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A CDR implementation based on openEHR ARM persistence method
- 2. What is CDR ? Ø Definition of CDR • A data store that holds and manages clinical data collected from service encounters at point of service locations (e.g. hospitals, clinics) (from ISO/TR 20514，2005) Ø Widely implemented internationally • 46.7% Asia Pacific, 71.2% Middle East, 67.2% Canada, 94.8% America Ø Early adoption in China • 0.57% hospitals in China until 2014 Ø CDR is particularly important to support medical information interoperation in China
- 3. A real-time unified database of patient clinical information Clinical Data Repository Patient ServiceClinical Support Research LIS CPOEPACSHMIS EIS OIS Interoperability Integration Engine CIS CDR in Hospital
- 4. Clinicians Engineers Clinical Data Repository HIS LIS PACS …RIS EIS OIS Data Viewer Data Analytics Decision Support 。。。 Data Mining Gap I want to query the count of patient with CIK therapy plan Researchers I want to find the relationship between diseases and certain factors Patients I want to find the number of patients like me I want to integrate my new system to CDR Increasing requirements cannot be filled Developed software cannot be fully used The Gap between requirements and reality
- 5. Solution An Open Extensible Information Platform Let the people with data requirements retrieve & query data themselves I can configure a simple form for my request I can get the necessary data as input to analytics software I can query whatever I needed I can transfer the data to my own structure Clinical Data Repository HIS LIS PACS …RIS EIS OIS Clinicians EngineersResearchers Patients
- 6. openEHR Methodology ØTwo level model & Shared archetypes - CKM ØFlexibility and scalability of semantics interoperation ØA promising approach to build the CDR system
- 9. Data persistence xml database Basic serialization XML databaseNode+path 1. Performance slower than conventional RDB 2. Not suitable to answer complex query Take into consideration that almost all the hospitals in China adopt relational database, the relational database persistence with openEHR approach is necessary. Hybrid serialization
- 10. ARM – Archetype Relational Mapping Mapping archetypes into multiple relational database tables and mapping leaf nodes into table fields. (Instruction ) PK (Observation) PK (Evaluation) PK (Composition) PK
- 11. Principle of ARM Keep the same granularity of information at modeling and persistence. openEHR Model Entity Relationship Model Archetype Entity Archetype Attribute Entity Attribute Embedded CLUSTER Weak Entity Archetype Specialization Entity Inheritance SLOT Relationship LINK Relationship Multiple Occurrence Attribute Multiple Occurrence Attribute Identification Attribute Identification Attribute Computation Attribute Computation Attribute openEHR Model Concepts vs. Entity Relational Model Concepts Experts RDB
- 12. Straight • Basic data type attribute • Action archetype state machine • Basic archetype structure Boost • Identification attribute • Query attribute • Flattened attribute Tune • Propagated attribute • Singularized attribute • General/Specific archetype ARM data persistence rules Denormalization
- 13. Archetype-driven data persistence Rules for mapping archetype to entity Archetype Template ARM Config Class -status_Value -reservedOrder_Value -…… Class -memberName -memberName -…… Archetype Object Mapping （AOM） entity object Object Relational Mapping （ORM） Archetype relational Mapping （ARM） RDB + +
- 14. Performance evaluation Query IV (ms) ARM (ms) Node+Path (ms) Query 1.1 80 (+74%) 46 5017 Query 1.2 91 (+54%) 59 5121 Query 1.3 196 (+15%) 170 5358 Query 2.1 221 (+16%) 191 24866 Query 2.2 219 (+17%) 187 25094 Query 2.3 474 (+129%) 207 26158 Query 3.1 242 270 (+12%) 294774 Query 3.2 224 299 (+33%) 297388 Query 3.3 254 411 (+62%) 362950 Query 4.1 198 (+13%) 176 127547 Query 4.2 254 (+32%) 193 128508 Query 4.3 1249 (+57%) 797 129901 Query 5.1 113 186 (+65%) 328181 Query 5.2 125 205 (+64%) 329097 Query 5.3 139 239 (+72%) 388727 Query 6.1 14596 (+5150%) 278 5746 Query 6.2 16340 (+5293%) 303 6029 Query 6.3 16453 (+5140%) 314 6984 Query 7.1 14582 (+1028%) 1293 41217 Query 7.2 14649 (+669%) 1904 53352 Ø A comparison study among the conventional relational database, the generated ARM database and the Node + Path database. Ø Five data-retrieving tests (Query 1.1 - Query 5.3) Ø Two patient-searching tests (Query 6.1 - Query 7.2) Ø The ARM achieve similar performance as the conventional relational databases. Ø The Node + Path database requires far more time than the other two databases.
