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
forager is a work-in-progress, the goal of which is to become a suite of integrated analytics tools focused on a comprehensive overview of a healthcare organization’s operational and financial performance areas. Build your own rule-based, automated reporting pipeline to monitor:
- Patient Scheduling
- Coding / Billing
- Productivity
- Collections & A/R
- Denial Management
Installation
You can install the development version of forager from GitHub with:
# install.packages("devtools")
devtools::install_github("andrewallenbruce/forager")
# install.packages("remotes")
remotes::install_github("andrewallenbruce/forager")Foundation: Time Measurement
Everything in a Healthcare RCM workflow is built upon the bedrock of time measurement.
- Task a is created at time t.
- Subtask a1 is assigned at time t1 to responsible party x1.
- Subtask a2 is assigned at time t2 to responsible party x2.
- So on, and so forth until…
- Task ai is completed at time ti.
Measuring the amount of time between each step becomes crucial in identifying workflow issues.
Example: The Lifecycle of a Claim
- Provider Lag: Days between Date of Service and Date of Release
- Billing Lag: Days between Date of Release and Date of Submission
- Acceptance Lag: Days between Date of Submission and Date of Acceptance
- Payment Lag: Days between Date of Acceptance and Date of Adjudication
- Days in AR: Days between Date of Release and Date of Adjudication
x <- forager::generate_data(15000)
x |> head(n = 10)
#> # A tibble: 10 × 5
#> claim_id payer ins_class balance dates
#> <variable> <chr> <chr> <variable> <list>
#> 1 00001 Medicaid Secondary 152.85747 <tibble [1 × 5]>
#> 2 00002 UnitedHealth Secondary 36.05447 <tibble [1 × 5]>
#> 3 00003 Centene Secondary 90.33910 <tibble [1 × 5]>
#> 4 00004 UnitedHealth Secondary 51.64573 <tibble [1 × 5]>
#> 5 00005 Medicaid Secondary 154.65047 <tibble [1 × 5]>
#> 6 00006 Humana Secondary 132.19150 <tibble [1 × 5]>
#> 7 00007 Centene Primary 77.79067 <tibble [1 × 5]>
#> 8 00008 Humana Secondary 139.89570 <tibble [1 × 5]>
#> 9 00009 Anthem Primary 71.28777 <tibble [1 × 5]>
#> 10 00010 Centene Secondary 141.13423 <tibble [1 × 5]>
x |> tidyr::unnest(dates) |>
tidyr::pivot_longer(cols = tidyr::starts_with("date"),
names_to = "date_type",
values_to = "date") |>
head(n = 10) |>
gluedown::md_table()| claim_id | payer | ins_class | balance | date_type | date |
|---|---|---|---|---|---|
| 00001 | Medicaid | Secondary | 152.85747 | date_of_service | 2020-09-19 |
| 00001 | Medicaid | Secondary | 152.85747 | date_of_release | 2020-09-30 |
| 00001 | Medicaid | Secondary | 152.85747 | date_of_submission | 2020-10-02 |
| 00001 | Medicaid | Secondary | 152.85747 | date_of_acceptance | 2020-10-05 |
| 00001 | Medicaid | Secondary | 152.85747 | date_of_adjudication | 2020-11-09 |
| 00002 | UnitedHealth | Secondary | 36.05447 | date_of_service | 2020-07-19 |
| 00002 | UnitedHealth | Secondary | 36.05447 | date_of_release | 2020-08-02 |
| 00002 | UnitedHealth | Secondary | 36.05447 | date_of_submission | 2020-08-04 |
| 00002 | UnitedHealth | Secondary | 36.05447 | date_of_acceptance | 2020-08-04 |
| 00002 | UnitedHealth | Secondary | 36.05447 | date_of_adjudication | 2020-09-09 |
