R Cookbook: Medical Billing

medical-billing medical-coding data-analytics

Personal R code cookbook for common Medical Billing performance analyses.

Andrew Bruce https://twitter.com/aabrucehimni (Healthcare Analytics in R)https://andrewbruce.netlify.app/about
04-06-2022
Updates

Updated 2021-09-21: Added paragraph about the actual .htaccess file.

Introduction

This post is simply a sandbox/cookbook of R code I’m exploring for common Medical Billing analyses.

Packages

Load Packages 
library(tidyverse)
library(tidytable)
library(rmarkdown)
library(lubridate)
library(htmltools)
library(tidymetrics)
library(googlesheets4)
library(janitor)
library(skimr)
library(tidycharts)

Data

I’m using a mock data set I’ve created and uploaded to Google Drive (which you can copy and download here) using the {randomNames} package for creating biller and client names, the {fixtuRes} package to generate claim volumes, claim rejections, and number of hours worked:

Load Data 
# Sheet is public and shareable
gs4_deauth()

# Google Sheet ID
id_billdata <- "1Qt65n2a4_M-tlXRdPB5mgqJ9zO4JeXMTks5a0PvLL38"

# Read in Google Sheet
google_billdata <- read_sheet(ss = id_billdata, sheet = "billing_data")

# Convert date column
google_billdata$date <- as.Date(google_billdata$date, "%m/%d/%Y")

# Convert client/biller columns to factors
google_billdata$client <- as.factor(google_billdata$client)
google_billdata$biller_last <- as.factor(google_billdata$biller_last)

# Print head of data
paged_table(google_billdata)

Performance Targets

I’m just looking to build a sort of proof of concept so I’ll only be looking at Rejection Rate and Claims Submitted Per Hour, and then analysing by time period, client, and biller.

Rejection Rate

Rejection Rate is a medical billing effectiveness measurement that is calculated by dividing the number of claims submitted by the number of rejections. There’s normally an internal benchmark that billers are required to keep their rejection rate under. I’ll be using \(3\%\) in this example.

Rejected healthcare claims are those that are submitted to a clearinghouse but are not sent on to a payer (i.e., not entered into their computer systems). This generally occurs because the claim has missing, incomplete, outdated, or incorrect information. Conversely, denied claims are received and processed by the payer who, after completing the adjudication process, determine the claim to be unpayable.

A denial can be appealed whereas a rejection cannot. It simply needs to be corrected and resubmitted. The time lost correcting and resubmitting a claim is what makes the Rejection Rate a vital performance measurement.

Claims Per Hour

Number of claims submitted per hour is a medical billing efficiency metric, calculated by dividing the total number of claims submitted by the number of hours worked. Again, there’s normally an internal benchmark set that billers need to meet or exceed. Current industry standards tend to fluctuate anywhere between 30 to 40 claims per hour. I’ll be using \(35\) claims per hour in this example.

An additional efficiency metric is the number of claims submitted per day. In this context, “day” means 8 hours, i.e. the average work day. The first step of the calculation consists of dividing the number of hours worked by 8. Divide the total number of claims submitted by the result in the first step for the final figure.

Declare Target Variables

# Rejection Rate Target
rr_trg <- 0.03

# Claims Per Hour Target
clmph_trg <- 35
Measure Type Target Formula
Rejection Rate Effectiveness \(< 3\%\) (Rejections \(\div\) Claims Submitted) \(\times\) 100
Claims Per Hour Efficiency \(\geq 35\) Claims Submitted \(\div\) Hours Worked

Cleaning & Calculations

# Convert data frame to tibble
google_billdata <- google_billdata |>
  as_tibble()

billdata_cross <- google_billdata |>
  tidymetrics::cross_by_periods() |>
  tidymetrics::cross_by_dimensions(biller_last, client) |>
  summarise(
    hrs = sum(hrs),
    claim = sum(claim),
    reject = sum(reject), .groups = "drop"
  ) |>
  mutate(
    rejrate = reject / claim,
    clmphr = claim / hrs,
    days = hrs / 8,
    clmpday = claim / days,
    rr_pass = case_when(rejrate < rr_trg ~ as.logical(1), TRUE ~ as.logical(0)),
    clmphr_pass = case_when(clmphr >= clmph_trg ~ as.logical(1), TRUE ~ as.logical(0)),
    nmon = as.ordered(lubridate::month(date, label = FALSE)),
    month = lubridate::month(date, label = TRUE, abbr = FALSE),
    dqtr = paste0(lubridate::quarter(date), "Q", format(date, "%y"))
  ) |>
  relocate(nmon:dqtr, .after = date)

paged_table(billdata_cross)

Iterating Over the Data

Overall

bill_all_period <- billdata_cross |>
  filter(biller_last == "All", client == "All") |>
  select(!(biller_last:client)) |>
  group_split.(period, .keep = FALSE, .named = TRUE)

