Appendectomy
Introduction
In this section we will: * identify which Appendectomy procedures were excluded and why * identify which different Appendectomy subgroups were created and why
If you have questions or concerns about this data please contact Alexander Nielson (alexnielson@utah.gov)
Load Libraries
Load Libraries
library(data.table)
library(tidyverse)
## Warning: replacing previous import 'vctrs::data_frame' by 'tibble::data_frame'
## when loading 'dplyr'
library(stringi)
library(ggridges)
library(broom)
library(disk.frame)
library(RecordLinkage)
library(googlesheets4)
library(bigrquery)
library(DBI)
devtools::install_github("utah-osa/hcctools2", upgrade="never" )
library(hcctools2)
Establish color palettes
cust_color_pal1 <- c(
"Anesthesia" = "#f3476f",
"Facility" = "#e86a33",
"Medicine Proc" = "#e0a426",
"Pathology" = "#77bf45",
"Radiology" = "#617ff7",
"Surgery" = "#a974ee"
)
cust_color_pal2 <- c(
"TRUE" = "#617ff7",
"FALSE" = "#e0a426"
)
cust_color_pal3 <- c(
"above avg" = "#f3476f",
"avg" = "#77bf45",
"below avg" = "#a974ee"
)
fac_ref_regex <- "(UTAH)|(IHC)|(HOSP)|(HOSPITAL)|(CLINIC)|(ANESTH)|(SCOPY)|(SURG)|(LLC)|(ASSOC)|(MEDIC)|(CENTER)|(ASSOCIATES)|(U OF U)|(HEALTH)|(OLOGY)|(OSCOPY)|(FAMILY)|(VAMC)|(SLC)|(SALT LAKE)|(CITY)|(PROVO)|(OGDEN)|(ENDO )|( VALLEY)|( REGIONAL)|( CTR)|(GRANITE)|( INSTITUTE)|(INSTACARE)|(WASATCH)|(COUNTY)|(PEDIATRIC)|(CORP)|(CENTRAL)|(URGENT)|(CARE)|(UNIV)|(ODYSSEY)|(MOUNTAINSTAR)|( ORTHOPEDIC)|(INSTITUT)|(PARTNERSHIP)|(PHYSICIAN)|(CASTLEVIEW)|(CONSULTING)|(MAGEMENT)|(PRACTICE)|(EMERGENCY)|(SPECIALISTS)|(DIVISION)|(GUT WHISPERER)|(INTERMOUNTAIN)|(OBGYN)"
Connect to GCP database
bigrquery::bq_auth(path = 'D:/gcp_keys/healthcare-compare-prod-95b3b7349c32.json')
# set my project ID and dataset name
project_id <- 'healthcare-compare-prod'
dataset_name <- 'healthcare_compare'
con <- dbConnect(
bigrquery::bigquery(),
project = project_id,
dataset = dataset_name,
billing = project_id
)
Get NPI table
query <- paste0("SELECT npi, clean_name, osa_group, osa_class, osa_specialization
FROM `healthcare-compare-prod.healthcare_compare.npi_full`")
#bq_project_query(billing, query) # uncomment to determine billing price for above query.