x |> tidyr::unnest(dates) |>
count_days(date_of_service, date_of_release, provider_lag) |>
count_days(date_of_release, date_of_submission, billing_lag) |>
count_days(date_of_submission, date_of_acceptance, processing_lag) |>
count_days(date_of_submission, date_of_adjudication, payer_lag) |>
count_days(date_of_release, date_of_adjudication, days_in_ar) |>
dplyr::group_by(month = clock::date_month_factor(date_of_service)) |>
dplyr::summarise(no_of_claims = dplyr::n(),
balance_total = sum(balance),
avg_prov_lag = round(mean(provider_lag), 2),
avg_bill_lag = round(mean(billing_lag), 2),
avg_accept_lag = round(mean(processing_lag), 2),
avg_pay_lag = round(mean(payer_lag), 2),
avg_days_in_ar = round(mean(days_in_ar), 2), .groups = "drop") |>
gluedown::md_table()| month | no_of_claims | balance_total | avg_prov_lag | avg_bill_lag | avg_accept_lag | avg_pay_lag | avg_days_in_ar |
|---|---|---|---|---|---|---|---|
| January | 1277 | 174559.7 | 10.79 | 2.20 | 3.05 | 33.18 | 35.37 |
| February | 1263 | 171249.5 | 11.22 | 2.32 | 3.11 | 33.01 | 35.34 |
| March | 1225 | 156436.5 | 11.05 | 2.31 | 3.14 | 33.05 | 35.36 |
| April | 1257 | 168064.5 | 11.11 | 2.39 | 3.08 | 33.19 | 35.58 |
| May | 1281 | 167327.5 | 11.22 | 2.31 | 3.09 | 33.20 | 35.51 |
| June | 1251 | 163328.0 | 10.84 | 2.28 | 3.20 | 33.26 | 35.54 |
| July | 1238 | 166397.6 | 10.92 | 2.31 | 3.06 | 33.17 | 35.47 |
| August | 1239 | 162255.0 | 10.82 | 2.32 | 3.09 | 33.10 | 35.41 |
| September | 1281 | 173373.6 | 10.99 | 2.22 | 3.12 | 33.09 | 35.31 |
| October | 1200 | 160917.7 | 11.12 | 2.28 | 3.09 | 33.25 | 35.53 |
| November | 1236 | 165392.2 | 11.00 | 2.35 | 3.16 | 33.16 | 35.51 |
| December | 1252 | 167007.3 | 10.92 | 2.39 | 3.11 | 33.21 | 35.60 |
x |> tidyr::unnest(dates) |>
count_days(date_of_service, date_of_release, provider_lag) |>
count_days(date_of_release, date_of_submission, billing_lag) |>
count_days(date_of_submission, date_of_acceptance, processing_lag) |>
count_days(date_of_submission, date_of_adjudication, payer_lag) |>
count_days(date_of_release, date_of_adjudication, days_in_ar) |>
dplyr::group_by(qtr = lubridate::quarter(date_of_service)) |>
dplyr::summarise(no_of_claims = dplyr::n(), balance_total = sum(balance), avg_prov_lag = round(mean(provider_lag), 2), avg_bill_lag = round(mean(billing_lag), 2),
avg_accept_lag = round(mean(processing_lag), 2),
avg_pay_lag = round(mean(payer_lag), 2),
avg_days_in_ar = round(mean(days_in_ar), 2), .groups = "drop") |>
gluedown::md_table()| qtr | no_of_claims | balance_total | avg_prov_lag | avg_bill_lag | avg_accept_lag | avg_pay_lag | avg_days_in_ar |
|---|---|---|---|---|---|---|---|
| 1 | 3765 | 502245.7 | 11.02 | 2.28 | 3.10 | 33.08 | 35.36 |
| 2 | 3789 | 498720.0 | 11.06 | 2.33 | 3.12 | 33.22 | 35.54 |
| 3 | 3758 | 502026.2 | 10.91 | 2.28 | 3.09 | 33.12 | 35.40 |
| 4 | 3688 | 493317.2 | 11.01 | 2.34 | 3.12 | 33.21 | 35.55 |
Aging Calculation
x |>
tidyr::unnest(dates) |>
forager:::count_days(date_of_service, date_of_adjudication, days_in_ar) |>
dplyr::group_by(aging_bucket = cut(days_in_ar, breaks = seq(0, 500, by = 30))) |>
dplyr::summarise(no_of_claims = dplyr::n(),
balance_total = sum(balance), .groups = "drop") |>
gluedown::md_table()| aging_bucket | no_of_claims | balance_total |
|---|---|---|
