Forager (noun)
A person that goes from place to place searching for things that they can eat or use.1
Ager (noun)
A person that calls from place to place searching for payment before insurance can refuse.2
You can install the development version of forager from
GitHub with:
# install.packages("pak")
pak::pak("andrewallenbruce/forager")library(tidyverse)
library(clock)
library(forager)
library(fuimus)* <b>Provider Lag</b>: Date of Service - Date of Release
* <b>Billing Lag</b>: Date of Release - Date of Submission
* <b>Acceptance Lag</b>: Date of Submission - Date of Acceptance
* <b>Payment Lag</b>: Date of Acceptance - Date of Adjudication
* <b>Days in AR</b>: Date of Release - Date of Adjudication*
x <- forager::generate_data(1500)
x
#> # A tibble: 1,500 × 10
#> claimid payer charges balance date_service date_release date_submission
#> <fct> <fct> <dbl> <dbl> <date> <date> <date>
#> 1 0005 Cigna 265. 0 2024-05-15 2024-05-20 2024-05-22
#> 2 0031 UHC 25.8 25.8 2024-05-15 2024-05-15 2024-05-15
#> 3 0053 Cigna 85.6 85.6 2024-05-15 2024-05-20 2024-05-24
#> 4 0127 Humana 21.5 0 2024-05-15 2024-05-21 2024-05-23
#> 5 0366 Cigna 43.1 43.1 2024-05-15 2024-05-24 2024-05-24
#> 6 0370 BCBS 80.9 80.9 2024-05-15 2024-05-26 2024-06-01
#> 7 0496 Medicare 64.4 64.4 2024-05-15 2024-05-19 2024-05-19
#> 8 0564 Anthem 14.2 14.2 2024-05-15 2024-05-20 2024-05-23
#> 9 0591 Humana 52.2 52.2 2024-05-15 2024-05-21 2024-05-22
#> 10 0733 Centene 121. 121. 2024-05-15 2024-05-31 2024-06-05
#> 11 0774 Cigna 74.6 74.6 2024-05-15 2024-05-24 2024-05-28
#> 12 0841 BCBS 31.2 0 2024-05-15 2024-05-17 2024-05-18
#> 13 0981 Anthem 36.2 36.2 2024-05-15 2024-05-23 2024-05-23
#> 14 1039 UHC 345. 345. 2024-05-15 2024-05-22 2024-05-27
#> 15 1050 Medicare 92.4 92.4 2024-05-15 2024-05-26 2024-06-05
#> 16 1148 Medicaid 53.4 0 2024-05-15 2024-05-25 2024-05-27
#> 17 1395 Cigna 458. 458. 2024-05-15 2024-06-05 2024-06-09
#> 18 1419 UHC 312. 312. 2024-05-15 2024-05-25 2024-05-28
#> 19 1453 Cigna 134. 0 2024-05-15 2024-05-30 2024-06-04
#> 20 0068 Humana 59.8 59.8 2024-05-14 2024-05-18 2024-05-22
#> # ℹ 1,480 more rows
#> # ℹ 3 more variables: date_acceptance <date>, date_adjudication <date>,
#> # date_reconciliation <date>x_pvt <- x |>
select(claimid:date_reconciliation) |>
pivot_longer(
cols = starts_with("date"),
names_to = "date_type",
values_to = "date") |>
mutate(
date_lag = lead(date) - date,
date_lag = lag(date_lag, order_by = date),
date_type = stringr::str_to_sentence(
str_remove_all(date_type, "date_")),
date_type = fct_relevel(
date_type,
"Service",
"Release",
"Submission",
"Acceptance",
"Adjudication",
"Reconciliation"),
.by = claimid)
x_pvt
#> # A tibble: 9,000 × 7
#> claimid payer charges balance date_type date date_lag
#> <fct> <fct> <dbl> <dbl> <fct> <date> <drtn>
#> 1 0005 Cigna 265. 0 Service 2024-05-15 NA days
#> 2 0005 Cigna 265. 0 Release 2024-05-20 5 days
#> 3 0005 Cigna 265. 0 Submission 2024-05-22 2 days
#> 4 0005 Cigna 265. 0 Acceptance 2024-05-30 8 days
#> 5 0005 Cigna 265. 0 Adjudication 2024-07-02 33 days
#> 6 0005 Cigna 265. 0 Reconciliation 2024-07-02 0 days
#> 7 0031 UHC 25.8 25.8 Service 2024-05-15 NA days
#> 8 0031 UHC 25.8 25.8 Release 2024-05-15 0 days
#> 9 0031 UHC 25.8 25.8 Submission 2024-05-15 0 days
#> 10 0031 UHC 25.8 25.8 Acceptance 2024-05-29 14 days