WEEKLY

paged_table(bill_all_period$week) |>
  select(!c(nmon:dqtr)) |>
  gt::gt()
date hrs claim reject rejrate clmphr days clmpday rr_pass clmphr_pass
2021-01-04 191.0000 6858 167 0.02435112 35.90576 23.87500 287.2461 TRUE TRUE
2021-01-11 215.0000 6771 160 0.02363019 31.49302 26.87500 251.9442 TRUE FALSE
2021-01-18 188.0000 6881 159 0.02310711 36.60106 23.50000 292.8085 TRUE TRUE
2021-01-25 173.0000 6507 152 0.02335946 37.61272 21.62500 300.9017 TRUE TRUE
2021-02-01 191.2000 7006 147 0.02098202 36.64226 23.90000 293.1381 TRUE TRUE
2021-02-08 187.0000 7504 135 0.01799041 40.12834 23.37500 321.0267 TRUE TRUE
2021-02-15 185.8000 6912 185 0.02676505 37.20129 23.22500 297.6103 TRUE TRUE
2021-02-22 198.0600 6179 173 0.02799806 31.19762 24.75750 249.5809 TRUE FALSE
2021-03-01 185.9360 7143 142 0.01987960 38.41644 23.24200 307.3315 TRUE TRUE
2021-03-08 186.3480 7722 144 0.01864802 41.43859 23.29350 331.5087 TRUE TRUE
2021-03-15 209.4601 6966 140 0.02009762 33.25693 26.18251 266.0554 TRUE FALSE
2021-03-22 192.3288 7710 172 0.02230869 40.08759 24.04110 320.7007 TRUE TRUE
2021-03-29 168.5533 7311 148 0.02024347 43.37501 21.06916 347.0001 TRUE TRUE
2021-04-05 168.0000 7514 204 0.02714932 44.72619 21.00000 357.8095 TRUE TRUE
2021-04-12 181.0000 6527 170 0.02604566 36.06077 22.62500 288.4862 TRUE TRUE
2021-04-19 191.0000 7678 188 0.02448554 40.19895 23.87500 321.5916 TRUE TRUE
2021-04-26 190.0000 6991 153 0.02188528 36.79474 23.75000 294.3579 TRUE TRUE
2021-05-03 167.8617 6574 124 0.01886218 39.16320 20.98271 313.3056 TRUE TRUE
2021-05-10 156.2158 6139 152 0.02475973 39.29820 19.52697 314.3856 TRUE TRUE
2021-05-17 147.2380 6119 158 0.02582121 41.55857 18.40475 332.4686 TRUE TRUE
2021-05-24 164.9611 6600 112 0.01696970 40.00943 20.62014 320.0754 TRUE TRUE
2021-05-31 158.0802 5788 137 0.02366966 36.61433 19.76002 292.9147 TRUE TRUE
2021-06-07 192.5475 7571 179 0.02364285 39.32017 24.06843 314.5614 TRUE TRUE
2021-06-14 171.3322 7239 156 0.02154994 42.25125 21.41653 338.0100 TRUE TRUE
2021-06-21 185.0221 7252 184 0.02537231 39.19531 23.12777 313.5625 TRUE TRUE
2021-06-28 181.2527 7785 132 0.01695568 42.95108 22.65659 343.6086 TRUE TRUE
2021-07-05 175.3389 6531 194 0.02970449 37.24786 21.91737 297.9829 TRUE TRUE
2021-07-12 192.6335 7634 188 0.02462667 39.62966 24.07919 317.0373 TRUE TRUE
2021-07-19 182.9111 6522 133 0.02039252 35.65667 22.86389 285.2533 TRUE TRUE
2021-07-26 152.0756 7689 178 0.02314995 50.56038 19.00945 404.4830 TRUE TRUE
2021-08-02 187.2944 7908 168 0.02124431 42.22229 23.41180 337.7783 TRUE TRUE
2021-08-09 166.4899 7516 142 0.01889303 45.14388 20.81124 361.1511 TRUE TRUE
2021-08-16 195.7025 7150 197 0.02755245 36.53506 24.46281 292.2804 TRUE TRUE
2021-08-23 197.4479 6217 158 0.02541419 31.48678 24.68099 251.8942 TRUE FALSE
2021-08-30 192.6048 6770 168 0.02481536 35.14970 24.07560 281.1976 TRUE TRUE
2021-09-06 193.8058 7346 187 0.02545603 37.90391 24.22573 303.2313 TRUE TRUE
2021-09-13 186.2775 6595 159 0.02410917 35.40416 23.28469 283.2333 TRUE TRUE
2021-09-20 192.8024 6890 137 0.01988389 35.73606 24.10030 285.8885 TRUE TRUE
2021-09-27 201.3381 7497 112 0.01493931 37.23588 25.16726 297.8871 TRUE TRUE
2021-10-04 167.5738 7160 112 0.01564246 42.72744 20.94673 341.8195 TRUE TRUE
2021-10-11 185.7447 6855 218 0.03180160 36.90549 23.21809 295.2439 FALSE TRUE
2021-10-18 201.1810 6494 156 0.02402217 32.27940 25.14762 258.2352 TRUE FALSE
2021-10-25 233.1546 6914 132 0.01909170 29.65415 29.14432 237.2332 TRUE FALSE
2021-11-01 173.5787 7077 170 0.02402148 40.77114 21.69734 326.1691 TRUE TRUE
2021-11-08 208.1927 7990 135 0.01689612 38.37790 26.02409 307.0232 TRUE TRUE
2021-11-15 198.6563 6398 161 0.02516411 32.20638 24.83203 257.6511 TRUE FALSE
2021-11-22 182.7476 7564 149 0.01969857 41.39042 22.84345 331.1234 TRUE TRUE
2021-11-29 176.1145 7527 168 0.02231965 42.73924 22.01432 341.9139 TRUE TRUE
2021-12-06 185.4985 6685 178 0.02662678 36.03803 23.18731 288.3042 TRUE TRUE
2021-12-13 178.6310 7361 182 0.02472490 41.20786 22.32887 329.6628 TRUE TRUE
2021-12-20 184.8027 6679 171 0.02560264 36.14125 23.10034 289.1300 TRUE TRUE
2021-12-27 195.8858 6878 161 0.02340797 35.11229 24.48573 280.8983 TRUE TRUE

Weekly Summary Stats

bill_all_period$week |>
  skim() |>
  yank("numeric") |>
  select(!(n_missing:complete_rate)) |>
  gt::gt()
skim_variable mean sd p0 p25 p50 p75 p100 hist
hrs 184.89772858 15.861464585 147.23797094 174.89887670 186.10677910 192.67573986 233.1545701 ▁▃▇▂▁
claim 7021.03846154 523.446270874 5788.00000000 6598.75000000 6978.50000000 7506.50000000 7990.0000000 ▂▅▇▇▅
reject 159.36538462 23.615406110 112.00000000 142.00000000 159.00000000 174.25000000 218.0000000 ▃▆▇▅▁
rejrate 0.02280253 0.003610842 0.01493931 0.02004419 0.02351908 0.02521616 0.0318016 ▂▆▇▅▁
clmphr 38.21080979 4.004769056 29.65414744 36.00496107 37.75831582 40.88031533 50.5603786 ▃▇▆▃▁
days 23.11221607 1.982683073 18.40474637 21.86235959 23.26334739 24.08446748 29.1443213 ▁▃▇▂▁
clmpday 305.68647836 32.038152447 237.23317955 288.03968860 302.06652655 327.04252263 404.4830287 ▃▇▆▃▁

Weekly Targets

# Rejection Rate Pass/Fail - Weekly
rr_week <- bill_all_period$week |>
  janitor::tabyl(rr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE) |>
  mutate(rr_pass = case_when(rr_pass == "FALSE" ~ "Fail", TRUE ~ "Pass")) |>
  rename("Result" = rr_pass, Weeks = n, Percentage = percent)
rr_week |>
  gt::gt() |>
  gtExtras::gt_theme_nytimes() |>
  gt::tab_header(title = "Rejection Rate by Week") |>
  gt::opt_table_lines()
Rejection Rate by Week
Result Weeks Percentage
Fail 1 1.92%
Pass 51 98.08%
 
# Claims Per Hour Pass/Fail - Weekly
clmphr_week <- bill_all_period$week |>
  janitor::tabyl(clmphr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE) |>
  mutate(clmphr_pass = case_when(clmphr_pass == "FALSE" ~ "Fail", TRUE ~ "Pass")) |>
  rename("Result" = clmphr_pass, Weeks = n, Percentage = percent)
clmphr_week |>
  gt::gt() |>
  gtExtras::gt_theme_nytimes() |>
  gt::tab_header(title = "Claims Per Hour by Week") |>
  gt::opt_table_lines()
Claims Per Hour by Week
Result Weeks Percentage
Fail 7 13.46%
Pass 45 86.54%
 
(week_targets_cross <- SmartEDA::ExpCustomStat(bill_all_period$week, Cvar = c("rr_pass", "clmphr_pass"), gpby = TRUE))
#    rr_pass clmphr_pass Count  Prop
# 1:    TRUE        TRUE    44 84.62
# 2:    TRUE       FALSE     7 13.46
# 3:   FALSE        TRUE     1  1.92
# Crosstable - Monthly
SmartEDA::ExpCustomStat(bill_all_period$week, Cvar = c("rr_pass", "clmphr_pass"), gpby = TRUE, filt = NULL) |>
  gt::gt() |>
  gtExtras::gt_theme_nytimes() |>
  gt::tab_header(title = "Weekly Targets") |>
  gt::opt_table_lines() |>
  gtExtras::fmt_symbol_first(column = Prop, suffix = "%")
Weekly Targets
rr_pass clmphr_pass Count Prop
TRUE TRUE 44 84.62%
TRUE FALSE 7 13.46 
FALSE TRUE 1 1.92 

