2021 16.6 1 Dexycu 0.005 Ml Dexamethasone 103.4 Mg/Ml Injec…
#> 8 2021 325 1 Durysta Bimatoprost 0.01 Mg Drug Implant [Duryst…
#> 9 2021 18.6 1 Durysta Bimatoprost 0.01 Mg Drug Implant [Duryst…
#> 10 2021 4.05 1 Durysta Bimatoprost 0.01 Mg Drug Implant [Duryst…
#> # ℹ 40 more rows
rx <- prescribers(year = 2021,
type = 'Drug',
npi = 1043218118) |>
select(year,
brand_name,
generic_name,
tot_claims,
tot_fills,
tot_supply,
tot_cost,
tot_benes)
rx
#> # A tibble: 27 × 8
#> year brand_name generic_name tot_claims tot_fills tot_supply tot_cost
#> <int> <chr> <chr> <int> <dbl> <int> <dbl>
#> 1 2021 Acetazolamide Er Acetazolami… 20 20.5 533 971.
#> 2 2021 Alphagan P Brimonidine… 34 51 1424 12450.
#> 3 2021 Azithromycin Azithromycin 661 1588. 45870 7181.
#> 4 2021 Brimonidine Tart… Brimonidine… 306 550. 15746 8700.
#> 5 2021 Bromsite Bromfenac S… 78 80.5 2387 21943.
#> 6 2021 Carteolol Hcl Carteolol H… 79 184. 5482 1578.
#> 7 2021 Cephalexin Cephalexin 57 57 279 444.
#> 8 2021 Combigan Brimonidine… 63 87.8 2444 17835.
#> 9 2021 Dorzolamide Hcl Dorzolamide… 71 151. 4533 2189
#> 10 2021 Dorzolamide-Timo… Dorzolamide… 99 216. 6463 3921.
#> # ℹ 17 more rows
#> # ℹ 1 more variable: tot_benes <int>
More Involved Example
# plan(multisession, workers = 4)
# open <- open_payments_(npi = 1043218118)
# plan(sequential)
# open <- open |> janitor::remove_empty(which = c('rows', 'cols'))
# open |>
# select(program_year,
# payer_name,
# pay_form,
# pay_nature,
# pay_total,
# pay_date,
# pay_count,
# row_id,
# group_id,
# name,
# covered,
# type,
# category,
# ndc,
# pdi)
# open |>
# count(payer_name,
# wt = pay_count,
# sort = TRUE)
# open |>
# count(payer_id,
# wt = pay_count,
# sort = TRUE)
# open |>
# filter(!is.na(type)) |>
# count(type, sort = TRUE)
