NHS England Accident & Emergency Attendances and Admissions

Reported attendances, 4 hour breaches and admissions for all A&E departments in England for the years 2016/17 through 2018/19 (Apr-Mar). The data has been tidied to be easily usable within the tidyverse of packages.

data(ae_attendances)

Format

Tibble with six columns

period

The month that this data relates to

org_code

The ODS code for this provider

type

The department type. either 1, 2 or other

attendances

the number of patients who attended this department in this month

breaches

the number of patients who breaches the 4 hour target in this month

admissions

the number of patients admitted from A&E to the hospital in this month

Source

NHS England Statistical Work Areas

Details

Data sourced from NHS England Statistical Work Areas which is available under the Open Government Licence v3.0

Examples

data(ae_attendances)
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)
library(scales)

# Create a plot of the performance for England over time
ae_attendances %>%
  group_by(period) %>%
  summarise_at(vars(attendances, breaches), sum) %>%
  mutate(performance = 1 - breaches / attendances) %>%
  ggplot(aes(period, performance)) +
  geom_hline(yintercept = 0.95, linetype = "dashed") +
  geom_line() +
  geom_point() +
  scale_y_continuous(labels = percent) +
  labs(title = "4 Hour performance over time")


# Now produce a plot showing the performance of each trust
ae_attendances %>%
  group_by(org_code) %>%
  # select organisations that have a type 1 department
  filter(any(type == "1")) %>%
  summarise_at(vars(attendances, breaches), sum) %>%
  arrange(desc(attendances)) %>%
  mutate(performance = 1 - breaches / attendances,
         overall_performance = 1 - sum(breaches) / sum(attendances),
         rank = rank(-performance, ties.method = "first") / n()) %>%
  ggplot(aes(rank, performance)) +
  geom_vline(xintercept = c(0.25, 0.5, 0.75), linetype = "dotted") +
  geom_hline(yintercept = 0.95, colour = "red") +
  geom_hline(aes(yintercept = overall_performance), linetype = "dotted") +
  geom_point() +
  scale_y_continuous(labels = percent) +
  theme_minimal() +
  theme(panel.grid = element_blank(),
        axis.text.x = element_blank()) +
  labs(title = "4 Hour performance by trust",
       subtitle = "Apr-16 through Mar-19",
       x = "", y = "")