forager • Tidy Healthcare Revenue Cycle Analysis • forager

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

📦 Installation

You can install forager from GitHub with:

# install.packages("pak")
pak::pak("andrewallenbruce/forager")

🔰 Usage

library(tidyverse)
library(clock)
library(ymd)
library(forager)
library(fuimus)

The Lifecycle of a Claim

(x <- mock_claims(15000))

#> # A tibble: 15,000 × 10
#>    claimid payer     charges balance date_ser…¹ date_rel…² date_sub…³ date_acc…⁴
#>    <chr>   <fct>       <dbl>   <dbl> <date>     <date>     <date>     <date>    
#>  1 00135   American      90.     90. 2024-07-06 2024-07-21 2024-07-26 2024-08-11
#>  2 00159   New York…     87.      0  2024-07-06 2024-07-16 2024-07-18 2024-08-03
#>  3 00180   Medicare     187.    187. 2024-07-06 2024-07-20 2024-07-25 2024-08-06
#>  4 00195   BCBS WY      174.    174. 2024-07-06 2024-07-22 2024-07-25 2024-08-03
#>  5 00199   Athene        32.     32. 2024-07-06 2024-07-13 2024-07-17 2024-07-25
#>  6 00251   BCBS WY      260.    260. 2024-07-06 2024-07-10 2024-07-13 2024-07-23
#>  7 00369   HCSC         104.    104. 2024-07-06 2024-07-16 2024-07-20 2024-07-28
#>  8 00373   Athene       144.    144. 2024-07-06 2024-07-19 2024-07-20 2024-07-24
#>  9 00481   Humana       119.      0  2024-07-06 2024-07-08 2024-07-09 2024-07-14
#> 10 00522   Molina       102.    102. 2024-07-06 2024-07-19 2024-07-20 2024-07-30
#> # ℹ 14,990 more rows
#> # ℹ abbreviated names: ¹date_service, ²date_release, ³date_submission,
#> #   ⁴date_acceptance
#> # ℹ 2 more variables: date_adjudication <date>, date_reconciliation <date>

(x <- prep_claims(x))

#> # A tibble: 15,000 × 13
#>    claimid payer         charges balance date_service aging_bin   dar days_rel…¹
#>    <chr>   <fct>           <dbl>   <dbl> <date>       <fct>     <dbl>      <dbl>
#>  1 00001   Humana            87.     87. 2024-06-11   0-30         16          1
#>  2 00002   Cigna            216.      0  2024-05-12   0-30         29          1
#>  3 00003   Equitable        140.    140. 2024-06-07   0-30         24          3
#>  4 00004   Highmark         185.    185. 2024-05-21   0-30         27          3
#>  5 00005   HCSC              72.     72. 2024-04-27   0-30         29          6
#>  6 00006   BCBS WY          124.    124. 2024-05-03   31-60        31          7
#>  7 00007   Athene           230.    230. 2024-04-12   0-30         30          8
#>  8 00008   New York Life     43.     43. 2024-06-09   31-60        50         12
#>  9 00009   New York Life    256.    256. 2024-04-20   31-60        34         17
#> 10 00010   Lincoln Nat'l    236.    236. 2024-05-05   31-60        49         12
#> # ℹ 14,990 more rows
#> # ℹ abbreviated name: ¹days_release
#> # ℹ 5 more variables: days_submission <dbl>, days_acceptance <dbl>,
#> #   days_adjudication <dbl>, days_reconciliation <dbl>, dates <list>

summarise_claims(x) |> 
  glimpse()

#> Rows: 1
#> Columns: 9
#> $ n_claims            <int> 15000
#> $ gross_charges       <dbl> 1986374
#> $ ending_ar           <dbl> 1323904
#> $ mean_release        <dbl> 8.010267
#> $ mean_submission     <dbl> 3.013267
#> $ mean_acceptance     <dbl> 7.467933
#> $ mean_adjudication   <dbl> 15.0508
#> $ mean_reconciliation <dbl> 2.227745
#> $ mean_dar            <dbl> 34.28633

x |> 
  group_by(
    year = year(date_service),
    month = month(date_service),
    payer
  ) |>
  summarise_claims() |> 
  arrange(payer) |>
  select(year, month, payer, n_claims, ending_ar, mean_dar)