MONTHLY

bill_all_period$month |>
  select(!c(date:nmon, dqtr)) |>
  gt::gt()
month hrs claim reject rejrate clmphr days clmpday rr_pass clmphr_pass
January 767.0000 27017 638 0.02361476 35.22425 95.87500 281.7940 TRUE TRUE
February 762.0600 27601 640 0.02318757 36.21893 95.25750 289.7515 TRUE TRUE
March 942.6263 36852 746 0.02024313 39.09503 117.82828 312.7602 TRUE TRUE
April 730.0000 28710 715 0.02490421 39.32877 91.25000 314.6301 TRUE TRUE
May 794.3567 31220 683 0.02187700 39.30224 99.29459 314.4179 TRUE TRUE
June 730.1545 29847 651 0.02181124 40.87765 91.26932 327.0212 TRUE TRUE
July 702.9592 28376 693 0.02442205 40.36650 87.86989 322.9320 TRUE TRUE
August 939.5395 35561 833 0.02342454 37.84939 117.44244 302.7951 TRUE TRUE
September 774.2239 28328 595 0.02100395 36.58890 96.77798 292.7112 TRUE TRUE
October 787.6541 27423 618 0.02253583 34.81605 98.45676 278.5284 TRUE FALSE
November 939.2898 36556 783 0.02141919 38.91877 117.41123 311.3501 TRUE TRUE
December 744.8180 27603 692 0.02506974 37.06006 93.10225 296.4805 TRUE TRUE

Monthly Summary Stats

bill_all_period$month |>
  skim() |>
  yank("numeric") |>
  select(!(n_missing:complete_rate)) |>
  gt::gt()
skim_variable mean sd p0 p25 p50 p75 p100 hist
hrs 801.22349050 87.809036714 702.95915455 741.15213342 770.6119294 830.58999023 942.62625724 ▆▇▁▁▅
claim 30424.50000000 3745.812874519 27017.00000000 27602.50000000 28543.0000000 32305.25000000 36852.00000000 ▇▁▁▁▃
reject 690.58333333 69.968120446 595.00000000 639.50000000 687.5000000 722.75000000 833.00000000 ▇▃▆▃▂
rejrate 0.02279277 0.001566213 0.02024313 0.02171323 0.0228617 0.02381658 0.02506974 ▅▇▂▇▇
clmphr 37.97054447 1.983737379 34.81604607 36.49640886 38.3840796 39.30887333 40.87764765 ▃▆▂▇▃
days 100.15293631 10.976129589 87.86989432 92.64401668 96.3264912 103.82374878 117.82828215 ▆▇▁▁▅
clmpday 303.76435580 15.869899035 278.52836852 291.97127087 307.0726370 314.47098661 327.02118121 ▃▆▂▇▃

QUARTERLY

bill_all_period$quarter |>
  select(!c(date:month))
# # A tidytable: 4 × 10
#   dqtr    hrs claim reject rejrate clmphr  days clmpday rr_pass clmphr_pass
#   <chr> <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl>   <dbl> <lgl>   <lgl>      
# 1 1Q21  2472. 91470   2024  0.0221   37.0  309.    296. TRUE    TRUE       
# 2 2Q21  2255. 89777   2049  0.0228   39.8  282.    319. TRUE    TRUE       
# 3 3Q21  2417. 92265   2121  0.0230   38.2  302.    305. TRUE    TRUE       
# 4 4Q21  2472. 91582   2093  0.0229   37.1  309.    296. TRUE    TRUE

Quarterly Summary Stats

bill_all_period$quarter |>
  skim() |>
  yank("numeric") |>
  select(!(n_missing:complete_rate))

Variable type: numeric

skim_variable mean sd p0 p25 p50 p75 p100 hist
hrs 2403.67 102.76 2254.51 2376.17 2444.20 2471.71 2471.76 ▃▁▁▃▇
claim 91273.50 1057.73 89777.00 91046.75 91526.00 91752.75 92265.00 ▃▁▁▇▃
reject 2071.75 43.49 2024.00 2042.75 2071.00 2100.00 2121.00 ▇▇▁▇▇
rejrate 0.02 0.00 0.02 0.02 0.02 0.02 0.02 ▂▁▁▁▇
clmphr 38.01 1.32 37.01 37.04 37.61 38.59 39.82 ▇▁▃▁▃
days 300.46 12.85 281.81 297.02 305.53 308.96 308.97 ▃▁▁▃▇
clmpday 304.11 10.57 296.06 296.32 300.92 308.71 318.57 ▇▁▃▁▃

Client

bill_client_period <- billdata_cross |>
  filter(biller_last == "All", client != "All") |>
  select(!(biller_last)) |>
  group_split.(period, .keep = FALSE, .named = TRUE)

WEEKLY

paged_table(bill_client_period$week) |>
  select(!c(nmon:dqtr))

Weekly Summary Stats

bill_client_period$week |>
  skim() |>
  yank("numeric") |>
  select(!(n_missing:complete_rate))

Variable type: numeric

skim_variable mean sd p0 p25 p50 p75 p100 hist
hrs 18.67 4.15 12.00 15.00 18.08 22.00 26.00 ▇▇▇▇▆
claim 708.92 173.12 403.00 559.50 714.00 861.00 1015.00 ▇▇▇▇▇
reject 16.09 7.58 2.00 10.00 16.00 23.00 32.00 ▇▇▇▇▃
rejrate 0.02 0.01 0.00 0.01 0.02 0.03 0.06 ▇▇▆▂▁
clmphr 39.77 13.13 16.68 30.69 37.58 45.86 83.17 ▅▇▃▂▁
days 2.33 0.52 1.50 1.88 2.26 2.75 3.25 ▇▇▇▇▆
clmpday 318.16 105.06 133.44 245.52 300.67 366.89 665.33 ▅▇▃▂▁

Weekly Targets

# Rejection Rate Pass
bill_client_period$week |>
  janitor::tabyl(rr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE)
#  rr_pass   n percent
#    FALSE 147  28.54%
#     TRUE 368  71.46%
# Claims Per Hour Pass
bill_client_period$week |>
  janitor::tabyl(clmphr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE)
#  clmphr_pass   n percent
#        FALSE 207  40.19%
#         TRUE 308  59.81%
# Crosstable
SmartEDA::ExpCustomStat(bill_client_period$week, Cvar = c("rr_pass", "clmphr_pass"), gpby = TRUE, filt = NULL)
#    rr_pass clmphr_pass Count  Prop
# 1:   FALSE        TRUE    76 14.76
# 2:    TRUE        TRUE   232 45.05
# 3:    TRUE       FALSE   136 26.41
# 4:   FALSE       FALSE    71 13.79

MONTHLY

paged_table(bill_client_period$month) |>
  select(!c(date:nmon, dqtr))

Monthly Summary Stats

bill_client_period$month |>
  skim() |>
  yank("numeric") |>
  select(!(n_missing:complete_rate))

Variable type: numeric

skim_variable mean sd p0 p25 p50 p75 p100 hist
hrs 80.80 12.44 58.00 71.00 80.00 88.90 111.27 ▅▇▇▅▂
claim 3068.02 554.51 2037.00 2612.50 3051.00 3513.50 4438.00 ▅▇▆▅▂
reject 69.64 18.33 30.00 56.00 69.00 84.00 122.00 ▃▇▇▅▁
rejrate 0.02 0.01 0.01 0.02 0.02 0.03 0.04 ▂▇▆▂▁
clmphr 38.20 5.53 22.15 34.83 37.75 41.06 55.17 ▁▅▇▂▁
days 10.10 1.56 7.25 8.88 10.00 11.11 13.91 ▅▇▇▅▂
clmpday 305.62 44.22 177.20 278.66 301.96 328.52 441.35 ▁▅▇▂▁

Monthly Targets

# Rejection Rate Pass
bill_client_period$month |>
  janitor::tabyl(rr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE)
#  rr_pass   n percent
#    FALSE  16  13.45%
#     TRUE 103  86.55%
# Claims Per Hour Pass
bill_client_period$month |>
  janitor::tabyl(clmphr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE)
#  clmphr_pass  n percent
#        FALSE 32  26.89%
#         TRUE 87  73.11%
# Crosstable
SmartEDA::ExpCustomStat(bill_client_period$month, Cvar = c("rr_pass", "clmphr_pass"), gpby = TRUE, filt = NULL)
#    rr_pass clmphr_pass Count  Prop
# 1:    TRUE        TRUE    79 66.39
# 2:    TRUE       FALSE    24 20.17
# 3:   FALSE       FALSE     8  6.72
# 4:   FALSE        TRUE     8  6.72

