`{costoffice}`

The costoffice package contains functions enabling the user to access the latest Physician Office Visit Costs datasets from Data.CMS.gov.

There are 83 datasets in total, each representing a different medical specialty.

Broken down by ZIP code, they contain the:

Most Utilized HCPCS Level II Procedure Code (for both New and Established patients)
Price Medicare Paid for the Visit (Min-Mode-Max)
Copay the Patient Paid for the Visit (Min-Mode-Max)

Installation

You can install the development version of costoffice from GitHub with:

# install.packages("devtools")
devtools::install_github("andrewallenbruce/costoffice", build_vignettes = TRUE)

# install.packages("remotes")
remotes::install_github("andrewallenbruce/costoffice", build_vignettes = TRUE)

library(costoffice)

`search_datasets()`

Returns a data frame of each dataset’s medical specialty, date of most recent release, and a download link for it’s corresponding csv file:

# Call with no arguments to return the entire data frame
search_datasets()

#> # A tibble: 83 × 3
#>    specialty                                        released   csv_url          
#>    <chr>                                            <date>     <chr>            
#>  1 addiction_medicine                               2022-07-14 https://data.cms…
#>  2 advanced_heart_failure_and_transplant_cardiology 2022-07-14 https://data.cms…
#>  3 allergy_immunology                               2022-07-14 https://data.cms…
#>  4 anesthesiology                                   2022-07-14 https://data.cms…
#>  5 cardiac_surgery                                  2022-07-14 https://data.cms…
#>  6 cardiology                                       2022-07-14 https://data.cms…
#>  7 certified_clinical_nurse_specialist              2022-07-14 https://data.cms…
#>  8 certified_nurse_midwife                          2022-07-14 https://data.cms…
#>  9 certified_registered_nurse_anesthetist_crna      2022-07-14 https://data.cms…
#> 10 clinic_or_group_practice                         2022-07-14 https://data.cms…
#> # ℹ 73 more rows

If needed, there are two arguments to this function. Use the specialty argument to return only exact matches (note the underscore in the names):

search_datasets(specialty = "emergency_medicine")

#> # A tibble: 1 × 3
#>   specialty          released   csv_url                                         
#>   <chr>              <date>     <chr>                                           
#> 1 emergency_medicine 2022-07-14 https://data.cms.gov/provider-data/sites/defaul…

Use the keyword argument to return partial matches:

search_datasets(keyword = "medicine")

#> # A tibble: 12 × 3
#>    specialty                            released   csv_url                      
#>    <chr>                                <date>     <chr>                        
#>  1 addiction_medicine                   2022-07-14 https://data.cms.gov/provide…
#>  2 emergency_medicine                   2022-07-14 https://data.cms.gov/provide…
#>  3 geriatric_medicine                   2022-07-14 https://data.cms.gov/provide…
#>  4 internal_medicine                    2022-07-14 https://data.cms.gov/provide…
#>  5 nuclear_medicine                     2022-07-14 https://data.cms.gov/provide…
#>  6 osteopathic_manipulative_medicine    2022-07-14 https://data.cms.gov/provide…
#>  7 pediatric_medicine                   2022-07-14 https://data.cms.gov/provide…
#>  8 physical_medicine_and_rehabilitation 2022-07-14 https://data.cms.gov/provide…
#>  9 preventive_medicine                  2022-07-14 https://data.cms.gov/provide…
#> 10 sleep_medicine                       2022-07-14 https://data.cms.gov/provide…
#> 11 sports_medicine                      2022-07-14 https://data.cms.gov/provide…
#> 12 undersea_and_hyperbaric_medicine     2022-07-14 https://data.cms.gov/provide…

Return a vector of the exact names of the medical specialties by simply calling search_datasets()$specialty:

