R Cookbook: Aging of Accounts

medical-billing medical-coding data-analytics

Personal R code cookbook for common RCM Aging analyses.

Andrew Bruce https://andrewbruce.netlify.app
2022-06-22

Introduction

This post is simply a sandbox/cookbook of R code I’m exploring for common healthcare aging of accounts-related analyses.

Packages

library(tidyverse)
library(tidytable)
library(highcharter)
library(rmarkdown)
library(lubridate)
library(htmltools)
library(htmlwidgets)
library(googlesheets4)
library(splot)

Data

# Sheet is public and shareable
gs4_deauth()

# Google Sheet ID
id_aging <- "1Td3_6sYOEVwSOdaWdBeUl8yLUp7ztFu8tesZ6LcLlv4"

# Read in Google Sheet
google_aging <- read_sheet(ss = id_aging, sheet = "Sheet1")

# Convert Date of Service column to date object
google_aging$dos <- as.Date(google_aging$dos, "%m/%d/%Y")

# Print head of data
paged_table(google_aging)
# Add Age Column
google_aging <- google_aging |>
  mutate(age = days(Sys.Date()) - days(dos))

google_aging$age <- as.numeric(google_aging$age, "hours")

google_aging <- google_aging |>
  mutate(age = age / 24)

# Add Bucket Column
google_aging <- google_aging |>
  mutate(bucket = case_when(
    age < 31 ~ "0-30",
    age >= 31 & age < 61 ~ "31-60",
    age >= 61 & age < 91 ~ "61-90",
    age >= 91 & age < 121 ~ "91-120",
    age >= 121 & age < 151 ~ "121-150",
    age >= 151 & age < 181 ~ "151-180",
    age >= 181 ~ "181+",
    TRUE ~ "UNKNOWN"
  ))

google_aging$bucket <- factor(google_aging$bucket,
  levels = c(
    "0-30", "31-60", "61-90",
    "91-120", "121-150", "151-180",
    "181+", "UNKNOWN"
  ),
  ordered = is.ordered(google_aging$bucket)
)

# Print head of data
paged_table(google_aging)
# Add 'filelimit' Column
google_aging <- google_aging |>
  mutate(filelimit = case_when(
    payer == "AETNA" ~ "180",
    payer == "Ambetter" ~ "180",
    payer == "Blue Cross" ~ "365",
    payer == "CIGNA" ~ "90",
    payer == "Coventry" ~ "180",
    payer == "Humana" ~ "90",
    payer == "Medicaid" ~ "180",
    payer == "Medicare" ~ "365",
    payer == "Meritain" ~ "90",
    payer == "UHC" ~ "90",
    payer == "Patient" ~ "0",
    TRUE ~ "Other"
  ))

## Convert 'filelimit' Column to Numeric
google_aging$filelimit <- as.numeric(google_aging$filelimit)

## Add 'filelimdate' Column - Age of Claim
google_aging <- google_aging |>
  mutate(filelimdate = case_when(
    filelimit > 0 ~ as.Date(dos) + days(filelimit),
    TRUE ~ as.Date(dos)
  ))

## Add 'filelimdays' Column - Age of Claim
google_aging <- google_aging |>
  mutate(filelimdays = case_when(
    filelimit > 0 ~ filelimit - age,
    TRUE ~ 5000 # dummy variable for Patient Class
  ))