QUARTERLY

paged_table(bill_client_period$quarter) |>
  select(!c(date:month))

Quarterly Summary Stats

bill_client_period$quarter |>
  skim() |>
  yank("numeric") |>
  select(!(n_missing:complete_rate))

Variable type: numeric

skim_variable mean sd p0 p25 p50 p75 p100 hist
hrs 240.37 23.26 167.47 228.75 236.00 248.25 299.20 ▁▁▇▂▁
claim 9127.35 977.60 6689.00 8541.50 9085.00 9457.50 11595.00 ▁▃▇▂▂
reject 207.18 37.72 119.00 181.75 205.00 233.25 277.00 ▂▆▇▇▆
rejrate 0.02 0.00 0.02 0.02 0.02 0.03 0.03 ▆▇▃▅▆
clmphr 38.06 2.99 28.58 36.57 38.27 40.06 44.90 ▁▂▇▇▂
days 30.05 2.91 20.93 28.59 29.50 31.03 37.40 ▁▁▇▂▁
clmpday 304.47 23.90 228.60 292.57 306.12 320.47 359.17 ▁▂▇▇▂

Quarterly Targets

# Rejection Rate Pass
bill_client_period$quarter |>
  janitor::tabyl(rr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE)
#  rr_pass  n percent
#     TRUE 40 100.00%
# Claims Per Hour Pass
bill_client_period$quarter |>
  janitor::tabyl(clmphr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE)
#  clmphr_pass  n percent
#        FALSE  5  12.50%
#         TRUE 35  87.50%
# Crosstable
SmartEDA::ExpCustomStat(bill_client_period$quarter, Cvar = c("rr_pass", "clmphr_pass"), gpby = TRUE, filt = NULL)
#    rr_pass clmphr_pass Count Prop
# 1:    TRUE        TRUE    35 87.5
# 2:    TRUE       FALSE     5 12.5

Mcintyre, Kaila

bill_Mcintyre <- billdata_cross |>
  filter(biller_last == "Mcintyre") |>
  select(!(biller_last)) |>
  group_split.(period, client, .keep = FALSE, .named = TRUE)

WEEKLY

bill_Mcintyre$week_All |>
  select(!c(nmon:dqtr))
# # A tidytable: 52 × 10
#    date         hrs claim reject rejrate clmphr  days clmpday rr_pass clmphr_pass
#    <date>     <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl>   <dbl> <lgl>   <lgl>      
#  1 2021-01-04  40    1400     33 0.0236    35    5       280  TRUE    TRUE       
#  2 2021-01-11  40    1335     22 0.0165    33.4  5       267  TRUE    FALSE      
#  3 2021-01-18  40    1498     27 0.0180    37.4  5       300. TRUE    TRUE       
#  4 2021-01-25  40    1256     22 0.0175    31.4  5       251. TRUE    FALSE      
#  5 2021-02-01  32.2  1588     13 0.00819   49.3  4.03    395. TRUE    TRUE       
#  6 2021-02-08  37    1356     24 0.0177    36.6  4.62    293. TRUE    TRUE       
#  7 2021-02-15  37.8  1281     34 0.0265    33.9  4.72    271. TRUE    FALSE      
#  8 2021-02-22  38.1  1228     25 0.0204    32.3  4.76    258. TRUE    FALSE      
#  9 2021-03-01  37.9  1428     28 0.0196    37.6  4.74    301. TRUE    TRUE       
# 10 2021-03-08  38.3  1472     31 0.0211    38.4  4.79    307. TRUE    TRUE       
# # … with 42 more rows
# # ℹ Use `print(n = ...)` to see more rows

Target Metrics Performance

# Rejection Rate Pass/Fail - Weekly
bill_Mcintyre$week_All |>
  select(!c(nmon:dqtr)) |>
  janitor::tabyl(rr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE)
#  rr_pass  n percent
#    FALSE  3   5.77%
#     TRUE 49  94.23%
# Claims Per Hour Pass/Fail - Weekly
bill_Mcintyre$week_All |>
  select(!c(nmon:dqtr)) |>
  janitor::tabyl(clmphr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE)
#  clmphr_pass  n percent
#        FALSE 13  25.00%
#         TRUE 39  75.00%
# Crosstable - Monthly
SmartEDA::ExpCustomStat(bill_Mcintyre$week_All, Cvar = c("rr_pass", "clmphr_pass"), gpby = TRUE, filt = NULL)
#    rr_pass clmphr_pass Count  Prop
# 1:    TRUE        TRUE    36 69.23
# 2:    TRUE       FALSE    13 25.00
# 3:   FALSE        TRUE     3  5.77

Monthly

bill_Mcintyre$month_All |>
  select(!c(date:nmon, dqtr))
# # A tidytable: 12 × 10
#    month       hrs claim reject rejrate clmphr  days clmpday rr_pass clmphr_pass
#    <ord>     <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl>   <dbl> <lgl>   <lgl>      
#  1 January   160    5489    104  0.0189   34.3  20      274. TRUE    FALSE      
#  2 February  145.   5453     96  0.0176   37.6  18.1    301. TRUE    TRUE       
#  3 March     196.   7496    129  0.0172   38.3  24.5    307. TRUE    TRUE       
#  4 April     160    6875    123  0.0179   43.0  20      344. TRUE    TRUE       
#  5 May        84.4  3108     44  0.0142   36.8  10.5    295. TRUE    TRUE       
#  6 June      162.   6137    109  0.0178   37.8  20.3    303. TRUE    TRUE       
#  7 July      151.   6170    135  0.0219   40.9  18.9    327. TRUE    TRUE       
#  8 August    195.   7560    126  0.0167   38.9  24.3    311. TRUE    TRUE       
#  9 September 162.   6224    101  0.0162   38.4  20.3    307. TRUE    TRUE       
# 10 October   173.   6030    108  0.0179   34.9  21.6    279. TRUE    FALSE      
# 11 November  197.   8026    150  0.0187   40.7  24.7    325. TRUE    TRUE       
# 12 December  176.   6024    101  0.0168   34.3  22.0    274. TRUE    FALSE

Target Metrics Performance

# Rejection Rate Pass/Fail - Monthly
bill_Mcintyre$month_All |>
  select(!c(date:nmon, dqtr)) |>
  janitor::tabyl(rr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE)
#  rr_pass  n percent
#     TRUE 12 100.00%
# Claims Per Hour Pass/Fail - Monthly
bill_Mcintyre$month_All |>
  select(!c(date:nmon, dqtr)) |>
  janitor::tabyl(clmphr_pass) |>
  adorn_pct_formatting(digits = 2, affix_sign = TRUE)
#  clmphr_pass n percent
#        FALSE 3  25.00%
#         TRUE 9  75.00%
# Crosstable - Monthly
SmartEDA::ExpCustomStat(bill_Mcintyre$month_All, Cvar = c("rr_pass", "clmphr_pass"), gpby = TRUE, filt = NULL)
#    rr_pass clmphr_pass Count Prop
# 1:    TRUE       FALSE     3   25
# 2:    TRUE        TRUE     9   75

Quarterly

bill_Mcintyre$quarter_All |>
  select(!(date:month))
# # A tidytable: 4 × 10
#   dqtr    hrs claim reject rejrate clmphr  days clmpday rr_pass clmphr_pass
#   <chr> <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl>   <dbl> <lgl>   <lgl>      
# 1 1Q21   501. 18438    329  0.0178   36.8  62.6    295. TRUE    TRUE       
# 2 2Q21   407. 16120    276  0.0171   39.7  50.8    317. TRUE    TRUE       
# 3 3Q21   508. 19954    362  0.0181   39.3  63.5    314. TRUE    TRUE       
# 4 4Q21   546. 20080    359  0.0179   36.8  68.2    294. TRUE    TRUE

