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hcruR

R-CMD-check Codecov test coverage GitHub version License: MIT Lifecycle: experimental

hcruR is an R package to help health economists and RWE analysts estimate and compare healthcare resource utilization (HCRU) from observational healthcare data, such as claims or electronic health records.

🚀 Features

đź“Ą Installation

You can install the development version of hcruR from GitHub with:

# Install from GitHub (after you upload the repo)
# install.packages("devtools")
devtools::install_github("mumbarkar/hcruR")

# install.packages("pak")
pak::pak("mumbarkar/hcruR")

🧪 Example Usage

This is a basic example which shows you how to solve a common problem:

This is a basic example which shows you how to solve a common problem:

# Load library
library(hcruR)

## Generate HCRU summary using dplyr (this can be used for create HCRU plots)

# Load sample data
data(hcru_sample_data)
data <- hcru_sample_data
head(hcru_sample_data)

# Estimate HCRU
hcru_summary <- estimate_hcru(data,
                          cohort_col = "cohort",
                          patient_id_col = "patient_id",
                          admit_col = "admission_date",
                          discharge_col = "discharge_date",
                          index_col = "index_date",
                          visit_col = "visit_date",
                          encounter_id_col = "encounter_id",
                          setting_col = "care_setting",
                          cost_col = "cost_usd",
                          readmission_col = "readmission",
                          time_window_col = "period",
                          los_col = "length_of_stay",
                          custom_var_list = NULL,
                          pre_days = 180,
                          post_days = 365,
                          readmission_days_rule = 30,
                          group_var_main = "cohort",
                          group_var_by = "care_setting",
                          test = NULL,
                          timeline = "Pre",
                          gt_output = FALSE)

hcru_summary

## Generate HCRU summary using gtsummary (a publication ready output) 

# Estimate HCRU
hcru_summary_gt <- estimate_hcru(data,
                          cohort_col = "cohort",
                          patient_id_col = "patient_id",
                          admit_col = "admission_date",
                          discharge_col = "discharge_date",
                          index_col = "index_date",
                          visit_col = "visit_date",
                          encounter_id_col = "encounter_id",
                          setting_col = "care_setting",
                          cost_col = "cost_usd",
                          readmission_col = "readmission",
                          time_window_col = "period",
                          los_col = "length_of_stay",
                          custom_var_list = NULL,
                          pre_days = 180,
                          post_days = 365,
                          readmission_days_rule = 30,
                          group_var_main = "cohort",
                          group_var_by = "care_setting",
                          test = NULL,
                          timeline = "Pre",
                          gt_output = TRUE)

hcru_summary_gt

## Generate the HCRU plot for average visits by cohort and time-line
# Calculate the average visits
sum_df1 <- hcru_summary$`Summary by settings using dplyr` |>
    dplyr::group_by(
      .data[["time_window"]], 
      .data[["cohort"]], 
      .data[["care_setting"]]) |>
    dplyr::summarise(
      AVG_VISIT = mean(.data[["Visits"]], na.rm = TRUE), .groups = "drop")

# Load the plot_hcru function
p1 <- plot_hcru(
  summary_df = sum_df1,
  x_var = "time_window",
  y_var = "AVG_VISIT",
  cohort_col = "cohort",
  facet_var = "care_setting",
  facet_var_n = 3,
  title = "Average visits by domain and cohort",
  x_label = "Healthcare Setting (Domain)",
  y_label = "Average visit",
  fill_label = "Cohort"
)

p1


## Generate HCRU plot for average cost by cohort and timeline
# Calculate the total cost
df2 <- hcru_summary$`Summary by settings using dplyr` |>
    dplyr::group_by(
      .data[["time_window"]], 
      .data[["cohort"]], 
      .data[["care_setting"]]) |>
    dplyr::summarise(
      AVG_COST = sum(.data[["Cost"]], na.rm = TRUE), .groups = "drop")

p2 <- plot_hcru(
  summary_df = df2,
  x_var = "time_window",
  y_var = "AVG_COST",
  cohort_col = "cohort",
  facet_var = "care_setting",
  facet_var_n = 3,
  title = "Average cost by domain and cohort",
  x_label = "Healthcare Setting (Domain)",
  y_label = "Average cost",
  fill_label = "Cohort"
)

p2

🧾 Function Reference

estimate_hcru() estimates of healthcare resource utilization (HCRU) from electronic health record data across various care settings (e.g., IP, OP, ED/ER). It provides descriptive summaries of patient counts, encounters, costs, length of stay, and readmission rates for pre- and post-index periods

Arguments

Argument Type Description
data data.frame Input claims dataset
cohort_col character Column name for cohort group
patient_id_col character Column name for patient ID
admit_col character Admission/start date column
discharge_col character Discharge/end date column
index_col character Index or anchor date for each patient
visit_col character Visit or claim date
encounter_id_col character Encounter or claim ID
setting_col character Setting type (e.g., “IP”, “OP”, “ED”)
cost_col character Column for cost data
readmission_col character Readmission indicator column
time_window_col character “Pre”/“Post” period column
los_col character Length of stay column
custom_var_list character Additional user-defined metrics (optional)
pre_days numeric Days before index date (default = 180)
post_days numeric Days after index date (default = 365)
readmission_days_rule numeric Max days for qualifying readmission (default = 30)
group_var_main character Main grouping variable (default = “cohort”)
group_var_by character Secondary grouping variable (e.g., “care_setting”)
test list Named list of tests for continuous vars (e.g., list(cost = "wilcox.test"))
timeline character Time window label (e.g., “Pre”, “Post”)
gt_output logical Whether to return a formatted gtsummary output (default = TRUE)
... ... Additional arguments for gtsummary::tbl_summary() if gt_output = TRUE
return_type character Type of output to return: “dplyr” for dplyr summary, “gtsummary” for gtsummary output (default = “dplyr”)

plot_hcru() provides the visualization of the events of the settings/domains grouped by cohort and time window.

Arguments:

Argument Type Description
summary_df dataframe Output from estimate_hcru() function.
x_var character Column name to plot on the x-axis (default "period").
y_var character Column name to plot on the y-axis (default "Cost").
cohort_col character Name of the column identifying cohorts (default "cohort").
facet_var character Column to generate subplots for (default "care_setting").
facet_var_n numeric Number of columns in the facet grid (default 3).
title character Title of the plot.
x_lable character Label for the x-axis.
y_lable character Label for the y-axis.
fill_lable character Label for the fill legend.

đź“Š Sample Data

This package includes a demo datasets for easy testing:

head(hcru_sample_data)

đź“š Vignette

Run the following to access the full walkthrough:

vignette("hcru-analysis", package = "hcruR")

🔬 Use Cases

🛠️ Development

To contribute:

git clone https://github.com/mumbarkar/hcruR.git
cd hcruR

đź“ś License

This package is licensed under the MIT License.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.