npi_full <- dbGetQuery(con, query) %>%
data.table()
get a subset of the NPI providers based upon taxonomy groups
gs4_auth(email="alexnielson@utah.gov")
surgery <- read_sheet("https://docs.google.com/spreadsheets/d/1GY8lKwUJuPHtpUl4EOw9eriLUDG9KkNWrMbaSnA5hOU/edit#gid=0",
sheet="major_surgery") %>% as.data.table
## Reading from "Doctor Types to Keep"
## Range "'major_surgery'"
surgery <- surgery[is.na(Remove) ] %>% .[["NUCC Classification"]]
npi_prov_pair <- npi_full[osa_class %in% surgery] %>%
.[,.(npi=npi,
clean_name = clean_name
)
]
Load Data
bun_proc <- disk.frame("full_apcd.df")
apendx <- bun_proc[surg_bun_t_appendectomy==T]
apendx <- apendx[,`:=`(
surg_sp_name_clean = surg_sp_npi %>% map_chr(get_npi_standard_name),
surg_bp_name_clean = surg_bp_npi %>% map_chr(get_npi_standard_name),
medi_sp_name_clean = medi_sp_npi %>% map_chr(get_npi_standard_name),
medi_bp_name_clean = medi_bp_npi %>% map_chr(get_npi_standard_name),
radi_sp_name_clean = radi_sp_npi %>% map_chr(get_npi_standard_name),
radi_bp_name_clean = radi_bp_npi %>% map_chr(get_npi_standard_name),
path_sp_name_clean = path_sp_npi %>% map_chr(get_npi_standard_name),
path_bp_name_clean = path_bp_npi %>% map_chr(get_npi_standard_name),
anes_sp_name_clean = anes_sp_npi %>% map_chr(get_npi_standard_name),
anes_bp_name_clean = anes_bp_npi %>% map_chr(get_npi_standard_name),
faci_sp_name_clean = faci_sp_npi %>% map_chr(get_npi_standard_name),
faci_bp_name_clean = faci_bp_npi %>% map_chr(get_npi_standard_name)
)]
apendx %>% saveRDS("apendx.RDS")
apendx <- readRDS("apendx.RDS")
examine appendectomy median price distribution and get the highly correlated tags
apendx %>% plot_med_density() %>% print()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
apendx %>% get_tag_cor() %>% print()
## Warning in stats::cor(cor_data): the standard deviation is zero
## # A tibble: 304 x 2
## name correlation
## <chr> <dbl>
## 1 surg_bun_t_lung 0.686
## 2 surg_bun_t_percut 0.686
## 3 medi_bun_t_establish 0.686
## 4 medi_bun_t_eye_exam 0.686
## 5 radi_bun_t_ct_scan 0.686
## 6 radi_bun_t_head 0.686
## 7 radi_bun_t_scan 0.686
## 8 radi_bun_t_brain 0.664
## 9 duration_max 0.656
## 10 surg_bun_t_pancreas 0.587
## # ... with 294 more rows
apendx[surg_bun_t_lung==T] %>% nrow()
## [1] 1
apendx[radi_bun_t_brain==T] %>% nrow()
## [1] 2
apendx[cnt > 4 & faci_bun_sum_med > 1000 & surg_bun_t_lung==F & radi_bun_t_brain==F] %>% nrow()
## [1] 826
apendx[cnt > 4 & faci_bun_sum_med > 1000 & surg_bun_t_lung==F & radi_bun_t_brain==F] %>% plot_med_density() %>% print()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
apendx[cnt > 4 & faci_bun_sum_med > 1000 & surg_bun_t_lung==F & radi_bun_t_brain==F] %>% get_tag_cor() %>% print()
## Warning in stats::cor(cor_data): the standard deviation is zero
## # A tibble: 281 x 2
## name correlation
## <chr> <dbl>
## 1 duration_mean 0.615
## 2 duration_max 0.594
## 3 medi_bun_t_sodium 0.402
## 4 medi_bun_t_inject 0.393
## 5 surg_bun_t_gallbladder 0.324
## 6 surg_bun_t_removal 0.321
## 7 faci_bun_t_hospital 0.318
## 8 medi_bun_t_cathet 0.308
## 9 surg_bun_t_bladder 0.288
## 10 surg_bun_t_lap 0.280
## # ... with 271 more rows
apendx <- apendx[cnt > 4 & faci_bun_sum_med > 1000 & surg_bun_t_lung==F & radi_bun_t_brain==F]