Lattimore, Victoria

bill_Lattimore <- billdata_cross |>
  filter(biller_last == "Lattimore") |>
  select(!(biller_last)) |>
  group_split.(period, client, .keep = FALSE, .named = TRUE)

paged_table(bill_Lattimore$month_All)

Legrand, Brandi

bill_Legrand <- billdata_cross |>
  filter(biller_last == "Legrand") |>
  select(!(biller_last)) |>
  group_split.(period, client, .keep = FALSE, .named = TRUE)

paged_table(bill_Legrand$month_All)

Bryant, Essence

bill_Bryant <- billdata_cross |>
  filter(biller_last == "Bryant") |>
  select(!(biller_last)) |>
  group_split.(period, client, .keep = FALSE, .named = TRUE)

paged_table(bill_Bryant$month_All)

Patterson, Dominique

bill_Patterson <- billdata_cross |>
  filter(biller_last == "Patterson") |>
  select(!(biller_last)) |>
  group_split.(period, client, .keep = FALSE, .named = TRUE)

paged_table(bill_Patterson$month_All)

One Number to Rule Them All

These are my attempts at what’s come to be known as a “North Star Metric” which you can read more about here. Ideally, by combining as many measurements as is reasonable, you can monitor one number instead of six. The legitimacy of the number/score can be difficult to parse if one does not take great care when creating the calculation. It might take a lot of testing to make sure the final score is accurately reflecting performance.

Biller Quality Index (BQI)

This measure is simply indexing (i.e. counting) the number of weeks that a biller’s

  1. Claims Per Hour is ABOVE the target (35)
  2. Rejection Rate is BELOW the target (3%)
billdata_cross |>
  filter(biller_last != "All" & client == "All" & rr_pass == 1 & period == "week") |>
  select(biller_last, rr_pass) |>
  SmartEDA::ExpCustomStat(
    Cvar = c("biller_last", "rr_pass"),
    stat = c("Count", "Prop"),
    gpby = TRUE, filt = NULL
  ) |>
  arrange(desc(Count))
#    biller_last rr_pass Count  Prop
# 1:    Mcintyre    TRUE    49 23.67
# 2:   Patterson    TRUE    41 19.81
# 3:      Bryant    TRUE    40 19.32
# 4:     Legrand    TRUE    40 19.32
# 5:   Lattimore    TRUE    37 17.87
billdata_cross |>
  filter(biller_last != "All" & client == "All" & clmphr_pass == 1 & period == "week") |>
  select(biller_last, clmphr_pass) |>
  SmartEDA::ExpCustomStat(
    Cvar = c("biller_last", "clmphr_pass"),
    stat = c("Count", "Prop"),
    gpby = TRUE, filt = NULL
  ) |>
  arrange(desc(Count))
#    biller_last clmphr_pass Count  Prop
# 1:    Mcintyre        TRUE    39 22.67
# 2:      Bryant        TRUE    35 20.35
# 3:   Patterson        TRUE    35 20.35
# 4:   Lattimore        TRUE    32 18.60
# 5:     Legrand        TRUE    31 18.02
# Crosstable of Rejection Rate & Claims Per Hour Pass/Fail Combinations
billdata_cross |>
  filter(biller_last != "All" & client == "All" & period == "week") |>
  select(biller_last, rr_pass, clmphr_pass) |>
  SmartEDA::ExpCustomStat(
    Cvar = c("biller_last", "rr_pass", "clmphr_pass"),
    stat = c("Count", "Prop"),
    gpby = TRUE, filt = NULL
  ) |>
  arrange(desc(Count))
#     biller_last rr_pass clmphr_pass Count  Prop
#  1:    Mcintyre    TRUE        TRUE    36 13.85
#  2:      Bryant    TRUE        TRUE    31 11.92
#  3:     Legrand    TRUE        TRUE    29 11.15
#  4:   Patterson    TRUE        TRUE    28 10.77
#  5:   Lattimore    TRUE        TRUE    25  9.62
#  6:   Patterson    TRUE       FALSE    13  5.00
#  7:    Mcintyre    TRUE       FALSE    13  5.00
#  8:   Lattimore    TRUE       FALSE    12  4.62
#  9:     Legrand    TRUE       FALSE    11  4.23
# 10:     Legrand   FALSE       FALSE    10  3.85
# 11:      Bryant    TRUE       FALSE     9  3.46
# 12:   Lattimore   FALSE       FALSE     8  3.08
# 13:      Bryant   FALSE       FALSE     8  3.08
# 14:   Lattimore   FALSE        TRUE     7  2.69
# 15:   Patterson   FALSE        TRUE     7  2.69
# 16:   Patterson   FALSE       FALSE     4  1.54
# 17:      Bryant   FALSE        TRUE     4  1.54
# 18:    Mcintyre   FALSE        TRUE     3  1.15
# 19:     Legrand   FALSE        TRUE     2  0.77

The drawback to this measure is that it only tells you how many times a biller met the organization’s internal benchmarks. It doesn’t tell you how well they met those benchmarks, obscuring the quality of their work.

Biller Quality Rating (BQR)

A rating score might better represent the quality of a biller’s output, measuring his or her’s effectiveness and efficiency by incorporating as many individual metrics as is reasonable.

The higher the rating, the better:

The way this calculation works is that the biller’s score is penalized for:

As an example, let’s compare two billers with the following stats:

Biller 1: Claims Per Hour = 29, Rejection Rate = 4%

# Rejection Rate / Claims Per Hour
0.04 / 29
# [1] 0.00137931

Biller 2: Claims Per Hour = 37, Rejection Rate = 1.5%

# Rejection Rate / Claims Per Hour
0.015 / 37
# [1] 0.0004054054

Biller 2 has a much higher (better) score because she had a higher Claims Per Hour and a lower Rejection Rate than Biller 1.

Biller 1: Claims Per Hour = 29, Rejection Rate = 1.5%

# Rejection Rate / Claims Per Hour
0.015 / 29
# [1] 0.0005172414

Biller 2: Claims Per Hour = 37, Rejection Rate = 4%

# Rejection Rate / Claims Per Hour
0.04 / 37
# [1] 0.001081081

Comparing the three ratings shows the effects of adjusting and accounting for different variables.

While Kaila Mcintyre is in third place in the Biller Quality Index, she is in the top-ranked position in both of the Quality Ratings. Brandi Legrand is in second place across all three measures, but Dominque Patterson rises to third place in both Quality Ratings.