#> # ℹ 8,990 more rowsx |>
group_by(
year = get_year(date_service),
month = date_month_factor(date_service)) |>
summarise(
Claims = n(),
Balance = sum(balance, na.rm = TRUE),
Release = as.numeric(mean((date_release - date_service), na.rm = TRUE)),
Submission = as.numeric(mean((date_submission - date_release), na.rm = TRUE)),
Acceptance = as.numeric(mean((date_acceptance - date_submission), na.rm = TRUE)),
Adjudication = as.numeric(mean((date_adjudication - date_acceptance), na.rm = TRUE)),
Reconciliation = as.numeric(mean((date_reconciliation - date_adjudication), na.rm = TRUE)),
Average_DAR = as.numeric(mean((date_reconciliation - date_service), na.rm = TRUE)),
.groups = "drop")
#> # A tibble: 4 × 10
#> year month Claims Balance Release Submission Acceptance Adjudication
#> <int> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2024 February 195 17437. 8.01 3.16 13.0 75.1
#> 2 2024 March 518 49572. 8.12 3.16 12.5 73.4
#> 3 2024 April 530 49209. 7.95 3.02 12.6 75.4
#> 4 2024 May 257 23824. 7.93 3.16 12.4 74.5
#> # ℹ 2 more variables: Reconciliation <dbl>, Average_DAR <dbl>x |>
group_by(
year = get_year(date_service),
nqtr = quarter(date_service)) |>
summarise(
Claims = n(),
Balance = sum(balance, na.rm = TRUE),
Release = as.numeric(mean((date_release - date_service), na.rm = TRUE)),
Submission = as.numeric(mean((date_submission - date_release), na.rm = TRUE)),
Acceptance = as.numeric(mean((date_acceptance - date_submission), na.rm = TRUE)),
Adjudication = as.numeric(mean((date_adjudication - date_acceptance), na.rm = TRUE)),
Reconciliation = as.numeric(mean((date_reconciliation - date_adjudication), na.rm = TRUE)),
Average_DAR = as.numeric(mean((date_reconciliation - date_service), na.rm = TRUE)),
.groups = "drop")
#> # A tibble: 2 × 10
#> year nqtr Claims Balance Release Submission Acceptance Adjudication
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2024 1 713 67009. 8.09 3.16 12.6 73.9
#> 2 2024 2 787 73033. 7.95 3.06 12.5 75.1
#> # ℹ 2 more variables: Reconciliation <dbl>, Average_DAR <dbl>x |>
mutate(days_in_ar = as.numeric((date_reconciliation - date_service))) |>
bin_aging(days_in_ar, bin_type = "chop") |>
group_by(aging_bin) |>
summarise(
n_claims = n(),
balance = roundup(sum(balance, na.rm = TRUE)),
.groups = "drop")
#> # A tibble: 7 × 3
#> aging_bin n_claims balance
#> <fct> <int> <dbl>
#> 1 (30, 60] 39 0
#> 2 (60, 90] 129 0
#> 3 (90, 120] 147 0
#> 4 (120, 150] 105 0
#> 5 (150, 180] 21 0
#> 6 (180, 210] 1 0
#> 7 <NA> 1058 140042.x |>
mutate(days_in_ar = as.numeric((date_reconciliation - date_service))) |>
bin_aging(days_in_ar, bin_type = "case") |>
group_by(aging_bin) |>
summarise(
n_claims = n(),
balance = roundup(sum(balance, na.rm = TRUE)),
.groups = "drop")
#> # A tibble: 5 × 3
#> aging_bin n_claims balance
#> <fct> <int> <dbl>
#> 1 31-60 39 0
#> 2 61-90 129 0
#> 3 91-120 147 0
#> 4 121+ 127 0
#> 5 <NA> 1058 140042.tibble(
date = date_build(2024, 1:12),
gct = rpois(12, 250000:400000),
earb = rpois(12, 290000:400000)
) |>
avg_dar(
date,
gct,
earb,
dart = 35,
by = "month")
#> # A tibble: 12 × 27
#> date gct earb nmon mon month nqtr yqtr dqtr year ymon
#> <date> <int> <int> <dbl> <ord> <ord> <int> <dbl> <chr> <dbl> <dbl>
#> 1 2024-01-01 249588 289611 1 Jan January 1 2024. 1Q24 2024 2024.