- 16. •AQL grammar •ANTLR grammar analyzer •Abstract grammar tree Grammar analysis •Archetype •Variable •Path Legality verification •Multiple SQLs Query execution •XML or ODIN •Gzip compression Result capsulation Archetype Query Language - AQL SELECT o/data[at0001]/events[at0006]/data[at0003]/items[at0004]/value, o/data[at0001]/events[at0006]/data[at0003]/items[at0005]/value FROM EHR e [openEHR-EHR-EHR.ehr.v1] CONTAINS COMPOSITION c [openEHR-EHR-COMPOSITION.encounter.v1] CONTAINS OBSERVATION o [openEHR-EHR-OBSERVATION.blood_pressure.v1] WHERE o/data[at0001]/events[at0006]/data[at0003]/items[at0004]/value >= 140 OR o/data[at0001]/events[at0006]/data[at0003]/items[at0005]/value >= 90 INSERT INTO OBSERVATION o [openEHR-EHR-OBSERVATION.blood_pressure.v1] VALUES o/uid/value = newUID(), o/data[at0001]/events[at0006]/data[at0003]/items[at0004]/value = 140, o/data[at0001]/events[at0006]/data[at0003]/items[at0005]/value = 90 UPDATE OBSERVATION o [openEHR-EHR-OBSERVATION.blood_pressure.v1] SET o/data[at0001]/events[at0006]/data[at0003]/items[at0004]/value = 140 WHERE o/data[at0001]/events[at0006]/data[at0003]/items[at0005]/value >= 90 DELETE FROM OBSERVATION o [openEHR-EHR-OBSERVATION.blood_pressure.v1] WHERE o/data[at0001]/events[at0006]/data[at0003]/items[at0004]/value >= 140 OR o/data[at0001]/events[at0006]/data[at0003]/items[at0005]/value >= 90
- 17. REST API Ø Auto generated REST API • GET, POST, PUT, DELETE with template uid or identification attribute as condition Ø REST API designer
- 18. Performance comparison Query serial number Records count API(ms) AQL(ms) 1 1 5 6 2 1 9 6 3 1 5 6 4 1 5 7 5 1 5 5 6 1 5 5 7 1 5 5 8 1 6 13 9 1 6 5 10 1 5 4 Average 1 5.6 6.2 Query serial number Records count API(ms) AQL(ms) 1 209 10 20 2 1209 21 71 3 2847 41 150 4 56 5 8 5 1221 19 72 6 1971 28 106 7 1337 24 74 8 7 5 5 9 279 15 20 10 532 15 33 Average 966.8 18.3 55.9 Retrieving patient information by patient identifier Retrieving image information by exam identifier The execution time is similar between AQL query and API query. On account of package for ODIN, the AQL average performance is little slower than API.
- 19. Archetype Model Information source Data Storage Medical Knowledge Data Requirement ARM rules Archetype Template Domain experts + UI layout Clinician experts Drag Drop Data binding Attributes edit Data entry UI Archetype-driven data application
- 20. archetype/template editor template archetype/ template database Application database create ARM expert AQL “WYSIWYG” editor General software framework Data application template design archetype Application development with user-control approach user application In order to achieve user-control application development, this study proposes archetype-driven approach using application template and general software framework.
- 22. • Archetype/ Template Editor • Archetype – relational mapping • AQL-based Data Access Interfaces • Archetype driven UI configuration Archetype-based CDR system – architecture
- 23. Ø Part of Project of “Medical Data Integration & Merging”, funded by Chinese National “863” Program, initiated in 2012. Ø Research Purpose: An methodology of implementing open and extensible CDR and a case study in a pilot hospital Shangxi Dayi Hospital with 2600 beds CDR Implementation in Chinese Hospital
- 24. CDR in Chinese Hospital - Architecture HIS LIS PACS …RIS EMR CIS Archetype template repository Data access service Diabetes follow-up Integrated data viewClinical decision support Research Data Query Quality Data analysis Data mining and analysis Application based on the CDR
- 25. CDR outcome 643665 34664534 1355345 14727482 32676 528600 115371 673619 760687 9236476 0 5000000 10000000 15000000 20000000 25000000 30000000 35000000 40000000 Data statistics Examination request Examination data Lab test request Lab test data Operation request Diagnosis Health examination Patient Encounter Order Ø 89 archetypes, 85 tables and 142 APIs. Ø Data volume (2012.8—2015.12) • Medical data 120G • 673619 patients and 760687 encounters
- 26. ARM used in Population Health Information Platform Overall Architecture (by ZJU & ZTEICT) Start from 2016, will be launched on Wuzhou, Guangxi in August
- 32. Ø openEHR is a promising methodology to provide a open and highly extensible solution for building CDR Ø The study show the feasibility of Archetype/Template driven approach. Ø More configurable applications such as decision tools, clinical quality management tools need to use archetypes as their data sources to achieve interoperability. What we learned from implementation?