The changes in ranking hopefully illustrate the need for more robust productivity measurements, i.e., just because a biller didn’t meet all three of the internal benchmarks \(x\) number of times does not mean that their overall performance is not of a high caliber:

Billing Performance Score (BPS)

Monthly Rank #1

# Add the individually scaled monthly scores
billdata_cross_month <- billdata_cross |>
  filter(
    period == "month",
    biller_last != "All",
    client != "All"
  ) |>
  mutate(
    rr_scale = scale(rejrate),
    cph_scale = scale(clmphr)
  )

# Calculate the quantiles for Claims Per Hour
cph_curve <- quantile(billdata_cross_month$cph_scale, c(.95, .9, .85, .8, .75, .7, .65, .6, .55, .5))

# Recode percentile ranks into Claims Per Hour Score
billdata_cross_month$cph_score[billdata_cross_month$cph_scale >= cph_curve[1]] <- 95
billdata_cross_month$cph_score[billdata_cross_month$cph_scale < cph_curve[1] & billdata_cross_month$cph_scale >= cph_curve[2]] <- 90
billdata_cross_month$cph_score[billdata_cross_month$cph_scale < cph_curve[2] & billdata_cross_month$cph_scale >= cph_curve[3]] <- 85
billdata_cross_month$cph_score[billdata_cross_month$cph_scale < cph_curve[3] & billdata_cross_month$cph_scale >= cph_curve[4]] <- 80
billdata_cross_month$cph_score[billdata_cross_month$cph_scale < cph_curve[4] & billdata_cross_month$cph_scale >= cph_curve[5]] <- 75
billdata_cross_month$cph_score[billdata_cross_month$cph_scale < cph_curve[5] & billdata_cross_month$cph_scale >= cph_curve[6]] <- 70
billdata_cross_month$cph_score[billdata_cross_month$cph_scale < cph_curve[6] & billdata_cross_month$cph_scale >= cph_curve[7]] <- 65
billdata_cross_month$cph_score[billdata_cross_month$cph_scale < cph_curve[7] & billdata_cross_month$cph_scale >= cph_curve[8]] <- 60
billdata_cross_month$cph_score[billdata_cross_month$cph_scale < cph_curve[8] & billdata_cross_month$cph_scale >= cph_curve[9]] <- 55
billdata_cross_month$cph_score[billdata_cross_month$cph_scale < cph_curve[9] & billdata_cross_month$cph_scale >= cph_curve[10]] <- 50
billdata_cross_month$cph_score[billdata_cross_month$cph_scale < cph_curve[10]] <- 45

# Calculate the quantiles for Rejection Rate
rr_curve <- quantile(billdata_cross_month$rr_scale, c(.95, .9, .85, .8, .75, .7, .65, .6, .55, .5))

# Recode percentile ranks into Rejection Rate Score
billdata_cross_month$rr_score[billdata_cross_month$rr_scale <= rr_curve[10]] <- 95
billdata_cross_month$rr_score[billdata_cross_month$rr_scale > rr_curve[10] & billdata_cross_month$rr_scale <= rr_curve[9]] <- 90
billdata_cross_month$rr_score[billdata_cross_month$rr_scale > rr_curve[9] & billdata_cross_month$rr_scale <= rr_curve[8]] <- 85
billdata_cross_month$rr_score[billdata_cross_month$rr_scale > rr_curve[8] & billdata_cross_month$rr_scale <= rr_curve[7]] <- 80
billdata_cross_month$rr_score[billdata_cross_month$rr_scale > rr_curve[7] & billdata_cross_month$rr_scale <= rr_curve[6]] <- 75
billdata_cross_month$rr_score[billdata_cross_month$rr_scale > rr_curve[6] & billdata_cross_month$rr_scale <= rr_curve[5]] <- 70
billdata_cross_month$rr_score[billdata_cross_month$rr_scale > rr_curve[5] & billdata_cross_month$rr_scale <= rr_curve[4]] <- 65
billdata_cross_month$rr_score[billdata_cross_month$rr_scale > rr_curve[4] & billdata_cross_month$rr_scale <= rr_curve[3]] <- 60
billdata_cross_month$rr_score[billdata_cross_month$rr_scale > rr_curve[3] & billdata_cross_month$rr_scale <= rr_curve[2]] <- 55
billdata_cross_month$rr_score[billdata_cross_month$rr_scale > rr_curve[2] & billdata_cross_month$rr_scale <= rr_curve[1]] <- 50
billdata_cross_month$rr_score[billdata_cross_month$rr_scale > rr_curve[1]] <- 45

# Calculate the mean of the scores
billdata_cross_month <- billdata_cross_month |>
  mutate(
    total_score = cph_score + rr_score,
    avg_score = total_score / 2
  )

paged_table(billdata_cross_month)
billdata_month_rank <- billdata_cross_month |>
  group_by(month, biller_last) |>
  summarise(avg_score = mean(avg_score, 3)) |>
  mutate(rank = rank(desc(avg_score), ties.method = "min"))

paged_table(billdata_month_rank)

Monthly Rank #2

# Add the individually scaled monthly scores
billdata_cross_month2 <- billdata_cross |>
  filter(
    period == "month",
    biller_last != "All",
    client == "All"
  ) |>
  mutate(
    rr_scale = scale(rejrate),
    cph_scale = scale(clmphr)
  )

# Calculate the quantiles for Claims Per Hour
cph_curve2 <- quantile(billdata_cross_month2$cph_scale, c(.95, .9, .85, .8, .75, .7, .65, .6, .55, .5))

# Recode percentile ranks into Claims Per Hour Score
billdata_cross_month2$cph_score[billdata_cross_month2$cph_scale >= cph_curve2[1]] <- 95
billdata_cross_month2$cph_score[billdata_cross_month2$cph_scale < cph_curve2[1] & billdata_cross_month2$cph_scale >= cph_curve2[2]] <- 90
billdata_cross_month2$cph_score[billdata_cross_month2$cph_scale < cph_curve2[2] & billdata_cross_month2$cph_scale >= cph_curve2[3]] <- 85
billdata_cross_month2$cph_score[billdata_cross_month2$cph_scale < cph_curve2[3] & billdata_cross_month2$cph_scale >= cph_curve2[4]] <- 80
billdata_cross_month2$cph_score[billdata_cross_month2$cph_scale < cph_curve2[4] & billdata_cross_month2$cph_scale >= cph_curve2[5]] <- 75
billdata_cross_month2$cph_score[billdata_cross_month2$cph_scale < cph_curve2[5] & billdata_cross_month2$cph_scale >= cph_curve2[6]] <- 70
billdata_cross_month2$cph_score[billdata_cross_month2$cph_scale < cph_curve2[6] & billdata_cross_month2$cph_scale >= cph_curve2[7]] <- 65
billdata_cross_month2$cph_score[billdata_cross_month2$cph_scale < cph_curve2[7] & billdata_cross_month2$cph_scale >= cph_curve2[8]] <- 60
billdata_cross_month2$cph_score[billdata_cross_month2$cph_scale < cph_curve2[8] & billdata_cross_month2$cph_scale >= cph_curve2[9]] <- 55
billdata_cross_month2$cph_score[billdata_cross_month2$cph_scale < cph_curve2[9] & billdata_cross_month2$cph_scale >= cph_curve2[10]] <- 50
billdata_cross_month2$cph_score[billdata_cross_month2$cph_scale < cph_curve2[10]] <- 45

# Calculate the quantiles for Rejection Rate
rr_curve2 <- quantile(billdata_cross_month2$rr_scale, c(.95, .9, .85, .8, .75, .7, .65, .6, .55, .5))

# Recode percentile ranks into Rejection Rate Score
billdata_cross_month2$rr_score[billdata_cross_month2$rr_scale <= rr_curve2[10]] <- 95
billdata_cross_month2$rr_score[billdata_cross_month2$rr_scale > rr_curve2[10] & billdata_cross_month2$rr_scale <= rr_curve2[9]] <- 90
billdata_cross_month2$rr_score[billdata_cross_month2$rr_scale > rr_curve2[9] & billdata_cross_month2$rr_scale <= rr_curve2[8]] <- 85
billdata_cross_month2$rr_score[billdata_cross_month2$rr_scale > rr_curve2[8] & billdata_cross_month2$rr_scale <= rr_curve2[7]] <- 80
billdata_cross_month2$rr_score[billdata_cross_month2$rr_scale > rr_curve2[7] & billdata_cross_month2$rr_scale <= rr_curve2[6]] <- 75
billdata_cross_month2$rr_score[billdata_cross_month2$rr_scale > rr_curve2[6] & billdata_cross_month2$rr_scale <= rr_curve2[5]] <- 70
billdata_cross_month2$rr_score[billdata_cross_month2$rr_scale > rr_curve2[5] & billdata_cross_month2$rr_scale <= rr_curve2[4]] <- 65
billdata_cross_month2$rr_score[billdata_cross_month2$rr_scale > rr_curve2[4] & billdata_cross_month2$rr_scale <= rr_curve2[3]] <- 60
billdata_cross_month2$rr_score[billdata_cross_month2$rr_scale > rr_curve2[3] & billdata_cross_month2$rr_scale <= rr_curve2[2]] <- 55
billdata_cross_month2$rr_score[billdata_cross_month2$rr_scale > rr_curve2[2] & billdata_cross_month2$rr_scale <= rr_curve2[1]] <- 50
billdata_cross_month2$rr_score[billdata_cross_month2$rr_scale > rr_curve2[1]] <- 45