#> 2 2024-02-01 249478 290606 2 Feb February 1 2024. 1Q24 2024 2024.
#> 3 2024-03-01 250528 290108 3 Mar March 1 2024. 1Q24 2024 2024.
#> 4 2024-04-01 250502 290315 4 Apr April 2 2024. 2Q24 2024 2024.
#> 5 2024-05-01 249248 288934 5 May May 2 2024. 2Q24 2024 2024.
#> 6 2024-06-01 250733 289835 6 Jun June 2 2024. 2Q24 2024 2024.
#> 7 2024-07-01 249966 290830 7 Jul July 3 2024. 3Q24 2024 2024.
#> 8 2024-08-01 249868 289768 8 Aug August 3 2024. 3Q24 2024 2024.
#> 9 2024-09-01 249620 289403 9 Sep September 3 2024. 3Q24 2024 2024.
#> 10 2024-10-01 249388 290610 10 Oct October 4 2024. 4Q24 2024 2024.
#> 11 2024-11-01 250213 289555 11 Nov November 4 2024. 4Q24 2024 2024.
#> 12 2024-12-01 250364 289094 12 Dec December 4 2024. 4Q24 2024 2024.
#> # ℹ 16 more variables: myear <chr>, nhalf <int>, yhalf <dbl>, dhalf <chr>,
#> # ndip <int>, adc <dbl>, dar <dbl>, dar_pass <lgl>, dar_diff <dbl>,
#> # ratio_actual <dbl>, ratio_ideal <dbl>, ratio_diff <dbl>, earb_target <dbl>,
#> # earb_dec_abs <dbl>, earb_dec_pct <dbl>, earb_gct_diff <int>tibble(
date = date_build(2024, 1:12),
gct = rpois(12, 250000:400000),
earb = rpois(12, 285500:400000)
) |>
avg_dar(
date,
gct,
earb,
dart = 35,
by = "quarter")
#> # A tibble: 4 × 18
#> date earb nmon nqtr month gct ndip adc dar dar_pass dar_diff
#> <date> <int> <dbl> <int> <ord> <int> <int> <dbl> <dbl> <lgl> <dbl>
#> 1 2024-03-01 285303 3 1 March 750838 91 8251. 34.6 TRUE -0.422
#> 2 2024-06-01 286436 6 2 June 750307 91 8245. 34.7 TRUE -0.260
#> 3 2024-09-01 284659 9 3 Sept… 750146 92 8154. 34.9 TRUE -0.0886
#> 4 2024-12-01 285243 12 4 Dece… 749404 92 8146. 35.0 FALSE 0.0176
#> # ℹ 7 more variables: ratio_actual <dbl>, ratio_ideal <dbl>, ratio_diff <dbl>,
#> # earb_target <dbl>, earb_dec_abs <dbl>, earb_dec_pct <dbl>,
#> # earb_gct_diff <int>Please note that the forager project is released with a Contributor
Code of
Conduct.
By contributing to this project, you agree to abide by its terms.