apendx %>% get_tag_density_information("medi_bun_t_sodium") %>% print()
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## [1] "tp_med ~ medi_bun_t_sodium"
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Picking joint bandwidth of 1430
## $dist_plots
##
## $stat_tables
apendx_w_sodium <- apendx[medi_bun_t_sodium == T & medi_bun_t_inject == T]
apendx_n_sodium <- apendx[medi_bun_t_sodium == F & medi_bun_t_inject == F]
appendectomy with sodium injection
-usually antibiotics or anesthesia
examine the median price density and the highly correlated tags
apendx_w_sodium %>% plot_med_density() %>% print()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
apendx_w_sodium %>% get_tag_cor() %>% print()
## Warning in stats::cor(cor_data): the standard deviation is zero
## # A tibble: 211 x 2
## name correlation
## <chr> <dbl>
## 1 medi_bun_t_cathet 0.381
## 2 medi_bun_t_flu 0.381
## 3 medi_bun_t_wound 0.381
## 4 path_bun_t_embryo 0.381
## 5 duration_max 0.368
## 6 surg_bun_t_removal 0.344
## 7 duration_mean 0.343
## 8 path_bun_t_anti 0.320
## 9 faci_bun_t_critical 0.314
## 10 path_bun_t_blood 0.273
## # ... with 201 more rows
apendx_w_sodium[medi_bun_t_flu==T] %>% nrow()
## [1] 1
apendx_w_sodium[surg_bun_t_removal==T] %>% nrow()
## [1] 2
apendx_w_sodium[faci_bun_t_critical==T] %>% nrow()
## [1] 2
apendx_w_sodium[path_bun_t_immuno==T] %>% nrow()
## [1] 9
apendx_w_sodium <- apendx_w_sodium[medi_bun_t_flu==F& surg_bun_t_removal==F & faci_bun_t_critical==F & path_bun_t_immuno==F ]
examine the median price density and the highly correlated tags
apendx_w_sodium %>% plot_med_density() %>% print()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
apendx_w_sodium %>% get_tag_cor() %>% print()
## Warning in stats::cor(cor_data): the standard deviation is zero
## # A tibble: 197 x 2
## name correlation
## <chr> <dbl>
## 1 faci_bun_t_dept 0.321
## 2 faci_bun_t_emergency 0.321
## 3 medi_bun_t_puncture 0.275
## 4 medi_bun_t_routine 0.275
## 5 medi_bun_t_venipuncture 0.275
## 6 duration_max 0.258
## 7 faci_bun_t_visit 0.235
## 8 medi_bun_t_observ 0.234
## 9 radi_bun_t_contrast 0.232
## 10 path_bun_t_comprehen 0.229
## # ... with 187 more rows
apendx_w_sodium %>% get_tag_density_information("faci_bun_t_emergency") %>% print()
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
## [1] "tp_med ~ faci_bun_t_emergency"
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Picking joint bandwidth of 969
## $dist_plots
##
## $stat_tables
at emergency department
apendx_w_sodium_at_emerg <- apendx_w_sodium[faci_bun_t_emergency==T]
examine the median price density and the highly correlated tags
apendx_w_sodium_at_emerg %>% plot_med_density() %>% print()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
apendx_w_sodium_at_emerg %>% get_tag_cor() %>% print()
## Warning in stats::cor(cor_data): the standard deviation is zero
## # A tibble: 140 x 2
## name correlation
## <chr> <dbl>
## 1 medi_bun_t_observ 0.321
## 2 medi_bun_t_puncture 0.299
## 3 medi_bun_t_routine 0.299
## 4 medi_bun_t_venipuncture 0.299
## 5 duration_max 0.274
## 6 duration_mean 0.228
## 7 path_bun_t_alkaline 0.226
## 8 path_bun_t_amino 0.226
## 9 path_bun_t_phosphatase 0.226
## 10 medi_bun_t_solution 0.203
## # ... with 130 more rows
apendx_w_sodium_at_emerg[path_bun_t_alkaline==T] %>% nrow()
