Introduction to ehrQL concepts
This page outlines some important concepts that you should understand before reading the rest of the documentation.
ehrQL is a Python-based query language for electronic health record (EHR) data. ehrQL has been designed for use with the OpenSAFELY platform.
An ehrQL query takes the form of a dataset definition.
A dataset definition is written in Python and is saved in a file.
When a dataset definition is executed against data, a dataset is generated and written to an output file.
When developing an ehrQL query, you run a dataset definition against dummy data. Dummy data can be produced automatically, or you can provide your own.
Once you are confident that your ehrQL query is correct, you can run it against real patient data in an OpenSAFELY backend.
A dataset is a table containing one row per patient, and one column per feature of interest.
Features of interest might include:
- a patient's age on a given date
- whether a patient has received a particular medication in a given interval
- a patient's recorded cause of death
Refer to the collection of examples for more examples of features, and how to include features in an ehrQL query.
A dataset is restricted to include only the patients who belong to a specified population. For example, a typical study might be interested only in the population of patients:
- who were registered with any practice on a given date
- who died in a certain interval
- who received a particular diagnosis
Once a dataset has been generated, further analysis can be carried out. Refer to ehrQL and OpenSAFELY for more details.
A short ehrQL example🔗
This example is here to give you an idea of what ehrQL looks like. It is explained more fully in the tutorial.
The dataset definition🔗
This is a minimal, but complete, example of a dataset definition:
from ehrql import Dataset
from ehrql.tables.beta.core import patients, medications
dataset = Dataset()
dataset.define_population(patients.date_of_birth.is_on_or_before("1999-12-31"))
asthma_codes = ["39113311000001107", "39113611000001102"]
latest_asthma_med = (
medications.where(medications.dmd_code.is_in(asthma_codes))
.sort_by(medications.date)
.last_for_patient()
)
dataset.med_date = latest_asthma_med.date
dataset.med_code = latest_asthma_med.dmd_code
In this dataset definition, the dataset is restricted to the population of patients born on or before 31st December 1999. The features of interest are the dm+d code and date of the last asthma medication that the patient was prescribed.
The output🔗
The above dataset definition, when executed against some simple dummy data, might generate a CSV file containing the following rows:
patient_id |
med_date |
med_code |
---|---|---|
0 | 2014-01-11 | 39113611000001102 |
1 | 2018-09-21 | 39113311000001107 |
4 | 2017-05-11 | 39113611000001102 |
5 | ||
7 | 2019-07-06 | 39113611000001102 |
8 |