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MIXED_TEST_SUIT.md

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Predicated and where

  1. Upload Corona_Anamnese.opt if not exist
  2. Upload Corona_Anamnese2.opt if not exist
  3. Create ehr and save {ehr_id1}
  4. Create composition Corona_Anamnese.json and save {comp_id1}
  5. Create ehr and save {ehr_id2}
  6. Create composition Corona_Anamnese2.json and save {comp_id2}
  7. Create composition Corona_Anamnese3.json and save {comp_id3}
  8. Run Query SELECT e/ehr_id/value, c/uid/value, o/name/value, l/value/value FROM EHR e CONTAINS COMPOSITION c CONTAINS OBSERVATION o[openEHR-EHR-OBSERVATION.symptom_sign_screening.v0,'Husten'] contains ELEMENT l[at0005,'Vorhanden?'] where c/archetype_details/template_id/value matches { 'Corona_Anamnese','Corona_Anamnese2'} and e/ehr_id/value matches {'{ehr_id1}','{ehr_id2}'}
  9. Check Result : [ "{ehr_id2}", "{comp_id3}", "Husten", "Vorhanden" ], [ "{ehr_id2}", "{comp_id2}", "Husten", "Nicht vorhanden" ], [ "{ehr_id1}", "{comp_id1}", "Husten", "Vorhanden" ] ]
  10. Run Query SELECT e/ehr_id/value, c/uid/value, o/name/value, l/value/value FROM EHR e CONTAINS COMPOSITION c CONTAINS OBSERVATION o[openEHR-EHR-OBSERVATION.symptom_sign_screening.v0,'Husten'] contains ELEMENT l[at0005] where c/archetype_details/template_id/value matches { 'Corona_Anamnese','Corona_Anamnese2'} and e/ehr_id/value matches {'{ehr_id1}','{ehr_id2}'}
  11. Check Result : [ "{ehr_id2}", "{comp_id3}", "Husten", "Vorhanden" ], [ "{ehr_id2}", "{comp_id2}", "Husten", "Nicht vorhanden" ], [ "{ehr_id1}", "{comp_id1}", "Husten", "Vorhanden" ] ]
  12. Run Query SELECT e/ehr_id/value, c/uid/value, o/name/value, l/value/value FROM EHR e CONTAINS COMPOSITION c CONTAINS OBSERVATION o[openEHR-EHR-OBSERVATION.symptom_sign_screening.v0,'Husten'] contains ELEMENT l[name/value='Vorhanden?'] where c/archetype_details/template_id/value matches { 'Corona_Anamnese','Corona_Anamnese2'} and e/ehr_id/value matches {'{ehr_id1}','{ehr_id2}'}
  13. Check Result : [ "{ehr_id2}", "{comp_id3}", "Husten", "Vorhanden" ], [ "{ehr_id2}", "{comp_id2}", "Husten", "Nicht vorhanden" ], [ "{ehr_id1}", "{comp_id1}", "Husten", "Vorhanden" ] ]
  14. Run Query SELECT e/ehr_id/value, c/uid/value, o/name/value, l/value/value FROM EHR e CONTAINS COMPOSITION c CONTAINS OBSERVATION o[openEHR-EHR-OBSERVATION.symptom_sign_screening.v0,'Husten'] contains ELEMENT l[at0005,'Vorhanden?'] where c/archetype_details/template_id/value matches { 'Corona_Anamnese'} and e/ehr_id/value matches {'{ehr_id1}'}
  15. Check Result : [ "{ehr_id1}", "{comp_id1}", "Husten", "Vorhanden" ] ]
  16. Run Query SELECT o/name/value, l/value/value FROM EHR e CONTAINS COMPOSITION c CONTAINS OBSERVATION o[openEHR-EHR-OBSERVATION.symptom_sign_screening.v0] contains ELEMENT l[at0005,'Vorhanden?'] where c/archetype_details/template_id/value matches { 'Corona_Anamnese'} and e/ehr_id/value matches {'{ehr_id1}'}
  17. Check Result : [ "Durchfall", "Nicht vorhanden" ], [ "Fieber oder erhöhte Körpertemperatur", "Vorhanden" ], [ "Gestörter Geruchssinn", "Nicht vorhanden" ], [ "Gestörter Geschmackssinn", "Nicht vorhanden" ], [ "Heiserkeit", "Nicht vorhanden" ], [ "Husten", "Vorhanden" ], [ "Schnupfen", "Vorhanden" ]