| (0,30] | 19 | 2776.459 |
| (30,60] | 14899 | 1984261.643 |
| (60,90] | 82 | 9271.023 |
Days in AR Monthly Calculation
y <- tibble::tibble(
date = clock::date_build(2022, 1:12),
gct = abs(rnorm(12, c(365000.567, 169094.46, 297731.74), c(2:3))),
earb = abs(rnorm(12, c(182771.32, 169633.64, 179347.72), c(2:3))))
y |>
forager::dar_month(date, gct, earb, dart = 35) |>
gluedown::md_table()| date | month | nmon | ndip | gct | earb | earb_trg | earb_dc | earb_pct | adc | dar | pass | actual | ideal | radiff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-01-01 | January | 1 | 31 | 365000.5 | 182770.0 | 412097.3 | -229327.35 | -125.47319 | 11774.210 | 15.52291 | TRUE | 0.5007390 | 1.129032 | -0.6282932 |
| 2022-02-01 | February | 2 | 28 | 169090.8 | 169632.9 | 211363.5 | -41730.61 | -24.60054 | 6038.958 | 28.08977 | TRUE | 1.0032060 | 1.250000 | -0.2467940 |
| 2022-03-01 | March | 3 | 31 | 297734.2 | 179348.2 | 336151.6 | -156803.34 | -87.42955 | 9604.330 | 18.67368 | TRUE | 0.6023769 | 1.129032 | -0.5266554 |
| 2022-04-01 | April | 4 | 30 | 364997.1 | 182774.3 | 425829.9 | -243055.66 | -132.98132 | 12166.570 | 15.02266 | TRUE | 0.5007554 | 1.166667 | -0.6659112 |
| 2022-05-01 | May | 5 | 31 | 169096.5 | 169633.6 | 190915.4 | -21281.81 | -12.54576 | 5454.725 | 31.09846 | TRUE | 1.0031762 | 1.129032 | -0.1258560 |
| 2022-06-01 | June | 6 | 30 | 297726.8 | 179345.0 | 347347.9 | -168002.94 | -93.67584 | 9924.227 | 18.07143 | TRUE | 0.6023811 | 1.166667 | -0.5642856 |
| 2022-07-01 | July | 7 | 31 | 365002.2 | 182771.0 | 412099.3 | -229328.24 | -125.47298 | 11774.264 | 15.52292 | TRUE | 0.5007395 | 1.129032 | -0.6282928 |
| 2022-08-01 | August | 8 | 31 | 169092.2 | 169627.4 | 190910.6 | -21283.18 | -12.54702 | 5454.588 | 31.09811 | TRUE | 1.0031650 | 1.129032 | -0.1258673 |
| 2022-09-01 | September | 9 | 30 | 297729.7 | 179344.1 | 347351.3 | -168007.25 | -93.67872 | 9924.324 | 18.07116 | TRUE | 0.6023722 | 1.166667 | -0.5642945 |
| 2022-10-01 | October | 10 | 31 | 364999.9 | 182773.2 | 412096.7 | -229323.51 | -125.46892 | 11774.191 | 15.52320 | TRUE | 0.5007485 | 1.129032 | -0.6282837 |
| 2022-11-01 | November | 11 | 30 | 169094.4 | 169636.7 | 197276.8 | -27640.12 | -16.29372 | 5636.479 | 30.09621 | TRUE | 1.0032069 | 1.166667 | -0.1634597 |
| 2022-12-01 | December | 12 | 31 | 297725.8 | 179346.6 | 336142.0 | -156795.39 | -87.42591 | 9604.057 | 18.67405 | TRUE | 0.6023886 | 1.129032 | -0.5266437 |
Days in AR Quarterly Calculation
y |> forager::dar_qtr(date, gct, earb, 35) |>
gluedown::md_table()| date | nqtr | ndip | gct_qtr | earb | earb_trg | earb_dc | earb_pct | adc | dar | pass | actual | ideal | radiff |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-03-01 | 1 | 90 | 831825.6 | 179348.2 | 323487.7 | -144139.5 | -80.37 | 9242.51 | 19.40 | TRUE | 0.22 | 0.39 | -0.17 |
| 2022-06-01 | 2 | 91 | 831820.4 | 179345.0 | 319930.9 | -140585.9 | -78.39 | 9140.88 | 19.62 | TRUE | 0.22 | 0.38 | -0.16 |
| 2022-09-01 | 3 | 92 | 831824.2 | 179344.1 | 316454.8 | -137110.7 | -76.45 | 9041.57 | 19.84 | TRUE | 0.22 | 0.38 | -0.16 |
| 2022-12-01 | 4 | 92 | 831820.1 | 179346.6 | 316453.3 | -137106.7 | -76.45 | 9041.52 | 19.84 | TRUE | 0.22 | 0.38 | -0.16 |
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.