How to use the monthly activity and performance data

Note: This guide is for illustration purposes only and may contain errors. It demonstrates example usage of the dataset and functions, but results should not be interpreted as validated analyses.

This example demonstrates how to explore monthly NHS Talking Therapies reports using the nhstt package. To illustrate the basic workflow for accessing and visualising this publicly available data we’ll look at three examples:

1. Measures describing different treatment end codes
2. Measures describing wait times from referral to first treatment
3. Comparing a measure across different providers

Setup

# Load nhstt package for data
library(nhstt)

# Load other packages for analysis
library(ggplot2)
library(lubridate)
library(dplyr)
library(scales)
library(stringr)
library(gt)

# Get 5 monthly activity performance reports
activity_performance <- get_activity_performance_monthly(
  periods = c("2024-01", "2024-02", "2024-03", "2024-04", "2024-05")
)

We start by loading R packages and downloading the monthly activity and performance dataset with get_activity_performance_monthly(). This report contains monthly performance indicators for NHS Talking Therapies services across England.

The activity_performance dataset defined above contains a total of 204 different activity and performance measures, available from January 2024 to May 2024 (5 reporting periods).

Example 1: Explore changes in referrals that ended

Define measures related to referrals that ended

Using the get_metadata_monthly() function, we can look up the descriptions for these measures:

# Define measure ids for analysis
referral_ended_measures <- c(
  "M057",
  "M058",
  "M059",
  "M060",
  "M061",
  "M066",
  "M340",
  "M062",
  "M063",
  "M066",
  "M344",
  "M341",
  "M069",
  "M342",
  "M070",
  "M071"
)

#> ℹ Downloading metadata_measures_monthly (monthly) for 2025-07
#> ! Download failed (attempt 1/3), retrying...
#> ℹ Downloading metadata_measures_monthly (monthly) for 2025-07! Download failed (attempt 2/3), retrying...
#> ℹ Downloading metadata_measures_monthly (monthly) for 2025-07✖ Downloading metadata_measures_monthly (monthly) for 2025-07 ... failed
#> 
#> ! Download failed, using package metadata for 2025-07

ID	Description
M057	Count of referrals that ended in the reporting period with an end code of 'Not suitable for IAPT service - no action taken or directed back to referrer’
M058	Count of referrals that ended in the reporting period with an end code of ‘Not suitable for IAPT service - signposted elsewhere with mutual agreement of patient’
M059	Count of referrals that ended in the reporting period with an end code of 'Discharged by mutual agreement following advice and support'
M060	Count of referrals that ended in the reporting period with an end code of 'Referred to another therapy service by mutual agreement'
M061	Count of referrals that ended in the reporting period with an end code of 'Suitable for IAPT service, but patient declined treatment that was offered'
M062	Count of referrals that ended in the reporting period with an end code of ‘Deceased (Seen but not taken on for a course of treatment)’
M063	Count of referrals that ended in the reporting period with an end code of 'Not known (Seen but not taken on for a course of treatment)'
M066	Count of referrals with an end date in the reporting period - Improving Access to Psychological Therapies care spell end code is 'Mutually agreed completion of treatment’
M069	Count of referrals that ended in the reporting period with an end code of ‘Deceased (Seen and taken on for a course of treatment)’
M070	Count of referrals that ended in the reporting period with an end code of ‘Not Known (Seen and taken on for a course of treatment)’
M340	Count of referrals that ended in the reporting period with an end code of ‘Incomplete Assessment (Patient dropped out)’
M341	Count of referrals that ended in the reporting period with an end code of ‘Termination of treatment earlier than patient requested’
M342	Count of referrals that ended in the reporting period with an end code of ‘Not assessed’
M344	Count of referrals that ended in the reporting period with an end code of ‘Termination of treatment earlier than Care Professional planned’
M071	Count of referrals that ended in the reporting period with an invalid end code

Analysis

Here we filter the data to include all measures defined above in referral_ended_measures and select all service providers:

# Select data for analysis
selected_activity_performance <- activity_performance |>
  filter(measure_id %in% c(referral_ended_measures)) |>
  filter(group_type == "Provider")

Now we can visualise the trends over time broken down by codes for referrals that ended for each service provider. Note that the y-axis scales are not fixed to allow better visualisation of trends for measures with different count ranges.

Trends for counts of different referrals that ended measures.

Example 2: Explore wait times from referrral to first treatment

Define measures

Here we will explore changes in the following measures related to wait times from referrral to first treatment. Note that there are other measures related to wait times available in the dataset.

# Define measure ids for analysis
first_tx_waited_measures <- c(
  "M039",
  "M040",
  "M041",
  "M042",
  "M043",
  "M044",
  "M045"
)

ID	Description
M039	Count of referrals yet to have a first treatment who have been waiting 0 to 2 weeks at the end of the reporting period
M040	Count of referrals yet to have a first treatment who have been waiting 0 to 4 weeks at the end of the reporting period
M041	Count of referrals yet to have a first treatment who have been waiting 0 to 6 weeks at the end of the reporting period
M042	Count of referrals yet to have a first treatment who have been waiting 0 to 12 weeks at the end of the reporting period
M043	Count of referrals yet to have a first treatment who have been waiting 0 to 18 weeks at the end of the reporting period
M044	Count of referrals yet to have a first treatment who have been waiting over 18 weeks at the end of the reporting period
M045	Count of referrals yet to have a first treatment who have been waiting over 90 days at the end of the reporting period

Analysis

Here we filter the data to include all measures defined above in first_tx_waited_measures and select all service providers:

# Select data for analysis
selected_activity_performance <- activity_performance |>
  filter(measure_id %in% first_tx_waited_measures) |>
  filter(group_type == "Provider")

Now we can visualise the trends over time broken down by different waiting time periods from referral to first treatment, with one line for each service provider.

Trends for counts of different waiting time periods from referral to first treatment for each service.

Example 3: Compare a measure across different providers

We can also focus on a single measure and compare counts across different services. In this example we look at measure M344 (Count of referrals that ended in the reporting period with an end code of ‘Termination of treatment earlier than Care Professional planned’) and visualise trends over time for the four providers with the highest overall counts.

Here we identify the 4 providers with the most recorded activity in M344 across the whole time period:

top4_m344_providers <- activity_performance |>
  filter(measure_id == "M344") |>
  filter(group_type == "Provider") |>
  select(org_name2, value) |>
  group_by(org_name2) |>
  summarise(total = sum(value, na.rm = TRUE)) |>
  slice_max(total, n = 4) |>
  pull(org_name2)

We can use this list (top4_m344_providers) to filter the data and plot trends for these four providers:

Counts of referrals with measure M344 for the four providers with the highest totals.