## [1] 1
apendx_w_sodium_at_emerg[medi_bun_t_solution==T] %>% nrow()
## [1] 23
apendx_w_sodium_at_emerg[path_bun_t_typing==T] %>% nrow()
## [1] 6
apendx_w_sodium_at_emerg[surg_bun_t_bowel==T] %>% nrow()
## [1] 1
apendx_w_sodium_at_emerg[path_bun_t_antibod==T] %>% nrow()
## [1] 7
apendx_w_sodium_at_emerg[radi_bun_t_mri==T] %>% nrow()
## [1] 10
apendx_w_sodium_at_emerg[faci_bun_t_consultation==T] %>% nrow()
## [1] 2
apendx_w_sodium_at_emerg <- apendx_w_sodium_at_emerg[path_bun_t_alkaline==F & path_bun_t_typing==F & surg_bun_t_bowel==F & faci_bun_t_consultation==F ]
examine the median price density and the highly correlated tags
apendx_w_sodium_at_emerg %>% plot_med_density() %>% print()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
apendx_w_sodium_at_emerg %>% get_tag_cor() %>% print()
## Warning in stats::cor(cor_data): the standard deviation is zero
## # A tibble: 130 x 2
## name correlation
## <chr> <dbl>
## 1 duration_max 0.324
## 2 medi_bun_t_observ 0.288
## 3 duration_mean 0.273
## 4 medi_bun_t_puncture 0.258
## 5 medi_bun_t_routine 0.258
## 6 medi_bun_t_venipuncture 0.258
## 7 radi_bun_t_mri 0.207
## 8 radi_bun_t_without_dye 0.207
## 9 radi_bun_t_ct 0.199
## 10 radi_bun_t_contrast 0.193
## # ... with 120 more rows
apendx_w_sodium_at_emerg_btbv4 <- apendx_w_sodium_at_emerg %>% btbv4()
apendx_w_sodium_at_emerg_bq <- apendx_w_sodium_at_emerg_btbv4[,
primary_doctor := pmap(.l=list(doctor_npi1=doctor_npi_str1,
doctor_npi2=doctor_npi_str2,
class_reqs="Surgery"#,
# specialization_reqs = ""
),
.f=calculate_primary_doctor) %>% as.character()
] %>%
#Filter out any procedures where our doctors fail both criteria.
.[!(primary_doctor %in% c("BOTH_DOC_FAIL_CRIT", "TWO_FIT_ALL_SPECS", "NONE_FIT_SPEC_REQ"))] %>%
.[,primary_doctor_npi := fifelse(primary_doctor==doctor_str1,
doctor_npi_str1,
doctor_npi_str2)] %>%
.[,`:=`(procedure_type=7, procedure_modifier="At Emergency Department")]
## [1] "multiple meet class req"
## [1] "BRAD A MYERS" "DARRELL L WILSON"
## [1] "multiple meet class req"
## [1] "REBECKA L MEYERS" "ERIC RICHARD SCAIFE"
apendx_w_sodium_at_emerg_bq <- apendx_w_sodium_at_emerg_bq[,.(
primary_doctor,
primary_doctor_npi,
most_important_fac ,
most_important_fac_npi,
procedure_type,
procedure_modifier,
tp_med_med,
tp_med_surg,
tp_med_medi,
tp_med_path,
tp_med_radi,
tp_med_anes,
tp_med_faci,
ingest_date = Sys.Date()
)]
bq_table_upload(x=procedure_table, values= apendx_w_sodium_at_emerg_bq, create_disposition='CREATE_IF_NEEDED', write_disposition='WRITE_APPEND')
not at emergecny department
apendx_w_sodium_n_emerg <- apendx_w_sodium[faci_bun_t_emergency==F]
examine the median price density and the highly correlated tags
apendx_w_sodium_n_emerg %>% plot_med_density() %>% print()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
apendx_w_sodium_n_emerg %>% get_tag_cor() %>% print()