# Print head of data
paged_table(google_aging)

Visualizations

Bar/Column Charts

Code 
google_aging |>
  group_by(bucket) |>
  summarise(
    total = sum(amt),
    encounters = n()
  ) |>
  hchart(
    "column",
    hcaes(bucket, round(encounters, 0), color = bucket)
  ) |>
  hc_yAxis(
    gridLineWidth = 0,
    labels = list(style = list(color = "#000000")),
    title = list(text = " ", style = list(color = "#000000"))
  ) |>
  hc_xAxis(
    labels = list(style = list(color = "#000000")),
    title = list(text = " "),
    lineWidth = 0,
    tickWidth = 0
  ) |>
  hc_title(text = "Number of Encounters by Bucket") |>
  hc_add_theme(hc_theme_smpl()) |>
  hc_tooltip(
    useHTML = TRUE,
    crosshairs = TRUE,
    borderWidth = 1,
    sort = TRUE
  ) |>
  hc_plotOptions(
    column = list(
      dataLabels = list(
        valueDecimals = 0,
        enabled = TRUE
      )
    )
  ) |>
  hc_size(height = 400, width = 800)
Code 
google_aging |>
  group_by(bucket, payer) |>
  summarise(
    total = sum(amt),
    encounters = n()
  ) |>
  hchart(
    "column",
    hcaes(bucket, round(encounters, 0), group = payer, color = payer)
  ) |>
  hc_yAxis(
    gridLineWidth = 0,
    labels = list(style = list(color = "#000000")),
    title = list(text = " ", style = list(color = "#000000"))
  ) |>
  hc_xAxis(
    labels = list(style = list(color = "#000000")),
    title = list(text = " "),
    lineWidth = 0,
    tickWidth = 0
  ) |>
  hc_title(text = "Number of Encounters Per Payer by Bucket") |>
  hc_add_theme(hc_theme_smpl()) |>
  hc_tooltip(
    useHTML = TRUE,
    crosshairs = TRUE,
    borderWidth = 1,
    sort = TRUE
  ) |>
  hc_plotOptions(
    column = list(
      dataLabels = list(
        valueDecimals = 0,
        enabled = TRUE
      )
    )
  ) |>
  hc_size(height = 400, width = 800)
Code 
google_aging |>
  group_by(payer, bucket) |>
  summarise(
    total = sum(amt),
    encounters = n()
  ) |>
  hchart(
    "bar",
    hcaes(bucket, round(total, 0), group = payer)
  ) |>
  hc_yAxis(
    gridLineWidth = 0,
    labels = list(style = list(color = "#000000")),
    title = list(text = " ", style = list(color = "#000000"))
  ) |>
  hc_xAxis(
    labels = list(style = list(color = "#000000")),
    title = list(text = " "),
    lineWidth = 0,
    tickWidth = 0
  ) |>
  hc_title(text = "Dollar Amount by Bucket Per Payer") |>
  hc_add_theme(hc_theme_smpl()) |>
  hc_tooltip(
    useHTML = TRUE,
    crosshairs = TRUE,
    borderWidth = 1,
    sort = TRUE
  ) |>
  hc_plotOptions(
    column = list(
      dataLabels = list(
        valueDecimals = 0,
        enabled = TRUE
      )
    )
  ) |>
  hc_size(height = 600, width = 800)

Linear Regression Plots

aging_bailey <- google_aging |>
  filter(provider == "Bailey")

splot(amt ~ age * payer * referring_phys, aging_bailey)

Code 
hchart(
  aging_bailey,
  "scatter",
  hcaes(x = age, y = amt, group = payer),
  regression = TRUE
) |>
  hc_add_theme(hc_theme_smpl()) |>
  hc_add_dependency("plugins/highcharts-regression.js") |>
  hc_size(height = 1000)
Code 
hchart(google_aging, "scatter",
  hcaes(x = age, y = amt, group = provider),
  regression = TRUE
) |>
  hc_add_theme(hc_theme_smpl()) |>
  hc_add_dependency("plugins/highcharts-regression.js") |>
  hc_size(height = 1000, width = 800)

Relationship Visualizations

refer2prov <- google_aging |>
  dplyr::group_by(referring_phys, provider) |>
  dplyr::summarise(count = n()) |>
  dplyr::arrange(desc(count)) |>
  ungroup() |>
  rename(from = referring_phys, to = provider, weight = count)
refer2prov
# # A tibble: 65 × 3
#    from        to      weight
#    <chr>       <chr>    <int>
#  1 Ramirez     James      177
#  2 Ramirez     Forrest    131
#  3 Ramirez     Timothy    117
#  4 Blankenship James       76
#  5 Burroughs   James       75
#  6 Ramirez     Bailey      70
#  7 Frier       James       69
#  8 Page        James       66
#  9 Burroughs   Forrest     65
# 10 Ramirez     Allen       64
# # … with 55 more rows
# # ℹ Use `print(n = ...)` to see more rows
prov2payer <- google_aging |>
  dplyr::group_by(provider, payer) |>
  dplyr::summarise(count = n()) |>
  dplyr::arrange(desc(count)) |>
  ungroup() |>
  rename(from = provider, to = payer, weight = count)
prov2payer
# # A tibble: 30 × 3
#    from    to         weight
#    <chr>   <chr>       <int>
#  1 James   Patient       308
#  2 Forrest Patient       268
#  3 Timothy Patient       206
#  4 James   Blue Cross    170
#  5 James   Medicare      156
#  6 Forrest Blue Cross    133
#  7 Bailey  Patient       128
#  8 Forrest Medicare      126
#  9 Allen   Patient       113
# 10 Timothy Blue Cross    113
# # … with 20 more rows
# # ℹ Use `print(n = ...)` to see more rows
ref2prov2pay <- rbind(refer2prov, prov2payer)
ref2prov2pay
# # A tibble: 95 × 3
#    from        to      weight
#    <chr>       <chr>    <int>
#  1 Ramirez     James      177
#  2 Ramirez     Forrest    131
#  3 Ramirez     Timothy    117
#  4 Blankenship James       76
#  5 Burroughs   James       75
#  6 Ramirez     Bailey      70
#  7 Frier       James       69
#  8 Page        James       66
#  9 Burroughs   Forrest     65
# 10 Ramirez     Allen       64
# # … with 85 more rows
# # ℹ Use `print(n = ...)` to see more rows

