Healthcare Quality Metrics – Example Data Cleaning and Visualization

claims <- data.frame(
  Patient_ID = 1:10,
  Age = c(45, 34, 66, 50, 72, 55, 48, 39, 60, 52),
  Readmitted_30Days = c(1, 0, 1, 0, 0, 1, 0, 1, 0, 0)
)

claims_clean <- claims |> distinct()
claims_clean

##    Patient_ID Age Readmitted_30Days
## 1           1  45                 1
## 2           2  34                 0
## 3           3  66                 1
## 4           4  50                 0
## 5           5  72                 0
## 6           6  55                 1
## 7           7  48                 0
## 8           8  39                 1
## 9           9  60                 0
## 10         10  52                 0

## Summary Statistics

summary(claims_clean)

##    Patient_ID         Age        Readmitted_30Days
##  Min.   : 1.00   Min.   :34.00   Min.   :0.0      
##  1st Qu.: 3.25   1st Qu.:45.75   1st Qu.:0.0      
##  Median : 5.50   Median :51.00   Median :0.0      
##  Mean   : 5.50   Mean   :52.10   Mean   :0.4      
##  3rd Qu.: 7.75   3rd Qu.:58.75   3rd Qu.:1.0      
##  Max.   :10.00   Max.   :72.00   Max.   :1.0

## Visualization Example

library(ggplot2)

ggplot(claims_clean, aes(x = Age, fill = factor(Readmitted_30Days))) +
  geom_histogram(binwidth = 5, color = "white") +
  labs(title = "Readmissions by Age Group",
       x = "Age",
       y = "Count",
       fill = "Readmitted in 30 Days") +
  theme_minimal()