openehr Querying for secondary uses Ian McNicoll

Transcription

1 openehr Querying for secondary uses Ian McNicoll

2 Example AQL All Nursing reports during this admission with a clinical synopsis record SELECT c FROM EHR[ehr_id/value='a4a3f0c8-254a-49a5-97dd-784d50c46b78'] CONTAINS Composition c[openehr-ehr-composition.encounter.v1] CONTAINS Evaluation e[openehr-ehr- EVALUATION.clinical_synopsis.v1] WHERE c/name/value matches {'Summary report'} AND c/context/start_time >= ' T ' AND c/context/start_time < ' T '

3 SELECT com0/context/start_time/value as START_DATE, Vital Signs chart obs1/data[at0001]/events[at0006]/data[at0003]/items[at0004]/value/magnitude as SYSTOLIC, obs1/data[at0001]/events[at0006]/data[at0003]/items[at0005]/value/magnitude as DIASTOLIC, obs3/data[at0002]/events[at0003]/data[at0001]/items[at0004]/value/magnitude as PULSE_RATE, obs4/data[at0001]/events[at0002]/data[at0003]/items[at0004]/value/magnitude as RESPIRATORY_RATE, obs6/data[at0001]/events[at0002]/data[at0003]/items[at0006]/value as SPO2_PROPORTION, obs7/data[at0002]/events[at0003]/data[at0001]/items[at0004]/value as BODY_TEMPERATURE_QUANTITY FROM EHR e[ehr_id/value='a4a3f0c8-254a-49a5-97dd-784d50c46b78'] CONTAINS COMPOSITION com0 [openehr-ehr-composition.encounter.v1] CONTAINS (OBSERVATION obs7 [openehr-ehr-observation.body_temperature-zn.v1] OR OBSERVATION obs3 [openehr-ehr-observation.heart_rate-pulse-zn.v1] OR OBSERVATION obs1 [openehr-ehr-observation.blood_pressure-zn.v1] OR OBSERVATION obs6 [openehr-ehr-observation.indirect_oximetry.v1] OR OBSERVATION obs4 [openehr-ehr-observation.respiration.v1]) WHERE com0/name/value matches {'Vital functions', 'Respiratory assessment'} AND com0/context/start_time >= ' T ' AND com0/context/start_time >= ' T '

4 Basic principle You can never guess how the data will be queried or what purpose it will be put to Make it as open (queryable) as possible even if you think it only has one use when you design it

5 Scenario 1 Get the number of all patients with diabetes who have HbA1c results greater than 7.0 in last 12 months.

6 Scenario 1 SELECT COUNT(e/ehr_id) FROM EHR e CONTAINS (COMPOSITION c [openehr-ehr- COMPOSITION.problem_list.v1] CONTAINS EVALUATION e [openehr-ehr-evaluation.problemdiagnosis.v1] AND COMPOSITION c1 [openehr-ehr-composition.report.v1] CONTAINS OBSERVATION o [openehr-ehr-observation.laboratoryhba1c.v1]) WHERE e/data/items[at0002.1]/value/value= diabetes mellitus AND c1/context/other_context/items[at0006]/items[at0013]/value > current-date() -P1Y AND o/data/events[at0002]/data/items[at0013.1]/value/numerator > 7

7 Scenario 2 Get all HbA1c observations that have been done in the last 12 months for a specific patient.

8 Scenario 2 SELECT o FROM EHR e[ehr_id=$ehrid] CONTAINS COMPOSITION c [openehr-ehr-composition.report.v1] CONTAINS OBSERVATION o [openehr-ehr-observation.laboratoryhba1c.v1] WHERE c/context/other_context[at0001]/items[at0006]/ items[at0013]/value > current-date()-1py

9 Scenario 3 Get a patient s current medication list

10 SELECT c Scenario 3 FROM EHR e[ehr_id=$ehrid] CONTAINS COMPOSITION c [openehr-ehr- COMPOSITION.medication_list.v1] WHERE c/name/value= current medication list

11 TIMEWINDOW Constrain the query source to data committed to the system before : TIMEWINDOW / Constrain the query source to data committed within the period of two years before : TIMEWINDOW P2Y/

12 AQL Queries are portable The one query will work the same way in any openehr system that uses archetypes unlike existing database queries that are dependent on the local database schema. This means they can be shared among decision support tools e.g. a query to detect risk of heart attack, based on history of BP, smoking, exercise, other conditions

13 Querying with Terminology SELECT o/data[at0002]/events[at0003]/data/items[at0015]/it ems[at0018]/name FROM Ehr [uid=$ehruid] CONTAINS Composition c CONTAINS Observation o[openehr-ehr- OBSERVATION.microbiology.v1] WHERE o/data[at0002]/events[at0003]/data/items[at0015]/it ems[at0018]/items[at0019]/items[at0021]/name/defi ning_code/code_string matches {' ', ' ', ' '}

14 Querying with Terminology SELECT e/ehr_status/subject/external_ref/id/value, diagnosis/data/items[at0002.1]/value FROM EHR e CONTAINS Composition c[openehr- EHR-COMPOSITION.problem_list.v1] CONTAINS Evaluation diagnosis [openehr-ehr- EVALUATION.problem-diagnosis.v1] WHERE c/name/value='current Problems' AND diagnosis /data/items[at0002.1]/value/defining_code matches { terminology://snomed- CT/hierarchy?rootConceptId= }

15 Research Query: demographic QUERY as Summary_Query_Report1 SELECT dataset, demo FROM EHR e CONTAINS COMPOSITION dataset[openehr-ehr- COMPOSITION.data_set.v1] AND COMPOSITION demo[openehr-ehr-composition.data_set.v1] WHERE demo/name/value='baseline_demographic' AND dataset/archetype_details/template_id/value='baseline_health VIEW TABLE as Summary_Query_Report1 Maps demo to baseline_demographic, dataset to baseline_health SELECT dataset.asthm_flg, demo.sex_cde, demo.cob_cde, demo.atndage_ord

16 Mappings in template <map alias="h2_id" template_path="/context/other_context[at000 1]/items[at0011 and name/defining_code/code_string='at0012']/v alue" /> <map alias="sex_cde" template_path="/content[openehr-ehr- ADMIN_ENTRY.demographics_ccv.v1 and name/value='demographics (CCV)']/data[at0001]/items[at0095 and name/value='individual']/items[at0065]/items [at0011]/value" />

17 Result: Stata output

18 Result: openehr data

19 Research Query: Breast cancer QUERY as cancer_breast SELECT e/ehr_id/value as ehr_id, composition FROM EHR e CONTAINS COMPOSITION composition[openehr-ehr- COMPOSITION.data_set.v1] WHERE composition/archetype_details/template_id/value='canc er_breast' VIEW TABLE as cancer_breast MAPS composition to cancer_breast SELECT ehr_id, composition.*

20 Result: Stata output

21 Result: openehr data