  18. Run Query SELECT n/value/value, l/value/value FROM EHR e CONTAINS COMPOSITION c CONTAINS OBSERVATION o[openEHR-EHR-OBSERVATION.symptom_sign_screening.v0] contains (ELEMENT l[at0005] and ELEMENT n[at0004]) where c/archetype_details/template_id/value matches { 'Corona_Anamnese'} and e/ehr_id/value matches {'{ehr_id1}'}
  19. Check Result : [ "Durchfall", "Nicht vorhanden" ], [ "Fieber oder erhöhte Körpertemperatur", "Vorhanden" ], [ "Gestörter Geruchssinn", "Nicht vorhanden" ], [ "Gestörter Geschmackssinn", "Nicht vorhanden" ], [ "Heiserkeit", "Nicht vorhanden" ], [ "Husten", "Vorhanden" ], [ "Schnupfen", "Vorhanden" ]
  20. Run Query SELECT n/value/value, l/value/value FROM EHR e CONTAINS COMPOSITION c CONTAINS OBSERVATION o[openEHR-EHR-OBSERVATION.symptom_sign_screening.v0] contains (ELEMENT l[at0005] and ELEMENT n[at0004]) where c/archetype_details/template_id/value matches { 'Corona_Anamnese'} and l/value/value = 'Vorhanden' and e/ehr_id/value matches {'{ehr_id1}'}
  21. Check Result : [ "Fieber oder erhöhte Körpertemperatur", "Vorhanden" ], [ "Husten", "Vorhanden" ], [ "Schnupfen", "Vorhanden" ]
  22. Run Query SELECT n/value/value, l/value/value FROM EHR e CONTAINS COMPOSITION c CONTAINS OBSERVATION o[openEHR-EHR-OBSERVATION.symptom_sign_screening.v0] contains (ELEMENT l[at0005] and ELEMENT n[at0004]) where c/archetype_details/template_id/value matches { 'Corona_Anamnese'} and l/value/value = 'Vorhanden' and n/value/value = 'Husten' and e/ehr_id/value matches {'{ehr_id1}'}
  23. Check Result : [ "Husten", "Vorhanden" ]

Contains and where

  1. Upload type_repetition_conformance_ehrbase.org.opt if not exist
  2. Create ehr
  3. Create composition type_repetition_conformance_ehrbase.org_where1.json
  4. Run Query SELECT s1/feeder_audit/originating_system_item_ids/id, s2/feeder_audit/originating_system_item_ids/id, o/feeder_audit/originating_system_item_ids/id, v/time/value , c1/items[at0001]/value/value , l/value/value FROM EHR e contains COMPOSITION c contains SECTION s1 contains SECTION s2 contains OBSERVATION o contains EVENT v contains CLUSTER c1 contains CLUSTER c2 contains ELEMENT l
  5. Check Result : [ [ "ad_hoc_heading 1", "conformance_section 1", "observation 1", "2021-02-03T04:05:06", "cluster inter text1", "cluster outer text1" ], [ "ad_hoc_heading 1", "conformance_section 1", "observation 1", "2021-02-03T04:05:06", "cluster inter text1", "cluster outer text2" ], [ "ad_hoc_heading 1", "conformance_section 1", "observation 1", "2022-02-03T04:05:06", "cluster inter text2", "cluster outer text3" ], [ "ad_hoc_heading 1", "conformance_section 1", "observation 1", "2022-02-03T04:05:06", "cluster inter text2", "cluster outer text4" ], [ "ad_hoc_heading 1", "conformance_section 2", "observation 2", "2023-02-03T04:05:06", "cluster inter text3", "cluster outer text5" ], [ "ad_hoc_heading 1", "conformance_section 2", "observation 2", "2023-02-03T04:05:06", "cluster inter text3", "cluster outer text6" ], [ "ad_hoc_heading 1", "conformance_section 2", "observation 2", "2024-02-03T04:05:06", "cluster inter text4", "cluster outer text7" ], [ "ad_hoc_heading 1", "conformance_section 2", "observation 2", "2024-02-03T04:05:06", "cluster inter text4", "cluster outer text8" ], [ "ad_hoc_heading 2", "conformance_section 