ACT-PharmacyFirst-Protocol2-healthcare-usage

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Pipeline Overview and Data Flow

Last update: 7 Apr 2026

The current workflow consists of three main components:

1. Pregnancy dataset and measures generation
2. Patient-level dataset aggregation and measures
3. Practice-level aggregation and summary (STP-level outputs)

The diagram below illustrates the overall data flow and dependencies between steps.

graph TD
%% Pregnancy pipeline
A1[Pregnancy dataset Nov 2024] --> B[Generate pregnancy measures]
A2[Pregnancy dataset Nov 2025] --> B
B --> C[Pregnancy checks and charts]

%% Patient pipeline for aggregation
D1[Patient dataset deinition] --> E1
D1[Patient dataset deinition] --> E2
E1[Patient dataset Feb 2024] --> F1[Combine monthly datasets]
E2[Patient dataset Mar 2024] --> F1

%% Patient pipeline for measures
D2[Patient dataset definition for measures] --> E3[Generate patient measures]

%% Practice pipeline
D2[Patient dataset definition for measures] --> E4
E4[Generate practice measures] --> F2[Aggregate to practice-level dataset]
F2 --> G[STP-level summary output]

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Pregnancy dataset and measures generation

Please refer to this issue for implementation details.

Patient-level dataset aggregation and measures

There are two patient-level dataset definition:

• dataset_definition_patients: used for generating monthly datasets for downstream analysis
• dataset_definition_patients_measures: for creating measures

Monthly data generation and aggregation

All monthly dataset generation steps rely on dataset_definition_patients. This requires defining explicit actions in project.yaml for each month. These actions cannot be easily parameterised, so a separate action must be created for each specific month.

Steps:

1. In config.py, define the start and end dates.

2. Run the following command to generate the monthly actions:

   python analysis/generate_project_action.py > project_test.yaml

3. Copy the generated monthly actions from project_test.yaml into project.yaml.

4. Update the combine_monthly_patient_gz step:

   • Modify the needs field so that it depends on all generated monthly datasets
   • The current configuration includes only two months as an example
   • When using config.py, this should be updated to include all months in the specified range

Patient-level measures

The generate_patient_measures step does not depend on the monthly datasets. Instead, it relies on:

• dataset_definition_patients_measures.py
• measures_patient.py

Practice-level aggregation and summary

Measures are first defined and calculated at the patient level. Each measure is computed over monthly intervals and grouped by practice, STP and region. The output from the measure generation step is used to construct a practice-level dataset. The practice-level dataset is then further aggregated to the STP level. This enables comparison across STPs and supports higher-level reporting.

About the OpenSAFELY framework

The OpenSAFELY framework is a Trusted Research Environment (TRE) for electronic health records research in the NHS, with a focus on public accountability and research quality.

Read more at OpenSAFELY.org.

Licences

As standard, research projects have a MIT license.