Sankey Diagram

Code 
hchart(
  ref2prov2pay, "sankey",
  hcaes(from = from, to = to, weight = weight)
) |>
  hc_size(height = 600, width = 800)

Dependency Wheel

Code 
hchart(
  ref2prov2pay, "dependencywheel",
  hcaes(from = from, to = to, weight = weight)
) |>
  hc_size(height = 800, width = 800)

Vtree Visualizations

library(vtree)

vtree(
  google_aging,
  "provider payer",
  horiz = FALSE,
  title = "Encounters",
  # imageheight = "15in",
  # imagewidth = "8in",
  height = "50%",
  width = "100%",
  rounded = FALSE,
  showvarnames = FALSE,
  pngknit = FALSE,
  sameline = FALSE,
  Venn = FALSE,
  seq = TRUE,
  pattern = TRUE
)
vtree(
  google_aging,
  "referring_phys",
  horiz = FALSE,
  title = "Encounters",
  height = "50%",
  width = "100%",
  rounded = FALSE,
  showvarnames = FALSE,
  pngknit = FALSE,
  pattern = FALSE
)

Misc Visualizations

Sunburst Chart

with {sunburstR}

Code 
aging_df <- data.frame(
  level1 = rep(c("Primary", "Secondary"), each = 7),
  level2 = rep(c(
    "Cigna",
    "BCBS",
    "Medicare",
    "Aetna",
    "Humana",
    "UHC",
    "Medicaid"
  ), 2),
  size = c(
    101586.44, 813932.10, 244682.06,
    315442.09, 338892.56, 692951.00,
    172394.44, 30869.21, 75555.29,
    12601.41, 39003.59, 27713.18,
    14384.15, 222480.09
  ),
  stringsAsFactors = FALSE
)
aging_df
#       level1   level2      size
# 1    Primary    Cigna 101586.44
# 2    Primary     BCBS 813932.10
# 3    Primary Medicare 244682.06
# 4    Primary    Aetna 315442.09
# 5    Primary   Humana 338892.56
# 6    Primary      UHC 692951.00
# 7    Primary Medicaid 172394.44
# 8  Secondary    Cigna  30869.21
# 9  Secondary     BCBS  75555.29
# 10 Secondary Medicare  12601.41
# 11 Secondary    Aetna  39003.59
# 12 Secondary   Humana  27713.18
# 13 Secondary      UHC  14384.15
# 14 Secondary Medicaid 222480.09
library(d3r)

tree <- d3_nest(aging_df, value_cols = "size")

str(tree)
#  'json' chr "{\"children\":[{\"name\":\"Primary\",\"children\":[{\"name\":\"Cigna\",\"size\":101586.44,\"colname\":\"level2\"| __truncated__
Code 
library(sunburstR)

sb1 <- sunburst(tree, width = "100%", height = 600)

sb3 <- sund2b(
  tree,
  width = "100%",
  height = 600,
  rootLabel = "Aging",
  showLabels = T,
  breadcrumbs = sund2bBreadcrumb(enabled = T),
  colors = list(range = RColorBrewer::brewer.pal(9, "Paired"))
)

div(
  style = "display: flex; align-items:center;",
  div(style = "width:50%; border:1px solid #ccc;", sb1),
  div(style = "width:50%; border:1px solid #ccc;", sb3)
)
Legend

