The role of healthcare analytics

The primary objective of healthcare analytics is to seek benefits for health administrators, organizations, and patients, most importantly by enhancing patient experiences and improving health outcomes. The healthcare industry generates vast amounts of data, primarily from electronic medical records (EMH) and administrative data.

EMH data aims to improve the accuracy of diagnoses and treatment plans while enhancing overall care quality by making patients’ health histories readily accessible to authorized providers. In contrast, administrative data encompasses patient interaction details, such as diagnoses and hospital readmissions, which can be analyzed to evaluate healthcare delivery, advantages, disadvantages, and cost-effectiveness.

Using GitHub Copilot in RStudio with a health dataset

In this first post in a series of two, I will introduce a complex healthcare data set and outline the problems I propose to solve using tidyverse and GitHub Copilot, which will facilitate nuanced argument tuning in tidyverse functions and accelerate our data analysis process.

Before we proceed, a few words of caution regarding Copilot:

1. We will use it on a task-by-task basis rather than for the entire workflow of data exploration and hypothesis investigation. This approach allows us to leverage Copilot for challenging data wrangling and cleaning tasks, saving time and enabling deeper analytical thinking. However, it is crucial to verify the code output from any AI pair programmer—always run the code to ensure it produces the desired results and manually triangulate to confirm accuracy.

2. On the important topic of data sensitivity and the use of Copilot, take a look at some useful discussions on Posit forums – here and here. In general, it is a good idea to consult your IT teams and follow any organizational guidelines around using AI tools, especially when it concerns sensitive data.

Diabetes data from UCI

# --------------------------------------------- #
# Load necessary libraries
# --------------------------------------------- #
library(dplyr)
library(ggplot2)
library(tidyr)
library(kableExtra)
# --------------------------------------------- #
# Read in the data, show example row
# --------------------------------------------- #
D <- read.csv("diabetes_data.csv", sep = ",")

row_ex <- t(D[47, c(-1, -2, -3, -4)])
row_ex <- data.frame("Fields" = rownames(row_ex), "Values" = row_ex[, 1], row.names = NULL)

knitr::kable(
  row_ex,
  caption = "An example data row {#tbl-ex}",
  booktabs = TRUE
) %>%
  kable_styling(full_width = F) %>%
  row_spec(c(44:46), background = "lightgray")

# --------------------------------------------- #
# Transformations --> admission_type_id, diag_1, A1c, readmitted
# --------------------------------------------- #

D <- D %>%
  mutate(
    admission_type = case_when(
      admission_type_id == 1 ~ "Emergency",
      admission_type_id == 2 ~ "Urgent",
      admission_type_id == 3 ~ "Elective",
      admission_type_id == 4 ~ "Newborn",
      admission_type_id == 5 ~ "Not Available",
      admission_type_id == 6 ~ "NULL",
      admission_type_id == 7 ~ "Trauma Center",
      admission_type_id == 8 ~ "Not Mapped"
    ), primary_diag = case_when(
      diag_1 %in% c(390:459, 785) ~ "Circulatory",
      diag_1 %in% c(460:519, 786) ~ "Respiratory",
      diag_1 %in% c(520:579, 787) ~ "Digestive",
      diag_1 %in% c(580:629, 788) ~ "Genitourinary",
      diag_1 %in% c(630:679) ~ "Pregnancy",
      diag_1 %in% c(680:709, 782) ~ "Skin",
      diag_1 %in% c(710:739) ~ "Musculoskeletal",
      diag_1 %in% c(740:759) ~ "Congenital",
      diag_1 %in% c(800:999) ~ "Injury",
      grepl("^250", diag_1) ~ "Diabetes",
      is.na(diag_1) ~ "Missing",
      TRUE ~ "Other"
    ), a1c = ifelse(A1Cresult == "None", "not measured", "measured"),
    reAdmit = ifelse(readmitted == "<30", "early readmit", "no/late readmit")
  )

# --------------------------------------------- #
# Testing the Copilot output for diagnoses
# --------------------------------------------- #

testDiag <- D %>%
  subset(primary_diag == "Respiratory") %>%
  group_by(diag_1, primary_diag) %>%
  summarise(n = n())
result <- testDiag %>%
  group_by(primary_diag) %>%
  summarise(diag_1 = paste(diag_1, collapse = ", ")) %>%
  ungroup()

knitr::kable(
  # list(testDiag[1:21,], testDiag[22:42,]),
  result,
  caption = "Testing Copilot output - ICD9 codes for a respiratory primary diagnosis {#tbl-val}",
  booktabs = TRUE
)