# Calculate the mean of the scores
billdata_cross_month2 <- billdata_cross_month2 |>
  mutate(
    total_score = cph_score + rr_score,
    avg_score = total_score / 2
  )

paged_table(billdata_cross_month2)
billdata_month_rank2 <- billdata_cross_month2 |>
  group_by(month, biller_last) |>
  summarise(avg_score = mean(avg_score, 3)) |>
  mutate(rank = rank(desc(avg_score), ties.method = "min"))

paged_table(billdata_month_rank2)

Potential Drawbacks

This is my simplified adaptation of what’s come to be known as a “North Star Metric” which you can read more about here. I’m using it more as a sort of “One Number to Rule Them All.” Ideally, by combining as many measurements as is reasonable, you can monitor one number instead of six. The legitimacy of the number/score can be difficult to parse if one does not take great care when creating the calculation. It might take a lot of testing to make sure the final score is accurately reflecting an individual’s performance.

For instance, the above example does not take into account:

A quality rating or score should judge an individual’s performance based solely upon circumstances within their control. Misrepresentation renders any such number meaningless.

As with any metric meant to sum up several other complex metrics, a quality score such as this should be used to quickly tell a stakeholder that either everything is alright or point to a potential issue that needs further review. The caveat is that the more complicated you make the number, the harder it will be to explain to decision makers who must understand exactly what it means.

Citations

Package Version Citation
base 4.2.1 R Core Team (2022)
distill 1.4 Dervieux et al. (2022)
grateful 0.1.11 Rodríguez-Sánchez, Jackson, and Hutchins (2022)
gt 0.6.0 Iannone, Cheng, and Schloerke (2022)
gtExtras 0.4.1 Mock (2022)
htmltools 0.5.3 Cheng et al. (2022)
janitor 2.1.0 Firke (2021)
knitr 1.39 Xie (2014); Xie (2015); Xie (2022)
rmarkdown 2.14 Xie, Allaire, and Grolemund (2018); Xie, Dervieux, and Riederer (2020); Allaire et al. (2022)
sessioninfo 1.2.2 Wickham et al. (2021)
skimr 2.1.4 Waring et al. (2022)
SmartEDA 0.3.8 Dayanand Ubrangala et al. (2021)
tidycharts 0.1.3 Biecek et al. (2022)
tidymetrics 0.0.1 Vaidyanathan and Robinson (2022)
tidytable 0.8.0 Fairbanks (2022)
tidyverse 1.3.2 Wickham et al. (2019)
xaringanExtra 0.7.0 Aden-Buie and Warkentin (2022)