Drilldown Piechart

with {echarts4r}

Code 
aging_df2 <- data.frame(
  parents = c(
    "",
    "Primary", "Primary",
    "Primary", "Primary",
    "Primary", "Primary", "Primary",
    "Secondary", "Secondary",
    "Secondary", "Secondary",
    "Secondary", "Secondary", "Secondary",
    "Everything", "Everything"
  ),
  labels = c(
    "Everything",
    "Cigna", "BCBS", "Medicare",
    "Aetna", "Humana", "UHC",
    "Medicaid", "Cigna", "BCBS",
    "Medicare", "Aetna", "Humana",
    "UHC", "Medicaid",
    "Primary", "Secondary"
  ),
  value = c(
    0,
    101586.44, 813932.10, 244682.06,
    315442.09, 338892.56, 692951.00,
    172394.44, 30869.21, 75555.29,
    12601.41, 39003.59, 27713.18,
    14384.15, 222480.09, 2679880.7,
    422606.9
  )
)

aging_df2
#       parents     labels      value
# 1             Everything       0.00
# 2     Primary      Cigna  101586.44
# 3     Primary       BCBS  813932.10
# 4     Primary   Medicare  244682.06
# 5     Primary      Aetna  315442.09
# 6     Primary     Humana  338892.56
# 7     Primary        UHC  692951.00
# 8     Primary   Medicaid  172394.44
# 9   Secondary      Cigna   30869.21
# 10  Secondary       BCBS   75555.29
# 11  Secondary   Medicare   12601.41
# 12  Secondary      Aetna   39003.59
# 13  Secondary     Humana   27713.18
# 14  Secondary        UHC   14384.15
# 15  Secondary   Medicaid  222480.09
# 16 Everything    Primary 2679880.70
# 17 Everything  Secondary  422606.90
# create a tree object
etree <- data.tree::FromDataFrameNetwork(aging_df2)

etree
#               levelName
# 1                      
# 2   °--Everything      
# 3       ¦--Primary     
# 4       ¦   ¦--Cigna   
# 5       ¦   ¦--BCBS    
# 6       ¦   ¦--Medicare
# 7       ¦   ¦--Aetna   
# 8       ¦   ¦--Humana  
# 9       ¦   ¦--UHC     
# 10      ¦   °--Medicaid
# 11      °--Secondary   
# 12          ¦--Cigna   
# 13          ¦--BCBS    
# 14          ¦--Medicare
# 15          ¦--Aetna   
# 16          ¦--Humana  
# 17          ¦--UHC     
# 18          °--Medicaid
Code 
library(echarts4r)

etree |>
  e_charts(
    width = "100%",
    height = 700
  ) |>
  e_sunburst() |>
  e_tooltip(style = "currency", locale = "US") |>
  e_title("Primary & Secondary Aging") |>
  e_theme("dark")

Sankey Diagram

in {echarts4r}

Code 
aging_df |>
  e_charts() |>
  e_sankey(level1, level2, size) |>
  e_title("Aging & Payer Mix") |>
  e_theme("macarons2") |>
  e_tooltip()

Citations

Package Version Citation
base 4.2.1 R Core Team (2022)
d3r 1.0.0 Bostock, Russell, et al. (2021)
data.tree 1.0.0 Glur (2020)
distill 1.4 Dervieux et al. (2022)
echarts4r 0.4.4 Coene (2022)
grateful 0.1.11 Rodríguez-Sánchez, Jackson, and Hutchins (2022)
highcharter 0.9.4 Kunst (2022)
htmltools 0.5.2 Cheng et al. (2021)
htmlwidgets 1.5.4 Vaidyanathan et al. (2021)
knitr 1.39 Xie (2014); Xie (2015); Xie (2022)
RColorBrewer 1.1.3 Neuwirth (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)
splot 0.5.2 Iserman (2022)
sunburstR 2.1.6 Bostock, Rodden, et al. (2021)
tidytable 0.8.0 Fairbanks (2022)
tidyverse 1.3.1 Wickham et al. (2019)
vtree 5.4.6 frame) (2021)
xaringanExtra 0.7.0 Aden-Buie and Warkentin (2022)

Last updated on

# [1] "2022-07-19 12:14:07 EDT"

Session Info

sessioninfo::session_info()
# ─ 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     2022-07-19
#  pandoc   2.17.1.1 @ C:/Program Files/RStudio/bin/quarto/bin/ (via rmarkdown)
# 
# ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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#  [1] C:/Users/andyb/AppData/Local/R/win-library/4.2
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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, June 22). Andrew Bruce: R Cookbook: Aging of Accounts. Retrieved from https://andrewbruce.netlify.app/posts/r-cookbook-aging/

BibTeX citation

@misc{bruce2022r,
  author = {Bruce, Andrew},
  title = {Andrew Bruce: R Cookbook: Aging of Accounts},
  url = {https://andrewbruce.netlify.app/posts/r-cookbook-aging/},
  year = {2022}
}

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