Available Medical Specialties

addiction_medicine
advanced_heart_failure_and_transplant_cardiology
allergy_immunology
anesthesiology
cardiac_surgery
cardiology
certified_clinical_nurse_specialist
certified_nurse_midwife
certified_registered_nurse_anesthetist_crna
clinic_or_group_practice
clinical_cardiac_electrophysiology
clinical_laboratory
colorectal_surgery_proctology
critical_care_intensivists
dentist
dermatology
diagnostic_radiology
emergency_medicine
endocrinology
family_practice
gastroenterology
general_practice
general_surgery
geriatric_medicine
geriatric_psychiatry
gynecological_oncology
hand_surgery
hematology
hematology_oncology
hematopoietic_cell_transplantation_and_cellular_therapy
hospice_and_palliative_care
hospitalist
infectious_disease
internal_medicine
interventional_cardiology
interventional_pain_management
interventional_radiology
licensed_clinical_social_worker
mammography_center
maxillofacial_surgery
medical_genetics_and_genomics
medical_oncology
medical_toxicology
nephrology
neurology
neuropsychiatry
neurosurgery
nuclear_medicine
nurse_practitioner
obstetrics_gynecology
ophthalmology
optometry
oral_surgery_dentist_only
orthopedic_surgery
osteopathic_manipulative_medicine
otolaryngology
pain_management
pathology
pediatric_medicine
peripheral_vascular_disease
physical_medicine_and_rehabilitation
physical_therapist_in_private_practice
physician_assistant
plastic_and_reconstructive_surgery
podiatry
preventive_medicine
psychiatry
psychologist_clinical
public_health_or_welfare_agency
pulmonary_disease
radiation_oncology
registered_dietitian_or_nutrition_professional
rheumatology
sleep_medicine
speech_language_pathologist
sports_medicine
surgical_oncology
thoracic_surgery
undefined_physician_type
undersea_and_hyperbaric_medicine
unknown_supplierprovider_specialty
urology
vascular_surgery

Example: Overview

dir <- "E:/costoffice_data/costoffice_2022_raw_data/"
paths <- list.files(dir, pattern = "[.]csv$", full.names = TRUE)
names <- basename(paths)
out <- gsub(".csv", ".rds", names)
outdir <- "E:/costoffice_data/costoffice_2022_clean_data/"

df_specialty <- out |>
  purrr::map(\(x) costoffice:::summarise_by_specialty(dir = outdir, name = x)) |>
  purrr::list_rbind()

df_state <- out |>
  purrr::map(\(x) costoffice:::summarise_by_state(dir = outdir, name = x)) |>
  purrr::list_rbind()

df_spec_state <- out |>
  purrr::map(\(x) costoffice:::summarise_by_spec_state(dir = outdir, name = x)) |>
  purrr::list_rbind()

Summary by Specialty

df_specialty

#> # A tibble: 332 × 9
#>    specialty          type      n   min avg_min avg_mode avg_max   max avg_range
#>    <chr>              <chr> <int> <dbl>   <dbl>    <dbl>   <dbl> <dbl>     <dbl>
#>  1 Addiction Medicine Esta… 43530  4.06    4.06     25.9    4.06  47.7      31.7
#>  2 Addiction Medicine Esta… 43530 16.3    16.3     104.    16.3  191.      127. 
#>  3 Addiction Medicine New … 43530 13.3    13.3      33.6   13.3   58.4      29.8
#>  4 Addiction Medicine New … 43530 53.1    53.1     134.    53.1  234.      119. 
#>  5 Advanced Heart Fa… Esta… 43530  4.06    4.06     25.9    4.06  47.7      31.7
#>  6 Advanced Heart Fa… Esta… 43530 16.3    16.3     104.    16.3  191.      127. 
#>  7 Advanced Heart Fa… New … 43530 13.3    13.3      33.6   13.3   58.4      29.8
#>  8 Advanced Heart Fa… New … 43530 53.1    53.1     134.    53.1  234.      119. 
#>  9 Allergy Immunology Esta… 43530  4.06    4.06     18.2    4.06  47.7      31.7
#> 10 Allergy Immunology Esta… 43530 16.3    16.3      73.0   16.3  191.      127. 
#> # ℹ 322 more rows

table(df_specialty$specialty, df_specialty$type) |> 
  as.data.frame() |> 
  dplyr::tibble() |> 
  dplyr::select(specialty = Var1, 
                type = Var2, 
                count = Freq) |> 
  dplyr::group_by(type) |> 
  dplyr::summarise(count = sum(count)) |> 
  dplyr::arrange(dplyr::desc(count))