3", "observation 3", "2021-02-03T04:05:06", "cluster inter text5", "cluster outer text9" ], [ "ad_hoc_heading 2", "conformance_section 3", "observation 3", "2021-02-03T04:05:06", "cluster inter text5", "cluster outer text10" ], [ "ad_hoc_heading 2", "conformance_section 3", "observation 3", "2022-02-03T04:05:06", "cluster inter text6", "cluster outer text11" ], [ "ad_hoc_heading 2", "conformance_section 3", "observation 3", "2022-02-03T04:05:06", "cluster inter text6", "cluster outer text12" ], [ "ad_hoc_heading 2", "conformance_section 4", "observation 4", "2023-02-03T04:05:06", "cluster inter text7", "cluster outer text13" ], [ "ad_hoc_heading 2", "conformance_section 4", "observation 4", "2023-02-03T04:05:06", "cluster inter text7", "cluster outer text14" ], [ "ad_hoc_heading 2", "conformance_section 4", "observation 4", "2024-02-03T04:05:06", "cluster inter text8", "cluster outer text15" ], [ "ad_hoc_heading 2", "conformance_section 4", "observation 4", "2024-02-03T04:05:06", "cluster inter text8", "cluster outer text16" ] ]
  6. Run Query SELECT s1/feeder_audit/originating_system_item_ids/id, s2/feeder_audit/originating_system_item_ids/id, o/feeder_audit/originating_system_item_ids/id, v/time/value , c1/items[at0001]/value/value , l/value/value FROM EHR e contains COMPOSITION c contains SECTION s1 contains SECTION s2 contains OBSERVATION o contains EVENT v contains CLUSTER c1 contains CLUSTER c2 contains ELEMENT l where s1/feeder_audit/originating_system_item_ids/id = 'ad_hoc_heading 2'
  7. Check Result : [ [ "ad_hoc_heading 2", "conformance_section 3", "observation 3", "2021-02-03T04:05:06", "cluster inter text5", "cluster outer text9" ], [ "ad_hoc_heading 2", "conformance_section 3", "observation 3", "2021-02-03T04:05:06", "cluster inter text5", "cluster outer text10" ], [ "ad_hoc_heading 2", "conformance_section 3", "observation 3", "2022-02-03T04:05:06", "cluster inter text6", "cluster outer text11" ], [ "ad_hoc_heading 2", "conformance_section 3", "observation 3", "2022-02-03T04:05:06", "cluster inter text6", "cluster outer text12" ], [ "ad_hoc_heading 2", "conformance_section 4", "observation 4", "2023-02-03T04:05:06", "cluster inter text7", "cluster outer text13" ], [ "ad_hoc_heading 2", "conformance_section 4", "observation 4", "2023-02-03T04:05:06", "cluster inter text7", "cluster outer text14" ], [ "ad_hoc_heading 2", "conformance_section 4", "observation 4", "2024-02-03T04:05:06", "cluster inter text8", "cluster outer text15" ], [ "ad_hoc_heading 2", "conformance_section 4", "observation 4", "2024-02-03T04:05:06", "cluster inter text8", "cluster outer text16" ] ]
  8. Run Query SELECT s1/feeder_audit/originating_system_item_ids/id, s2/feeder_audit/originating_system_item_ids/id, o/feeder_audit/originating_system_item_ids/id, v/time/value , c1/items[at0001]/value/value , l/value/value FROM EHR e contains COMPOSITION c contains SECTION s1 contains SECTION s2 contains OBSERVATION o contains EVENT v contains CLUSTER c1 contains CLUSTER c2 contains ELEMENT l where s1/feeder_audit/originating_system_item_ids/id = 'ad_hoc_heading 2' and s2/feeder_audit/originating_system_item_ids/id = 'conformance_section 4' and v/time/value = '2024-02-03T04:05:06'