Last updated on

# [1] "2022-07-20 16:11:52 EDT"

Session Info

Session Info 
session
# ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#  setting  value
#  version  R version 4.2.1 (2022-06-23 ucrt)
#  os       Windows 10 x64 (build 25158)
#  system   x86_64, mingw32
#  ui       RTerm
#  language (EN)
#  collate  English_United States.utf8
#  ctype    English_United States.utf8
#  tz       America/New_York
#  date     2022-07-20
#  pandoc   2.18 @ C:/Program Files/RStudio/bin/quarto/bin/tools/ (via rmarkdown)
# 
# ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#  package       * version    date (UTC) lib source
#  assertthat      0.2.1      2019-03-21 [1] CRAN (R 4.2.0)
#  backports       1.4.1      2021-12-13 [1] CRAN (R 4.2.0)
#  base64enc       0.1-3      2015-07-28 [1] CRAN (R 4.2.0)
#  broom           1.0.0      2022-07-01 [1] CRAN (R 4.2.1)
#  bslib           0.4.0      2022-07-16 [1] CRAN (R 4.2.1)
#  cachem          1.0.6      2021-08-19 [1] CRAN (R 4.2.0)
#  cellranger      1.1.0      2016-07-27 [1] CRAN (R 4.2.0)
#  checkmate       2.1.0      2022-04-21 [1] CRAN (R 4.2.0)
#  cli             3.3.0      2022-04-25 [1] CRAN (R 4.2.0)
#  colorspace      2.0-3      2022-02-21 [1] CRAN (R 4.2.0)
#  crayon          1.5.1      2022-03-26 [1] CRAN (R 4.2.0)
#  curl            4.3.2      2021-06-23 [1] CRAN (R 4.2.0)
#  data.table      1.14.2     2021-09-27 [1] CRAN (R 4.2.0)
#  DBI             1.1.3      2022-06-18 [1] CRAN (R 4.2.0)
#  dbplyr          2.2.1      2022-06-27 [1] CRAN (R 4.2.0)
#  digest          0.6.29     2021-12-01 [1] CRAN (R 4.2.0)
#  distill         1.4        2022-05-12 [1] CRAN (R 4.2.0)
#  downlit         0.4.2      2022-07-05 [1] CRAN (R 4.2.0)
#  dplyr         * 1.0.9      2022-04-28 [1] CRAN (R 4.2.0)
#  ellipsis        0.3.2      2021-04-29 [1] CRAN (R 4.2.0)
#  evaluate        0.15       2022-02-18 [1] CRAN (R 4.2.0)
#  fansi           1.0.3      2022-03-24 [1] CRAN (R 4.2.0)
#  fastmap         1.1.0      2021-01-25 [1] CRAN (R 4.2.0)
#  fontawesome     0.2.2      2021-07-02 [1] CRAN (R 4.2.0)
#  forcats       * 0.5.1      2021-01-27 [1] CRAN (R 4.2.0)
#  fs              1.5.2      2021-12-08 [1] CRAN (R 4.2.0)
#  gargle          1.2.0.9002 2022-06-05 [1] Github (r-lib/gargle@1e67aa0)
#  generics        0.1.3      2022-07-05 [1] CRAN (R 4.2.0)
#  GGally          2.1.2      2021-06-21 [1] CRAN (R 4.2.0)
#  ggplot2       * 3.3.6      2022-05-03 [1] CRAN (R 4.2.0)
#  glue            1.6.2      2022-02-24 [1] CRAN (R 4.2.0)
#  googledrive     2.0.0      2021-07-08 [1] CRAN (R 4.2.0)
#  googlesheets4 * 1.0.0      2021-07-21 [1] CRAN (R 4.2.0)
#  grateful      * 0.1.11     2022-05-07 [1] Github (Pakillo/grateful@ba9b003)
#  gridExtra       2.3        2017-09-09 [1] CRAN (R 4.2.1)
#  gt              0.6.0      2022-05-24 [1] CRAN (R 4.2.0)
#  gtable          0.3.0      2019-03-25 [1] CRAN (R 4.2.0)
#  gtExtras        0.4.1      2022-07-13 [1] CRAN (R 4.2.1)
#  haven           2.5.0      2022-04-15 [1] CRAN (R 4.2.0)
#  highr           0.9        2021-04-16 [1] CRAN (R 4.2.0)
#  hms             1.1.1      2021-09-26 [1] CRAN (R 4.2.0)
#  htmltools     * 0.5.3      2022-07-18 [1] CRAN (R 4.2.1)
#  htmlwidgets     1.5.4      2021-09-08 [1] CRAN (R 4.2.0)
#  httr            1.4.3      2022-05-04 [1] CRAN (R 4.2.0)
#  ISLR            1.4        2021-09-15 [1] CRAN (R 4.2.0)
#  janitor       * 2.1.0      2021-01-05 [1] CRAN (R 4.2.0)
#  jquerylib       0.1.4      2021-04-26 [1] CRAN (R 4.2.0)
#  jsonlite        1.8.0      2022-02-22 [1] CRAN (R 4.2.0)
#  knitr         * 1.39       2022-04-26 [1] CRAN (R 4.2.0)
#  lifecycle       1.0.1      2021-09-24 [1] CRAN (R 4.2.0)
#  lpSolve         5.6.15     2020-01-24 [1] CRAN (R 4.2.0)
#  lubridate     * 1.8.0      2021-10-07 [1] CRAN (R 4.2.0)
#  magrittr      * 2.0.3      2022-03-30 [1] CRAN (R 4.2.0)
#  MASS            7.3-58     2022-07-14 [1] CRAN (R 4.2.1)
#  memoise         2.0.1      2021-11-26 [1] CRAN (R 4.2.0)
#  modelr          0.1.8      2020-05-19 [1] CRAN (R 4.2.0)
#  munsell         0.5.0      2018-06-12 [1] CRAN (R 4.2.0)
#  paletteer       1.4.0      2021-07-20 [1] CRAN (R 4.2.0)
#  pillar          1.8.0      2022-07-18 [1] CRAN (R 4.2.1)
#  pkgconfig       2.0.3      2019-09-22 [1] CRAN (R 4.2.0)
#  plyr            1.8.7      2022-03-24 [1] CRAN (R 4.2.0)
#  purrr         * 0.3.4      2020-04-17 [1] CRAN (R 4.2.0)
#  R.cache         0.15.0     2021-04-30 [1] CRAN (R 4.2.0)
#  R.methodsS3     1.8.2      2022-06-13 [1] CRAN (R 4.2.0)
#  R.oo            1.25.0     2022-06-12 [1] CRAN (R 4.2.0)
#  R.utils         2.12.0     2022-06-28 [1] CRAN (R 4.2.0)
#  R6              2.5.1      2021-08-19 [1] CRAN (R 4.2.0)
#  RColorBrewer    1.1-3      2022-04-03 [1] CRAN (R 4.2.0)
#  Rcpp            1.0.9      2022-07-08 [1] CRAN (R 4.2.1)
#  readr         * 2.1.2      2022-01-30 [1] CRAN (R 4.2.0)
#  readxl          1.4.0      2022-03-28 [1] CRAN (R 4.2.0)
#  rematch2        2.1.2      2020-05-01 [1] CRAN (R 4.2.0)
#  renv            0.15.5     2022-05-26 [1] CRAN (R 4.2.0)
#  repr            1.1.4      2022-01-04 [1] CRAN (R 4.2.0)
#  reprex          2.0.1      2021-08-05 [1] CRAN (R 4.2.0)
#  reshape         0.8.9      2022-04-12 [1] CRAN (R 4.2.0)
#  rlang           1.0.4      2022-07-12 [1] CRAN (R 4.2.1)
#  rmarkdown     * 2.14       2022-04-25 [1] CRAN (R 4.2.0)
#  rstudioapi      0.13       2020-11-12 [1] CRAN (R 4.2.0)
#  rsvg            2.3.1      2022-04-20 [1] CRAN (R 4.2.0)
#  rvest           1.0.2      2021-10-16 [1] CRAN (R 4.2.0)
#  sampling        2.9        2021-01-13 [1] CRAN (R 4.2.0)
#  sass            0.4.2      2022-07-16 [1] CRAN (R 4.2.1)
#  scales          1.2.0      2022-04-13 [1] CRAN (R 4.2.0)
#  sessioninfo     1.2.2      2021-12-06 [1] CRAN (R 4.2.0)
#  skimr         * 2.1.4      2022-06-14 [1] Github (ropensci/skimr@413ad47)
#  SmartEDA        0.3.8      2021-06-05 [1] CRAN (R 4.2.0)
#  snakecase       0.11.0     2019-05-25 [1] CRAN (R 4.2.0)
#  stringi         1.7.8      2022-07-11 [1] CRAN (R 4.2.1)
#  stringr       * 1.4.0      2019-02-10 [1] CRAN (R 4.2.0)
#  styler          1.7.0      2022-03-13 [1] CRAN (R 4.2.0)
#  tibble        * 3.1.7      2022-05-03 [1] CRAN (R 4.2.0)
#  tidycharts    * 0.1.3      2022-01-18 [1] CRAN (R 4.2.1)
#  tidymetrics   * 0.0.1      2022-05-07 [1] Github (datacamp/tidymetrics@47f157a)
#  tidyr         * 1.2.0      2022-02-01 [1] CRAN (R 4.2.0)
#  tidyselect      1.1.2      2022-02-21 [1] CRAN (R 4.2.0)
#  tidytable     * 0.8.0      2022-06-14 [1] Github (markfairbanks/tidytable@13c9b1d)
#  tidyverse     * 1.3.2      2022-07-18 [1] CRAN (R 4.2.1)
#  tzdb            0.3.0      2022-03-28 [1] CRAN (R 4.2.0)
#  utf8            1.2.2      2021-07-24 [1] CRAN (R 4.2.0)
#  uuid            1.1-0      2022-04-19 [1] CRAN (R 4.2.0)
#  vctrs           0.4.1      2022-04-13 [1] CRAN (R 4.2.0)
#  withr           2.5.0      2022-03-03 [1] CRAN (R 4.2.0)
#  xaringanExtra   0.7.0      2022-07-16 [1] CRAN (R 4.2.1)
#  xfun            0.31       2022-05-10 [1] CRAN (R 4.2.0)
#  xml2            1.3.3      2021-11-30 [1] CRAN (R 4.2.0)
#  yaml            2.3.5      2022-02-21 [1] CRAN (R 4.2.0)
# 
#  [1] C:/Users/andyb/AppData/Local/R/win-library/4.2
#  [2] C:/Program Files/R/R-4.2.1/library
# 
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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Biecek, Przemysław, Piotr Piątyszek, Kinga Ułasik, and Bartosz Sawicki. 2022. Tidycharts: Generate Tidy Charts Inspired by ’IBCS’. https://CRAN.R-project.org/package=tidycharts.
Cheng, Joe, Carson Sievert, Barret Schloerke, Winston Chang, Yihui Xie, and Jeff Allen. 2022. Htmltools: Tools for HTML. https://CRAN.R-project.org/package=htmltools.
Dayanand Ubrangala, Kiran R, Ravi Prasad Kondapalli, and Sayan Putatunda. 2021. SmartEDA: Summarize and Explore the Data. https://CRAN.R-project.org/package=SmartEDA.
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———. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. https://yihui.org/knitr/.
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References

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Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/andrewallenbruce, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Bruce (2022, April 6). Andrew Bruce: R Cookbook: Medical Billing. Retrieved from https://andrewbruce.netlify.app/posts/r-cookbook-medical-billing/

BibTeX citation

@misc{bruce2022r,
  author = {Bruce, Andrew},
  title = {Andrew Bruce: R Cookbook: Medical Billing},
  url = {https://andrewbruce.netlify.app/posts/r-cookbook-medical-billing/},
  year = {2022}
}

Andrew A. Bruce - - made with , and {distill}