#> # A tibble: 16 × 2
#>    type                      count
#>    <fct>                     <int>
#>  1 Established Copay (99214)    41
#>  2 Established Price (99214)    41
#>  3 Established Copay (99213)    39
#>  4 Established Price (99213)    39
#>  5 New Copay (99204)            32
#>  6 New Price (99204)            32
#>  7 New Copay (99203)            29
#>  8 New Price (99203)            29
#>  9 New Copay (99205)            18
#> 10 New Price (99205)            18
#> 11 New Copay (NA)                4
#> 12 New Price (NA)                4
#> 13 Established Copay (99211)     2
#> 14 Established Price (99211)     2
#> 15 Established Copay (99215)     1
#> 16 Established Price (99215)     1

Summary by State

df_state

#> # A tibble: 19,920 × 9
#>    state type                   n   min avg_min avg_mode avg_max   max avg_range
#>    <chr> <chr>              <int> <dbl>   <dbl>    <dbl>   <dbl> <dbl>     <dbl>
#>  1 AK    Established Copay…   282  5.59    5.59     34.0    5.59  47.7      42.1
#>  2 AK    Established Price…   282 22.4    22.4     136.    22.4  191.      168. 
#>  3 AK    New Copay (99204)    282 18.7    18.7      44.1   18.7   58.4      39.7
#>  4 AK    New Price (99204)    282 74.8    74.8     176.    74.8  234.      159. 
#>  5 AL    Established Copay…   859  4.06    4.06     24.8    4.06  34.8      30.5
#>  6 AL    Established Price…   859 16.3    16.3      99.3   16.3  139.      122. 
#>  7 AL    New Copay (99204)    859 13.4    13.4      32.3   13.4   42.7      28.8
#>  8 AL    New Price (99204)    859 53.5    53.5     129.    53.5  171.      115. 
#>  9 AR    Established Copay…   738  4.07    4.07     23.9    4.07  34.2      29.4
#> 10 AR    Established Price…   738 16.3    16.3      95.6   16.3  137.      118. 
#> # ℹ 19,910 more rows

table(df_state$state, df_state$type) |> 
  as.data.frame() |> 
  dplyr::tibble() |> 
  dplyr::select(state = Var1, 
                type = Var2, 
                count = Freq) |> 
  dplyr::group_by(type) |> 
  dplyr::summarise(count = sum(count)) |> 
  dplyr::arrange(dplyr::desc(count))

#> # A tibble: 16 × 2
#>    type                      count
#>    <fct>                     <int>
#>  1 Established Copay (99214)  2419
#>  2 Established Price (99214)  2419
#>  3 Established Copay (99213)  2301
#>  4 Established Price (99213)  2301
#>  5 New Copay (99204)          1888
#>  6 New Price (99204)          1888
#>  7 New Copay (99203)          1711
#>  8 New Price (99203)          1711
#>  9 New Copay (99205)          1062
#> 10 New Price (99205)          1062
#> 11 New Copay (NA)              236
#> 12 New Price (NA)              236
#> 13 Established Copay (99211)   118
#> 14 Established Price (99211)   118
#> 15 Established Copay (99215)    59
#> 16 Established Price (99215)    59

Summary by Specialty & State

df_spec_state

#> # A tibble: 19,920 × 10
#>    specialty          state type  zip_codes   min avg_min avg_mode avg_max   max
#>    <chr>              <chr> <chr>     <int> <dbl>   <dbl>    <dbl>   <dbl> <dbl>
#>  1 Addiction Medicine AK    Esta…       282  5.59    5.59     34.0    5.59  47.7
#>  2 Addiction Medicine AK    Esta…       282 22.4    22.4     136.    22.4  191. 
#>  3 Addiction Medicine AK    New …       282 18.7    18.7      44.1   18.7   58.4
#>  4 Addiction Medicine AK    New …       282 74.8    74.8     176.    74.8  234. 
#>  5 Addiction Medicine AL    Esta…       859  4.06    4.06     24.8    4.06  34.8
#>  6 Addiction Medicine AL    Esta…       859 16.3    16.3      99.3   16.3  139. 
#>  7 Addiction Medicine AL    New …       859 13.4    13.4      32.3   13.4   42.7
#>  8 Addiction Medicine AL    New …       859 53.5    53.5     129.    53.5  171. 
#>  9 Addiction Medicine AR    Esta…       738  4.07    4.07     23.9    4.07  34.2
#> 10 Addiction Medicine AR    Esta…       738 16.3    16.3      95.6   16.3  137. 
#> # ℹ 19,910 more rows
#> # ℹ 1 more variable: avg_range <dbl>