#> # A tibble: 108 × 6
#>     year month payer     n_claims ending_ar mean_dar
#>    <int> <int> <fct>        <int>     <dbl>    <dbl>
#>  1  2024     4 Humana         131    11829.      35.
#>  2  2024     5 Humana         197    16190.      34.
#>  3  2024     6 Humana         209    18879.      34.
#>  4  2024     7 Humana          39     2893.      33.
#>  5  2024     4 Cigna          106    10455.      34.
#>  6  2024     5 Cigna          208    17423.      34.
#>  7  2024     6 Cigna          190    17407.      33.
#>  8  2024     7 Cigna           44     3708.      36.
#>  9  2024     4 Equitable      125    11415.      35.
#> 10  2024     5 Equitable      202    13414.      35.
#> # ℹ 98 more rows

x |> 
  group_by(
    year = year(date_service),
    qtr = quarter(date_service),
    payer
    ) |>
  summarise_claims() |> 
  arrange(payer) |>
  select(year, qtr, payer, n_claims, ending_ar, mean_dar)

#> # A tibble: 54 × 6
#>     year   qtr payer     n_claims ending_ar mean_dar
#>    <int> <int> <fct>        <int>     <dbl>    <dbl>
#>  1  2024     2 Humana         537    46899.      34.
#>  2  2024     3 Humana          39     2893.      33.
#>  3  2024     2 Cigna          504    45285.      34.
#>  4  2024     3 Cigna           44     3708.      36.
#>  5  2024     2 Equitable      519    41301.      35.
#>  6  2024     3 Equitable       40     2298.      35 
#>  7  2024     2 Highmark       489    41779.      35.
#>  8  2024     3 Highmark        29     2757.      34.
#>  9  2024     2 HCSC           547    52913.      34.
#> 10  2024     3 HCSC            41     3544.      34.
#> # ℹ 44 more rows

Days in AR Calculation

Monthly

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 × 15
#>    date         gct  earb  ndip   adc  dart   dar dar_pass ratio_id…¹ ratio_ac…²
#>    <date>     <int> <int> <int> <dbl> <dbl> <dbl> <lgl>         <dbl>      <dbl>
#>  1 2024-01-01 2.5e5 2.9e5    31 8021.    35   36. FALSE           1.1        1.2
#>  2 2024-02-01 2.5e5 2.9e5    29 8604.    35   34. TRUE            1.2        1.2
#>  3 2024-03-01 2.5e5 2.9e5    31 8066.    35   36. FALSE           1.1        1.2
#>  4 2024-04-01 2.5e5 2.9e5    30 8339.    35   35. TRUE            1.2        1.2
#>  5 2024-05-01 2.5e5 2.9e5    31 8047.    35   36. FALSE           1.1        1.2
#>  6 2024-06-01 2.5e5 2.9e5    30 8329.    35   35. TRUE            1.2        1.2
#>  7 2024-07-01 2.5e5 2.9e5    31 8099.    35   36. FALSE           1.1        1.2
#>  8 2024-08-01 2.5e5 2.9e5    31 8061.    35   36. FALSE           1.1        1.2
#>  9 2024-09-01 2.5e5 2.9e5    30 8302.    35   35. TRUE            1.2        1.2
#> 10 2024-10-01 2.5e5 2.9e5    31 8049.    35   36. FALSE           1.1        1.2
#> 11 2024-11-01 2.5e5 2.9e5    30 8313.    35   35. TRUE            1.2        1.2
#> 12 2024-12-01 2.5e5 2.9e5    31 8054.    35   36. FALSE           1.1        1.2
#> # ℹ abbreviated names: ¹ratio_ideal, ²ratio_actual
#> # ℹ 5 more variables: ratio_diff <dbl>, earb_target <dbl>, earb_diff <dbl>,
#> #   gct_pct <dbl>, earb_pct <dbl>

Quarterly

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 × 15
#>   date         earb   gct  ndip   adc  dart   dar dar_pass ratio_id…¹ ratio_ac…²
#>   <date>      <int> <int> <int> <dbl> <dbl> <dbl> <lgl>         <dbl>      <dbl>
#> 1 2024-03-01 284676 7.5e5    91 8245.    35   35. TRUE           0.38       0.38
#> 2 2024-06-01 285645 7.5e5    91 8262.    35   35. TRUE           0.38       0.38
#> 3 2024-09-01 285768 7.5e5    92 8162.    35   35. FALSE          0.38       0.38
#> 4 2024-12-01 285689 7.5e5    92 8133.    35   35. FALSE          0.38       0.38
#> # ℹ abbreviated names: ¹ratio_ideal, ²ratio_actual
#> # ℹ 5 more variables: ratio_diff <dbl>, earb_target <dbl>, earb_diff <dbl>,
#> #   gct_pct <dbl>, earb_pct <dbl>

⚖️ Code of Conduct

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.

🏛️ Governance

This project is primarily maintained by Andrew Bruce. Other authors may occasionally assist with some of these duties.