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Job request: 24069

Organisation:
Bennett Institute
Workspace:
wp-work-package-1
ID:
fwqdf62i54jc3shq
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 in the project, within a secure environment.

By cross-referencing the list of jobs with the pipeline section below, you can infer what security level various outputs were written to. Researchers can never directly view outputs marked as highly_sensitive ; they can only request that code runs against them. Outputs marked as moderately_sensitive can be viewed by an approved researcher by logging into a highly secure environment. Only outputs marked as moderately_sensitive can be requested for release to the public, via a controlled output review service.

Jobs

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

  • Created:

  • Started:

  • Finished:

  • Runtime: 38:54:50

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

Job information

Status
Succeeded
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