Example: `Family Practice` Specialty

fam_pract <- search_datasets(specialty = "family_practice") |> 
  dplyr::pull(csv_url) |> 
  costoffice:::tidyup(name = "Family Practice")

# Mode Variation by Region
fam_pract |> 
  dplyr::mutate(hcpcs = paste0("(", hcpcs, ")")) |> 
  tidyr::unite("type", c(patient, cost, hcpcs), sep = " ") |> 
  dplyr::group_by(region, type) |> 
  skimr::skim(dplyr::where(is.numeric)) |> 
  skimr::yank("numeric") |> 
  dplyr::filter(skim_variable == "mode") |> 
  dplyr::select(!c(n_missing, complete_rate, skim_variable)) |>
  dplyr::arrange(type) |> 
  head(20)

Variable type: numeric

region	type	mean	sd	p0	p25	p50	p75	p100	hist
Northeast	Established Copay (99214)	26.94	1.87	24.69	25.41	25.83	28.37	31.32	▇▂▃▂▂
Midwest	Established Copay (99214)	25.15	0.72	24.32	24.57	25.16	25.37	27.76	▇▆▂▁▁
South	Established Copay (99214)	25.42	1.25	23.90	24.70	25.21	25.94	30.05	▇▆▁▁▁
West	Established Copay (99214)	27.04	2.04	24.21	25.52	26.58	28.05	33.96	▇▇▂▁▁
NA	Established Copay (99214)	26.40	1.14	23.90	26.32	26.32	26.32	31.32	▂▇▂▁▁
Northeast	Established Price (99214)	107.76	7.49	98.76	101.62	103.32	113.47	125.27	▇▂▃▂▂
Midwest	Established Price (99214)	100.61	2.88	97.26	98.30	100.66	101.50	111.04	▇▆▂▁▁
South	Established Price (99214)	101.68	5.01	95.61	98.79	100.83	103.76	120.20	▇▆▁▁▁
West	Established Price (99214)	108.16	8.17	96.84	102.07	106.33	112.22	135.85	▇▇▂▁▁
NA	Established Price (99214)	105.59	4.57	95.61	105.28	105.28	105.28	125.27	▂▇▂▁▁
Northeast	New Copay (99203)	23.37	1.72	21.31	21.99	22.31	24.61	27.52	▇▂▃▂▂
Midwest	New Copay (99203)	21.73	0.68	20.98	21.17	21.79	22.01	24.25	▇▇▁▁▁
South	New Copay (99203)	22.00	1.14	20.58	21.36	21.80	22.56	26.15	▇▇▁▁▁
West	New Copay (99203)	23.39	1.75	20.84	22.08	23.01	24.26	29.17	▅▇▃▁▁
NA	New Copay (99203)	22.88	1.03	20.58	22.82	22.82	22.82	27.52	▂▇▂▁▁
Northeast	New Price (99203)	93.47	6.89	85.25	87.96	89.24	98.42	110.06	▇▂▃▂▂
Midwest	New Price (99203)	86.94	2.74	83.92	84.67	87.16	88.06	96.99	▇▇▁▁▁
South	New Price (99203)	88.01	4.57	82.31	85.46	87.21	90.25	104.59	▇▇▁▁▁
West	New Price (99203)	93.56	6.99	83.37	88.34	92.05	97.05	116.69	▅▇▃▁▁
NA	New Price (99203)	91.51	4.10	82.31	91.28	91.28	91.28	110.06	▂▇▂▁▁

