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Job request: 24061

Organisation:
Bennett Institute
Workspace:
wp-work-package-1
ID:
bzvwian634zpleuk
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Arina Tamborska requested one or more actions to be run against real patient data within a secure environment.

By cross-referencing the list of jobs with the pipeline section below, you can infer what security level the outputs were written to.

The output security levels are:

  • highly_sensitive
    • Researchers can never directly view these outputs
    • Researchers can only request code is run against them
  • moderately_sensitive
    • Can be viewed by an approved researcher by logging into a highly secure environment
    • These are the only outputs that can be requested for public release via a controlled output review service.

Pipeline

Show project.yaml 
version: "3.0"

expectations:
  population_size: 1000

actions:
  generate_patient_measures:
    run: ehrql:v1 generate-measures --output output/patient_measures/patient_measures.csv.gz analysis/wp_patient_measures_def.py -- --drop_reason --drop_prescriptions --drop_indicat_prescript
    outputs:
       highly_sensitive:
         dataset: output/patient_measures/patient_measures.csv.gz
  #generate_practice_measures_keep_follow_up:
  #  run: ehrql:v1 generate-measures --output output/practice_measures/practice_measures_keep_follow_up.csv.gz analysis/wp_practice_measures_def.py -- --drop_reason --drop_prescriptions --drop_indicat_prescript
  #  outputs:
  #    highly_sensitive:
  #      dataset: output/practice_measures/practice_measures_keep_follow_up.csv.gz
  #generate_practice_measures_keep_reason:
  #  run: ehrql:v1 generate-measures --output output/practice_measures/practice_measures_keep_reason.csv.gz analysis/wp_practice_measures_def.py -- --drop_follow_up --drop_prescriptions --drop_indicat_prescript
  #  outputs:
  #    highly_sensitive:
  #      dataset: output/practice_measures/practice_measures_keep_reason.csv.gz
  #generate_practice_measures_keep_prescriptions:
  #  run: ehrql:v1 generate-measures --output output/practice_measures/practice_measures_keep_prescriptions.csv.gz analysis/wp_practice_measures_def.py -- --drop_follow_up --drop_reason --drop_indicat_prescript
  #  outputs:
  #    highly_sensitive:
  #      dataset: output/practice_measures/practice_measures_keep_prescriptions.csv.gz
  #generate_practice_measures_keep_indicat_prescript:
  #  run: ehrql:v1 generate-measures --output output/practice_measures/practice_measures_keep_indicat_prescript.csv.gz analysis/wp_practice_measures_def.py -- --drop_follow_up --drop_reason --drop_prescriptions
  #  outputs:
  #    highly_sensitive:
  #      dataset: output/practice_measures/practice_measures_keep_indicat_prescript.csv.gz
  generate_practice_measures:
    run: ehrql:v1 generate-measures --output output/practice_measures/practice_measures.csv.gz analysis/wp_practice_measures_def.py -- --drop_reason --drop_prescriptions --drop_indicat_prescript
    outputs:
      highly_sensitive:
        dataset: output/practice_measures/practice_measures.csv.gz
  generate_app_measures_intv_1:
     run: ehrql:v1 generate-measures --output output/appointments/app_measures_1.csv analysis/appointments/app_measures.py -- --start_intv 2023-07-01
     outputs:
       moderately_sensitive:
         dataset: output/appointments/app_measures_1.csv
  generate_app_measures_intv_2:
     run: ehrql:v1 generate-measures --output output/appointments/app_measures_2.csv analysis/appointments/app_measures.py -- --start_intv 2023-12-01
     outputs:
       moderately_sensitive:
         dataset: output/appointments/app_measures_2.csv
  generate_app_measures_intv_3:
    run: ehrql:v1 generate-measures --output output/appointments/app_measures_3.csv analysis/appointments/app_measures.py -- --start_intv 2018-07-01
    outputs:
      moderately_sensitive:
        dataset: output/appointments/app_measures_3.csv
  generate_app_measures_intv_4:
    run: ehrql:v1 generate-measures --output output/appointments/app_measures_4.csv analysis/appointments/app_measures.py -- --start_intv 2018-12-01
    outputs:
      moderately_sensitive:
        dataset: output/appointments/app_measures_4.csv
  # generate_pre_processing:
  #   run: python:latest analysis/pre_processing.py 
  #       --output output/practice_measures/processed_practice_measures.csv.gz
  #       --output output/patient_measures/processed_patient_measures.csv.gz
  #       --output output/patient_measures/frequency_table.csv
  #   needs: [generate_patient_measures, generate_practice_measures]
  #   outputs:
  #     highly_sensitive:
  #       practice_measure: output/practice_measures/processed_practice_measures.csv.gz
  #       patient_measure: output/patient_measures/processed_patient_measures.csv.gz
  #     moderately_sensitive:
  #       frequency_table: output/patient_measures/frequency_table.csv
  # generate_measures_viz:
  #   run: r:latest analysis/viz_measures.r
  #   needs: [generate_pre_processing]
  #   outputs:
  #     moderately_sensitive:
  #       app_figures: output/*/*.png
  #       total_measures: output/total_measures/*.csv
  # generate_test_data:
  #   run: ehrql:v1 generate-dataset analysis/dataset.py --output output/patient_measures/test.csv --test-data-file analysis/test_dataset.py
  #   outputs:
  #     highly_sensitive:
  #       dataset: output/patient_measures/test.csv

Timeline

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  • Started:

  • Finished:

  • Runtime:

These timestamps are generated and stored using the UTC timezone on the TPP backend.

Job request

Status
Failed 
GitRepoNotReachableError: Could not read from https://github.com/opensafely/winter-pressures-phase-II
Backend
TPP
Workspace
wp-work-package-1
Requested by
Arina Tamborska
Branch
main
Force run dependencies
No
Git commit hash
5cf16cb
Requested actions
  • generate_app_measures_intv_1
  • generate_app_measures_intv_2
  • generate_app_measures_intv_3
  • generate_app_measures_intv_4

Code comparison

Compare the code used in this job request