# --------------------------------------------- #
# 1) Missing value vs NULL for admission type
# --------------------------------------------- #
p <- D %>%
  group_by(admission_type, age) %>%
  summarise(n = n()) %>%
  mutate(freq = n / sum(n))

ggplot(p, aes(x = age, y = admission_type)) +
  geom_tile(aes(fill = n)) +
  scale_fill_gradient(low = "white", high = "blue", labels = function(n) scales::comma(abs(n))) +
  labs(title = "Plot 1: Admission type vs Age - examining missing values") +
  theme(plot.title = element_text(size = 14, color = "black", hjust = 0.5)) +
  geom_text(aes(label = scales::comma(n)), size = 3, color = "gray")

# --------------------------------------------- #
# 2) Missing values for diagnoses
# --------------------------------------------- #

missing_diag <- D %>%
  select(diag_1, diag_2, diag_3) %>%
  summarise(across(everything(), ~ sum(. == "?"))) %>%
  pivot_longer(cols = everything(), names_to = "Diagnosis", values_to = "Missing") %>%
  mutate(Diagnosis = factor(Diagnosis, levels = c("diag_1", "diag_2", "diag_3")))

ggplot(missing_diag, aes(x = Diagnosis, y = Missing)) +
  geom_bar(stat = "identity", fill = "#56B4E9", color = "black") +
  labs(x = "", y = "Number of Missing Values") +
  theme_minimal() +
  labs(title = "Plot 2: Diagnoses - examining missing values") +
  theme(plot.title = element_text(size = 14, color = "black", hjust = 0.5)) +
  scale_y_continuous(labels = function(n) scales::comma(abs(n)))

Conclusion and a glimpse into part 2

Healthcare data holds immense potential to enhance patient outcomes, making the ability to navigate complex datasets an invaluable skill for the future. In this post, we introduced a diabetes patient encounter data set, demonstrating how to preprocess it for analysis and highlighting key response variables and covariates that will be explored in part . While we occasionally leveraged Copilot for assistance, precise prompts were essential for effective results.

Thus, in the upcoming part , we will delve into three critical response variables (see Table 1): early readmission, specifically investigating the factors influencing readmission within days of discharge; diabetes medication prescriptions during hospital encounters; and any change in medications. Our analysis will prioritize correlation over causation, continuing to use ggplot2 and dplyr to extract meaningful insights from the data.

We will closely examine the following relationships:

1. A1c test and early readmission

2. A1c test and medication prescription

3. A1c test and medication changes

Here, the A1c test is defined as a binary indicator of whether this vital assessment was conducted. This analysis is pivotal for understanding patient management and hospital readmission, ultimately contributing to improved patient care outcomes. Stay tuned for more!

Some more tips on Copilot in RStudio

1. After Copilot is running in your RStudio, take another look at Posit’s guide, especially where they describe the most effective way to use it while working within RStudio. For example:

Code suggestions are typically most useful when applied to a well-scoped and specific problem. When trying to solve larger problems or write longer functions, it is best to break the problem down into smaller pieces and use Copilot and your own expertise to generate code for each chunk. Similar to how a chef might use a recipe to cook each dish that makes up a larger meal, Copilot can be used to generate code for smaller pieces of a larger problem.

2. When starting to use Copilot for the first time within RStudio, try asking it simpler questions as prompts, to get used to its autocompletions, e.g.,

• # summarize the data
• # plot glucose vs bmi in a scatterplot

3. You can also begin writing a piece of code and allow Copilot to finish it via autocomplete, e.g.,

About the Author

Vidisha writes: “I am a statistician and data science professional. For practitioners like me, ‘tidyverse’ is a household word. I’ve used it for complex data wrangling, visualization, and advanced analytics work, especially where I needed clarity on how to leverage my data so that it allows for story-telling that informs business decisions, recasting my work into actionable insights. I recently began exploring GPT-4o mini and GitHub Copilot to help me speed up my workflows, especially within Healthcare Analytics. I am excited to share this post series to help readers understand the role of RStudio and AI in Healthcare Analytics!”