# Established Patient Price (99214)
fam_pract |> 
  dplyr::filter(cost == "Price") |> 
  dplyr::mutate(hcpcs = paste0("(", hcpcs, ")")) |> 
  tidyr::unite("type", c(patient, cost, hcpcs), sep = " ") |> 
  dplyr::group_by(type) |> 
  skimr::skim(dplyr::where(is.numeric)) |> 
  skimr::yank("numeric") |> 
  dplyr::filter(type == "Established Price (99214)") |> 
  dplyr::select(!c(n_missing, complete_rate, type))

Variable type: numeric

skim_variable	mean	sd	p0	p25	p50	p75	p100	hist
min	18.07	1.46	16.26	17.04	17.62	18.54	23.77	▇▃▂▁▁
max	144.80	9.07	133.85	138.80	142.08	147.31	190.74	▇▃▁▁▁
mode	103.65	6.63	95.61	98.99	101.50	105.65	135.85	▇▃▁▁▁
range	126.72	7.68	117.58	121.56	124.49	128.95	168.37	▇▂▁▁▁

# New Patient Price (99203)
fam_pract |> 
  dplyr::filter(cost == "Price") |> 
  dplyr::mutate(hcpcs = paste0("(", hcpcs, ")")) |> 
  tidyr::unite("type", c(patient, cost, hcpcs), sep = " ") |> 
  dplyr::group_by(type) |> 
  skimr::skim(dplyr::where(is.numeric)) |> 
  skimr::yank("numeric") |> 
  dplyr::filter(type == "New Price (99203)") |> 
  dplyr::select(!c(n_missing, complete_rate, type))

Variable type: numeric

skim_variable	mean	sd	p0	p25	p50	p75	p100	hist
min	58.24	4.04	53.14	55.31	56.94	59.43	74.82	▇▃▁▁▁
max	177.24	11.13	163.67	169.74	174.06	180.62	233.63	▇▃▁▁▁
mode	89.70	5.93	82.31	85.61	87.96	91.33	116.69	▇▃▁▁▁
range	119.00	7.12	110.54	114.54	116.89	120.94	158.82	▇▂▁▁▁

# Established Patient Copay (99214)
fam_pract |> 
  dplyr::filter(cost == "Copay") |> 
  dplyr::mutate(hcpcs = paste0("(", hcpcs, ")")) |> 
  tidyr::unite("type", c(patient, cost, hcpcs), sep = " ") |> 
  dplyr::group_by(type) |> 
  skimr::skim(dplyr::where(is.numeric)) |> 
  skimr::yank("numeric") |> 
  dplyr::filter(type == "Established Copay (99214)") |> 
  dplyr::select(!c(n_missing, complete_rate, type))

Variable type: numeric

skim_variable	mean	sd	p0	p25	p50	p75	p100	hist
min	4.52	0.36	4.06	4.26	4.40	4.64	5.94	▇▃▂▁▁
max	36.20	2.27	33.46	34.70	35.52	36.83	47.69	▇▃▁▁▁
mode	25.91	1.66	23.90	24.75	25.37	26.41	33.96	▇▃▁▁▁
range	31.68	1.92	29.40	30.39	31.12	32.24	42.09	▇▂▁▁▁

# New Patient Copay (99203)
fam_pract |> 
  dplyr::filter(cost == "Copay") |> 
  dplyr::mutate(hcpcs = paste0("(", hcpcs, ")")) |> 
  tidyr::unite("type", c(patient, cost, hcpcs), sep = " ") |> 
  dplyr::group_by(type) |> 
  skimr::skim(dplyr::where(is.numeric)) |> 
  skimr::yank("numeric") |> 
  dplyr::filter(type == "New Copay (99203)") |> 
  dplyr::select(!c(n_missing, complete_rate, type))

Variable type: numeric

skim_variable	mean	sd	p0	p25	p50	p75	p100	hist
min	14.56	1.01	13.28	13.83	14.23	14.86	18.70	▇▃▁▁▁
max	44.31	2.78	40.92	42.43	43.51	45.16	58.41	▇▃▁▁▁
mode	22.42	1.48	20.58	21.40	21.99	22.83	29.17	▇▃▁▁▁
range	29.75	1.78	27.63	28.64	29.22	30.24	39.70	▇▂▁▁▁