  9. Check Result : [ [ "ad_hoc_heading 2", "conformance_section 4", "observation 4", "2024-02-03T04:05:06", "cluster inter text8", "cluster outer text15" ], [ "ad_hoc_heading 2", "conformance_section 4", "observation 4", "2024-02-03T04:05:06", "cluster inter text8", "cluster outer text16" ] ]
  10. Run Query SELECT s1/feeder_audit/originating_system_item_ids/id, s2/feeder_audit/originating_system_item_ids/id, o/feeder_audit/originating_system_item_ids/id, v/time/value , c1/items[at0001]/value/value , l/value/value FROM EHR e contains COMPOSITION c contains SECTION s1 contains SECTION s2 contains OBSERVATION o contains EVENT v contains CLUSTER c1 contains CLUSTER c2 contains ELEMENT l where s1/feeder_audit/originating_system_item_ids/id = 'ad_hoc_heading 2' and s2/feeder_audit/originating_system_item_ids/id = 'conformance_section 4' and v/time/value = '2024-02-03T04:05:06' and l/value/value = 'cluster outer text16'
  11. Check Result : [ [ "ad_hoc_heading 2", "conformance_section 4", "observation 4", "2024-02-03T04:05:06", "cluster inter text8", "cluster outer text16" ] ]

DISTINCT

  1. Upload Corona_Anamnese.opt if not exist
  2. Upload Corona_Anamnese2.opt if not exist
  3. Create ehr and save {ehr_id1}
  4. Create composition Corona_Anamnese.json and save {comp_id1}
  5. Create ehr and save {ehr_id2}
  6. Create composition Corona_Anamnese2.json and save {comp_id2}
  7. Create composition Corona_Anamnese3.json and save {comp_id3}
  8. Run Query SELECT DISTINCT o/data[at0001]/events[at0002]/data[at0003]/items[at0022]/items[at0004]/value/value , o/data[at0001]/events[at0002]/data[at0003]/items[at0022]/items[at0005]/value/value FROM EHR e contains COMPOSITION c contains OBSERVATION o[openEHR-EHR-OBSERVATION.symptom_sign_screening.v0] order by o/data[at0001]/events[at0002]/data[at0003]/items[at0022]/items[at0004]/value/value , o/data[at0001]/events[at0002]/data[at0003]/items[at0022]/items[at0005]/value/value
  9. Check Result in Order : [ [ "Durchfall", "Nicht vorhanden" ], [ "Fieber oder erhöhte Körpertemperatur", "Vorhanden" ], [ "Heiserkeit", "Nicht vorhanden" ], [ "Husten", "Nicht vorhanden" ], [ "Husten", "Vorhanden" ], [ "Schnupfen", "Vorhanden" ], [ "gestörter Geruchssinn", "Nicht vorhanden" ], [ "gestörter Geschmackssinn", "Nicht vorhanden" ] ]
  10. Run Query SELECT DISTINCT o/data[at0001]/events[at0002]/data[at0003]/items[at0022]/items[at0004]/value/value FROM EHR e contains COMPOSITION c contains OBSERVATION o[openEHR-EHR-OBSERVATION.symptom_sign_screening.v0] where o/data[at0001]/events[at0002]/data[at0003]/items[at0022]/items[at0005]/value/value = 'Vorhanden' order by o/data[at0001]/events[at0002]/data[at0003]/items[at0022]/items[at0004]/value/value
  11. Check Result in Order : [ [ "Fieber oder erhöhte Körpertemperatur" ], [ "Husten" ], [ "Schnupfen" ] ]
  12. Run Query SELECT DISTINCT o/data[at0001]/events[at0002]/data[at0003]/items[at0022]/items[at0004]/value/value FROM EHR e contains COMPOSITION c contains OBSERVATION o[openEHR-EHR-OBSERVATION.symptom_sign_screening.v0] where o/data[at0001]/events[at0002]/data[at0003]/items[at0022]/items[at0005]/value/value = 'Vorhanden' order by o/data[at0001]/events[at0002]/data[at0003]/items[at0022]/items[at0004]/value/value limit 1 offset 1
  13. Check Result in Order : [ [ "Husten" ] ]