![Author rules in GDL2 Editor
RESOURCE_DESCRIPTION
original_author:Hash<string,,string>[1]
other_contributors:string[0..*]
lifecycle_state:terminology_code[1]
details:DETAIL
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DETAIL
language: terminology_code[1]
purpose:string[1]
keywords:string[0..*]
use:string[0..1]
misuse:string[0..1]
original_resource_url:Hash<string,,string>[0..1]
other_details:Hash<string,,string>[0..1]
copyright:string[0..1]
original_language:terminology_code[1]
descrption:RESOURCE_DESCRIPTION
data_bindings:DATA_BINDING
pre_conditions:ASSERTION
rules:RULE
templates:OUTPUT_TEMPLATE
default_actions:ASSIGNMENT[*]
model_id:string[1]
template_id:string[0..1]
predicates:ASSERTION[*]
type:string[1]
elements:ELEMENT
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id:string[1]
when:ASSERTION[*]
then:ASSIGNMENT[*]
priority:integer[1]
RULE id:string[1]
name:string[1]
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template_id:string[0..1]
object:OBJECT
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term_definitions:Hash<string,hash<string,TERM>>[1]
term_bindings:Hash<string,TREM_BINDING>[0..1]
TERMINOLOGY
code:string[1]
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description:string[1]
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TERM
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definition:GUIDELINE_DEFINIITON
terminology:TERMINOLOGY
GUIDELINE_DEFINIITON
GUIDELINE
AUTHORED_RESOURCE
TOP
LEVEL](https://image.slidesharecdn.com/guideline-basedcdssforcovid-19-200724102459/85/Guideline-based-CDSS-for-COVID-19-18-320.jpg)
![Rule validation
The rules were validated with patient data adopted from a published patient case report[1].
[1] First case of 2019 novel coronavirus in the United States. N Engl J Med 2020;382(10):929–936](https://image.slidesharecdn.com/guideline-basedcdssforcovid-19-200724102459/85/Guideline-based-CDSS-for-COVID-19-24-320.jpg)
Guideline based CDSS for COVID-19
- 1. Dr. Shan Nan (nanshan@zju.edu.cn) On behalf of all the team members Zhejiang University 2020.07.24 Guideline-based CDSS for COVID-19
- 2. Me and my group Shan Nan Ph.D • PhD of Biomedical Engineering • Post Doc in Zhejiang University. • Experienced on CIG and rule-based CDSS Medical Informatics Group, the earliest medical informatics research group in China
- 3. The openEHR team Prof. Huilong Duan OpenEHR Ambassador Prof. Xudong Lu OpenEHR Ambassador Tianhua Tang PhD Student Mengyang Li PhD Student Hongshuo Feng MSc Student OpenEHR team with Dr. Rong Chen, the inventor of GDL Yijie Wang Informaticist Dr. Heather Leslie External consultant Kuai Yu Software Engineer
- 4. Motivations
- 5. COVID-19 global crisis Informaticists are obligatory to take actions!
- 6. Efforts in informatics communities Evidence-based CDSS and knowledge base have not been reported
- 7. Timeline of the Chinese guideline 7th Edition Mar. 14 6th Edition Feb. 19 5th Edition Feb. 3 4th Edition Jan. 27 3rd Edition Jan. 23 2nd Edition Jan. 22 1st Edition Jan. 16 First Case Dec. 26 Under Control Mar. 23
- 8. Sharing the COVID-19 knowledge • Different Language • Narrative guidelines needs proper interpretation • Difficult to integrate with heterogeneous information systems https://gmcc.alibabadoctor.com/prevention-manual
- 9. OpenEHR approaches have the potential 1500 people 87 countries
- 10. Info model and a formal guideline language OpenEHR archetypes make the information model easy to understand, reusable and sharable The Guideline Definition Language provides a formal way to computerize narrative guidelines
- 11. Our mission • Specify a guideline acquisition approach using openEHR • Develop and disseminate a computer interpretable guideline for COVID-19 • Implement CDS applications for COVID-19
- 12. COVID-19 CIG R&D
- 13. A rapid guideline acquisition approach We used openEHR archetypes and Guideline Definition Language to capture the knowledge in guidelines and rapidly developed CDSS applications.
- 14. A collaboration platform to manage the KA
- 15. Extract IF-THEN relations We extracted the Diagnostic Criteria, Clinical Classification, Clinical Warning Sign, Differential Diagnosis, Treatment, and Discharge sections in the guideline.
- 16. Extract entities Entities in each sentence were marked up and extracted. Then they are mapped to openEHR concepts.