Code

library(ggplot2)
library(cmapplot)
p <- fam_pract |> 
  dplyr::filter(cost == "Price") |> 
  dplyr::mutate(hcpcs = paste0("(", hcpcs, ")")) |> 
  tidyr::unite("type", c(patient, cost, hcpcs), sep = " ") |> 
  ggplot(aes(x = mode, 
             color = type, 
             fill = type)) + 
  cmapplot::theme_cmap(
    gridlines = "h", 
    axisticks = "x",
    panel.grid.major.y = element_line(color = "light gray")) +
  geom_density(alpha = 0.5) +
  scale_x_continuous(n.breaks = 5) +
  cmapplot::cmap_fill_discrete("governance") +
  cmapplot::cmap_color_discrete("governance") +
  guides(fill = guide_legend(reverse = TRUE),
         color = "none")

Code

gg_new_price <- fam_pract |> 
  dplyr::filter(cost == "Price", 
                patient == "New") |> 
  dplyr::filter(!is.na(state)) |> 
  dplyr::mutate(hcpcs = paste0("(", hcpcs, ")")) |> 
  tidyr::unite("type", 
               c(patient, cost, hcpcs), 
               sep = " ") |> 
  ggplot() + 
  cmapplot::theme_cmap(
    gridlines = "h", 
    axisticks = "x",
    panel.grid.major.y = element_line(color = "light gray")) +
  stat_summary(
    aes(x = mode, 
        y = forcats::fct_reorder(state, mode, median)),
    fun.min = min,
    fun.max = max,
    fun = median)

gg_est_price <- fam_pract |> 
  dplyr::filter(cost == "Price", 
                patient == "Established") |> 
  dplyr::filter(!is.na(state)) |> 
  dplyr::mutate(hcpcs = paste0("(", hcpcs, ")")) |> 
  tidyr::unite("type", 
               c(patient, cost, hcpcs), 
               sep = " ") |> 
  ggplot() + 
  cmapplot::theme_cmap(
    gridlines = "h", 
    axisticks = "x",
    panel.grid.major.y = element_line(color = "light gray")) +
  stat_summary(
    aes(x = mode, 
        y = forcats::fct_reorder(state, mode, median)),
    fun.min = min,
    fun.max = max,
    fun = median)

gg_new_copay <- fam_pract |> 
  dplyr::filter(cost == "Copay", 
                patient == "New") |> 
  dplyr::filter(!is.na(state)) |> 
  dplyr::mutate(hcpcs = paste0("(", hcpcs, ")")) |> 
  tidyr::unite("type", 
               c(patient, cost, hcpcs), 
               sep = " ") |> 
  ggplot() + 
  cmapplot::theme_cmap(
    gridlines = "h", 
    axisticks = "x",
    panel.grid.major.y = element_line(color = "light gray")) +
  stat_summary(
    aes(x = mode, 
        y = forcats::fct_reorder(state, mode, median)),
    fun.min = min,
    fun.max = max,
    fun = median)

gg_est_copay <- fam_pract |> 
  dplyr::filter(cost == "Copay", 
                patient == "Established") |> 
  dplyr::filter(!is.na(state)) |> 
  dplyr::mutate(hcpcs = paste0("(", hcpcs, ")")) |> 
  tidyr::unite("type", 
               c(patient, cost, hcpcs), 
               sep = " ") |> 
  ggplot() + 
  cmapplot::theme_cmap(
    gridlines = "h", 
    axisticks = "x",
    panel.grid.major.y = element_line(color = "light gray")) +
  stat_summary(
    aes(x = mode, 
        y = forcats::fct_reorder(state, mode, median)),
    fun.min = min,
    fun.max = max,
    fun = median)

Code of Conduct

Please note that the costoffice project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Name		Name	Last commit message	Last commit date
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.github		.github
R		R
data-raw		data-raw
data		data
man		man
pkgdown		pkgdown
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.Rhistory		.Rhistory
.gitignore		.gitignore
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
DESCRIPTION		DESCRIPTION
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
Rplots.pdf		Rplots.pdf
_pkgdown.yml		_pkgdown.yml
codecov.yml		codecov.yml
costoffice.Rproj		costoffice.Rproj

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