## Warning in stats::cor(cor_data): the standard deviation is zero
## # A tibble: 145 x 2
## name correlation
## <chr> <dbl>
## 1 path_bun_t_typing 0.373
## 2 path_bun_t_screen 0.343
## 3 path_bun_t_anti 0.322
## 4 path_bun_t_antibod 0.322
## 5 path_bun_t_blood 0.303
## 6 surg_bun_t_remov 0.288
## 7 surg_bun_t_remove 0.288
## 8 surg_bun_t_excise 0.264
## 9 surg_bun_t_lesion 0.264
## 10 surg_bun_t_exc 0.243
## # ... with 135 more rows
appendectomy no sodium injection
apendx_n_sodium %>% plot_med_density() %>% print()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
apendx_n_sodium %>% get_tag_cor() %>% print()
## Warning in stats::cor(cor_data): the standard deviation is zero
## # A tibble: 205 x 2
## name correlation
## <chr> <dbl>
## 1 duration_mean 0.591
## 2 duration_max 0.56
## 3 surg_bun_t_bladder 0.370
## 4 surg_bun_t_gallbladder 0.370
## 5 surg_bun_t_cysto 0.368
## 6 surg_bun_t_cystoscopy 0.368
## 7 surg_bun_t_treat 0.368
## 8 surg_bun_t_removal 0.322
## 9 surg_bun_t_cyst 0.316
## 10 surg_bun_t_add-on 0.312
## # ... with 195 more rows
apendx_n_sodium[surg_bun_t_bladder==T] %>% nrow()
## [1] 4
apendx_n_sodium[surg_bun_t_gallbladder==T] %>% nrow()
## [1] 4
apendx_n_sodium[surg_bun_t_cystoscopy==T] %>% nrow()
## [1] 2
apendx_n_sodium[surg_bun_t_bowel==T] %>% nrow()
## [1] 3
apendx_n_sodium[surg_bun_t_removal==T] %>% nrow()
## [1] 8
apendx_n_sodium[surg_bun_t_cyst==T] %>% nrow()
## [1] 6
apendx_n_sodium[`surg_bun_t_add-on`==T] %>% nrow()
## [1] 13
apendx_n_sodium[surg_bun_t_pancreas==T] %>% nrow()
## [1] 1
apendx_n_sodium[surg_bun_t_renal==T] %>% nrow()
## [1] 1
apendx_n_sodium[surg_bun_t_pancreas==T] %>% nrow()
## [1] 1
apendx_n_sodium[surg_bun_t_lap==F] %>% nrow()
## [1] 19
apendx_n_sodium[surg_bun_t_fluid==T] %>% nrow()
## [1] 2
apendx_n_sodium[surg_bun_t_imag==T] %>% nrow()
## [1] 1
apendx_n_sodium[surg_bun_t_colon==T] %>% nrow()
## [1] 1
apendx_n_sodium[surg_bun_t_partial==T] %>% nrow()
## [1] 1
apendx_n_sodium[surg_bun_t_ovarian==T] %>% nrow()
## [1] 2
apendx_n_sodium[surg_bun_t_implant==T] %>% nrow()
## [1] 2
apendx_n_sodium <- apendx_n_sodium[surg_bun_t_bladder==F &
surg_bun_t_gallbladder==F &
surg_bun_t_cystoscopy==F &
surg_bun_t_bowel==F &
surg_bun_t_removal==F &
surg_bun_t_cyst==F &
`surg_bun_t_add-on`==F &
surg_bun_t_pancreas==F &
surg_bun_t_renal==F &
surg_bun_t_pancreas==F &
surg_bun_t_lap==T &
surg_bun_t_fluid==F &
surg_bun_t_imag==F &
surg_bun_t_colon==F &
surg_bun_t_partial==F &
surg_bun_t_partial==F &
surg_bun_t_implant==F
]
apendx_n_sodium %>% plot_med_density() %>% print()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
apendx_n_sodium %>% get_tag_cor() %>% print()
## Warning in stats::cor(cor_data): the standard deviation is zero
## # A tibble: 130 x 2
## name correlation
## <chr> <dbl>
## 1 duration_mean 0.558
## 2 duration_max 0.481
## 3 medi_bun_t_choline 0.174
## 4 radi_bun_t_with_dye 0.153
## 5 radi_bun_t_ct 0.140
## 6 radi_bun_t_echo 0.139
## 7 faci_bun_t_care 0.128
## 8 radi_bun_t_imag 0.113
## 9 radi_bun_t_imaging 0.113
## 10 radi_bun_t_mri 0.113
## # ... with 120 more rows
apendx_n_sodium %>% plot_price_vs_duration() %>% print()
apendx_n_sodium[duration_mean<2] %>% plot_price_vs_duration() %>% print()
apendx_n_sodium[duration_mean<2] %>% plot_med_density() %>% print()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