- 17. Elicit COVID-19 related archetypes Li M, Leslie H, Qi B, Nan S, Feng H, Cai H, Lu X, Duan H, Development of an openEHR Template for COVID- 19 Based on Clinical Guidelines, JMIR, Vol22, No 6 (2020): June. https://www.jmir.org/2020/6/e20239/ Collect data elements from guideline https://ckm.openehr.org/ckm/templates/1013.26.291
- 18. Author rules in GDL2 Editor RESOURCE_DESCRIPTION original_author:Hash<string,,string>[1] other_contributors:string[0..*] lifecycle_state:terminology_code[1] details:DETAIL other_details:Hash<string,string>[0..1] DETAIL language: terminology_code[1] purpose:string[1] keywords:string[0..*] use:string[0..1] misuse:string[0..1] original_resource_url:Hash<string,,string>[0..1] other_details:Hash<string,,string>[0..1] copyright:string[0..1] original_language:terminology_code[1] descrption:RESOURCE_DESCRIPTION data_bindings:DATA_BINDING pre_conditions:ASSERTION rules:RULE templates:OUTPUT_TEMPLATE default_actions:ASSIGNMENT[*] model_id:string[1] template_id:string[0..1] predicates:ASSERTION[*] type:string[1] elements:ELEMENT DATA_BINDING id:string[1] when:ASSERTION[*] then:ASSIGNMENT[*] priority:integer[1] RULE id:string[1] name:string[1] model_id:string[1] template_id:string[0..1] object:OBJECT OUTPUT_TEMPLATE id:string[1] path:string[1] ELEMENT term_definitions:Hash<string,hash<string,TERM>>[1] term_bindings:Hash<string,TREM_BINDING>[0..1] TERMINOLOGY code:string[1] text:string[1] description:string[1] other_items:Hash<string,string>[0..1] TERM code:string[1] TERM_BINDING gdl_version:string[0..1] id:string[1] concept:string[1] definition:GUIDELINE_DEFINIITON terminology:TERMINOLOGY GUIDELINE_DEFINIITON GUIDELINE AUTHORED_RESOURCE TOP LEVEL
- 20. Import archetypes 27 archetypes were imported here.
- 21. Data binding
- 22. Edit a list of rules E.g. if CT image report contains ground glass opacities, patchy shadows, interstitial changes, consolidation, or infiltration, the patient has a COVID-19 risk factor.
- 23. Logical structure of the GDL rule set COVID-19 Diagnosis & Treatment Discharge Yes Yes Critical case classification Clinical_Warning.v0 Classfication.v0 Suspect cases diagnosis White_cell_count.v0 Lymphocyte_count.v0 Suspected_Diagnosis.v0 Yes No Exclude No No General treatment General_Treatment.v0 Diagnosis IgG_Antibody_Detection.v0 IgM_Antibody_Detection.v0 Confirmed_Diagnosis.v0 Discharge criteria Out_Hosipital.v0 Body_Temperature_Monitor.v0 Nucleic_acid_test_result.v0 Supportive treatment Respiratory_support_Treatment.v0 Circulation_support_Treatment.v0 Convalescent_plasma_Treatment.v0 Blood_Purification_Treatment.v0 Continuous_Renal_Replacement_Therapy.v0 Other_Treatment.v0 No Yes Totally 18 GDL rules have been authored.
- 24. Rule validation The rules were validated with patient data adopted from a published patient case report[1]. [1] First case of 2019 novel coronavirus in the United States. N Engl J Med 2020;382(10):929–936
- 25. Disseminate and share the rules Templates and rules were shared in GitHub and announced in openEHR disclosure https://discourse.openehr.org/t/chinese-covid-19-diagnosis-and- treatment-decision-support-openehr-templates-and-rules/516 https://github.com/ZJU-BME-VICO/openEHR-COVID-19 S Nan, T Tang, H Feng, Y Wang, M Li, X Lu, H Duan, Rapid Development and Dissemination of a Computer Interpretable Guideline for COVID-19, JMIR, under revision
- 27. Clinical requirements Although COVID-19 has been largely under control in China since March 2020, small scale breakouts have still been reported in Jilin, Guangdong, Beijing and most recently, Xinjiang. COVID-19 screening in the hospital is still required for a certain period of time. Scenario 2: Screening susceptive cases in management dept. in hospital Scenario 1: COVID-19 diagnosis at fever/Respiratory/Infection clinics GDL rules
- 28. Implementation framework We use a CDS platform (named Tracebook) to build CDS applications by configuring rules and UI components rather than coding from scratch. GDL can be applied here UI can be designed here
- 29. Mapping between GDL and Drools A GDL Rule A Drools Rule Archetypes => Object Data Model GDL Expression => MVEL Unfortunately, there is currently a lack of open source/free GDL execution engine…