apendx_n_sodium[duration_mean<2] %>% get_tag_cor() %>% print()
## Warning in stats::cor(cor_data): the standard deviation is zero
## # A tibble: 125 x 2
## name correlation
## <chr> <dbl>
## 1 medi_bun_t_choline 0.275
## 2 duration_max 0.269
## 3 duration_mean 0.241
## 4 radi_bun_t_echo 0.200
## 5 radi_bun_t_imag 0.183
## 6 radi_bun_t_imaging 0.183
## 7 radi_bun_t_mri 0.183
## 8 radi_bun_t_without_dye 0.183
## 9 anes_bun_t_mod_sed 0.183
## 10 radi_bun_t_exam 0.182
## # ... with 115 more rows
apendx_n_sodium <- apendx_n_sodium[duration_mean<2]
apendx_n_sodium [medi_bun_t_choline==T] %>% nrow()
## [1] 1
apendx_n_sodium[radi_bun_t_imaging==T] %>% nrow()
## [1] 1
apendx_n_sodium[medi_bun_t_saline==T] %>% nrow()
## [1] 3
apendx_n_sodium <- apendx_n_sodium[medi_bun_t_choline==F & radi_bun_t_imaging==F & medi_bun_t_saline==F]
apendx_n_sodium %>% plot_med_density() %>% print()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
apendx_n_sodium %>% get_tag_cor() %>% print()
## Warning in stats::cor(cor_data): the standard deviation is zero
## # A tibble: 116 x 2
## name correlation
## <chr> <dbl>
## 1 duration_max 0.281
## 2 duration_mean 0.245
## 3 radi_bun_t_echo 0.188
## 4 radi_bun_t_ct 0.181
## 5 radi_bun_t_contrast 0.173
## 6 radi_bun_t_exam 0.164
## 7 radi_bun_t_abdom 0.153
## 8 faci_bun_t_est 0.135
## 9 surg_bun_t_dev 0.129
## 10 surg_bun_t_device 0.129
## # ... with 106 more rows
apendx_n_sodium[radi_bun_t_echo==T] %>% nrow()
## [1] 106
apendx_n_sodium[radi_bun_t_exam==T] %>% nrow()
## [1] 128
apendx_n_sodium[radi_bun_t_ct==T] %>% nrow()
## [1] 174
apendx_n_sodium_btbv4 <- apendx_n_sodium %>% btbv4()
apendx_n_sodium_bq <- apendx_n_sodium_btbv4[,
primary_doctor := pmap(.l=list(doctor_npi1=doctor_npi_str1,
doctor_npi2=doctor_npi_str2,
class_reqs="Surgery"#,
# specialization_reqs = ""
),
.f=calculate_primary_doctor) %>% as.character()
] %>%
#Filter out any procedures where our doctors fail both criteria.
.[!(primary_doctor %in% c("BOTH_DOC_FAIL_CRIT", "TWO_FIT_ALL_SPECS", "NONE_FIT_SPEC_REQ"))] %>%
.[,primary_doctor_npi := fifelse(primary_doctor==doctor_str1,
doctor_npi_str1,
doctor_npi_str2)] %>%
.[,`:=`(procedure_type=7, procedure_modifier="Standard")]
## [1] "multiple meet class req"
## [1] "KELLY D NOLAN" "MARK RYAN MAWHINNEY"
## [1] "multiple meet class req"
## [1] "DAVID E SKARDA" "BRIAN THOMAS BUCHER"
## [1] "multiple meet class req"
## [1] "MILDA SHAPIRO" "WILLIAM NOEL PEUGH"
## [1] "multiple meet class req"
## [1] "ROBERT SHELDON PRICE" "NICKOLAS RAY BYRGE"
## [1] "multiple meet class req"
## [1] "STEPHEN JOSEPH FENTON" "DOUGLAS C BARNHART"
apendx_n_sodium_bq <- apendx_n_sodium_bq[,.(
primary_doctor,
primary_doctor_npi,
most_important_fac ,
most_important_fac_npi,
procedure_type,
procedure_modifier,
tp_med_med,
tp_med_surg,
tp_med_medi,
tp_med_path,
tp_med_radi,
tp_med_anes,
tp_med_faci,
ingest_date = Sys.Date()
)]
apendx_n_sodium_bq <- apendx_n_sodium_bq[,
primary_doctor_npi := fifelse(primary_doctor=="ALEXANDER LORENZO COLONNA",
"1568659324",
primary_doctor_npi)]
bq_table_upload(x=procedure_table, values= apendx_n_sodium_bq, create_disposition='CREATE_IF_NEEDED', write_disposition='WRITE_APPEND')