- 30. Data model mapping Category openEHR Archetype Object Data Model Demographic openEHR-EHR-OBSERVATION.age.v0 PatientInfo Epidemic History openEHR-EHR-OBSERVATION.exposure_assessment.v0 EpidemicHistory Medical Record openEHR-EHR-OBSERVATION.pf_ratio.v0 MedicalRecord openEHR-EHR-OBSERVATION.story.v1 Exam openEHR-EHR-CLUSTER.imaging_finding.v0 ImgExamResultopenEHR-EHR-CLUSTER.imaging_result-COVID_19.v0 openEHR-EHR-OBSERVATION.imaging_exam_result.v0 Vital Sign openEHR-EHR-CLUSTER.inspired_oxygen.v1 PhysicalSign openEHR-EHR-CLUSTER.level_of_exertion.v0 openEHR-EHR-CLUSTER.problem_qualifier.v1 openEHR-EHR-OBSERVATION.body_temperature.v2 openEHR-EHR-OBSERVATION.pulse_oximetry.v1 openEHR-EHR-OBSERVATION.respiration.v2 Laboratory Test openEHR-EHR-CLUSTER.specimen.v0 LabTestResultopenEHR-EHR-CLUSTER.laboratory_test_analyte.v1 openEHR-EHR-OBSERVATION.laboratory_test_result.v1 Symptom openEHR-EHR-CLUSTER.symptom_sign.v1 Symptom openEHR-EHR-COMPOSITION.encounter.v1 openEHR-EHR-OBSERVATION.symptom_sign_screening.v0 openEHR-EHR-OBSERVATION.condition_screening.v0 Diagnosis openEHR-EHR-EVALUATION.differential_diagnoses.v0 DiagnosisopenEHR-EHR-EVALUATION.health_risk.v1 openEHR-EHR-EVALUATION.problem_diagnosis.v1 Order openEHR-EHR-EVALUATION.recommendation.v1 Order openEHR-EHR-INSTRUCTION.medication_order.v2 openEHR-EHR-INSTRUCTION.therapeutic_order.v0 openEHR-EHR-OBSERVATION.management_screening.v0
- 31. Rule mapping Guideline Section GDL Rule Drools Rule (5) Diagnostic Criteria COVID_Confirmed_Diagnosis.v0.gdl2 Diagnosis_Confirmed COVID_Lymphocyte_count.v0.gdl2 COVID_Nucleic_acid_test_result.v0.gdl2 COVID_White_blood_cell_count.v0.gdl2 COVID_White_cell_count.v0.gdl2 (6) Clinical Classification COVID_Classfication.v0.gdl2 Classification (7) Clinical Warning Sign COVID_Clinical_Warning.v0.gdl2 Clinical_Warning (8) Differential Diagnosis COVID_Suspected_Diagnosis.v0.gdl2 Diagnosis_Suspected (10) Treatment COVID_Blood_Purification_Treatment.v0.gdl2 Treatment_Modern COVID_Circulation_support_Treatment.v0.gdl2 COVID_Continuous_Renal_Replacement_Therapy.v0.gdl2 COVID_Convalescent_plasma_Treatment.v0.gdl2 COVID_General_Treatment.v0.gdl2 COVID_Immunotherapy.v0.gdl2 COVID_Other_Treatment.v0.gdl2 COVID_Respiratory_support_Treatment.v0.gdl2 (11) Discharge COVID_Body_Temperature_Monitor.v0.gdl2 Discharge COVID_Out_Hosipital.v0.gdl2 *For section 5, 10, and 11, there are multiple GDL rules for one section. This is because GDL2 Editor now only allows one rule in a file, whereas these sections contain several rules.
- 32. Deployment and trial plan Diagnosis and treatment CDSS for clinics CDSS for screening susceptive cases • These applications are currently being deployed in PLA Central Theater General Hospital. • An observational before-after study is prepared to validate the effectiveness of the CDS. • It is expected that the miss diagnose rate of susceptive cases and confirmed cases can be decreased.
- 33. Discussions
- 34. What do we benefit from openEHR • Using archetypes saved our time and lifted our burden on understanding data items while developing the CIG. • Most archetypes in CKM can be used directly. • Totally 27 archetypes have been used for the COVID-19 CIG, among which 26 were directly acquired from CKM and 1 (openEHR-EHR- CLUSTER.imaging_result-COVID_19.v0) was acquired from CKM and modified for the COVID-19 CDS purpose. • GDL and its editor helped us to develop and test CIGs without coding.
- 35. What are yet not perfect in openEHR • Lack of an open source or free GDL execution engine. • GDL2 Editor currently has 2 limitations. • Data types such as DV_PROPORTION and DV_DURATION are not fully supported in the editor. • Difficult to support collection data type (e.g. array, set). Not yet supported while executing
- 36. Proposal to develop an open source tool Using the experience gained from COVID-19 CIG and CDSS development, we are currently working on developing an openEHR EL based rule editor and execution engine. We plan to make it open source in the future.
- 37. ➢ Evidence-based CDSS and knowledge are required while fighting with COVID-19. ➢ We proposed an approach to use openEHR archetypes and GDL, and developed a CIG. ➢ Two CDS applications are under deployment in real clinical settings. Wrap up
- 38. Thanks!