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Enabling Clinical Data Reuse with openEHR
Data Warehouse Environments
Luis Marco-Ruiz, Pablo Pazos Gutiérrez, Koray Atalag, Johan Gustav Bellika, Kassaye Yitbarek
Yigzaw
2
Agenda1. Background
1.1. Learning Healthcare System1.2. Semantic Interoperability1.3. Linkage EHR – Inference models
2. METL2.1. Modelling2.2. Extract2.3. Transform2.4. Load
3. Experiences3.1. Laboratory Service at University Hospital North Norway3.2. NZ Cardiac Registry3.3. Path based queries
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Background – Limitations of EBM• Since its inception EBM has improved healthcare
outcomes by “collating studies, setting methodologies and publication standards, developing reasons and courses for technical appraisal and building new knowledge bases to be implemented in routine care”[1]
• However, some factors like the over-influence of industry in clinical research, the overwhelming amount of evidence in a form of scientific papers, the reduction of knowledge to algorithmic rules and the poor adoption to the individual patient needs have raised as EBM limitations
[1] T. Greenhalgh, J. Howick, N. Maskrey, and for the Evidence Based Medicine Renaissance Group, “Evidence based medicine: a movement in crisis?,” BMJ, vol. 348, no. jun13 4, pp. g3725–g3725, 2014.
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Background – The Learning Healthcare System
•In response to the limitations, the US Institute of medicine (IOM) summarized the pillars needed to overcome them in the proposal of a new healthcare paradigm named the Learning Healthcare System [1]:
– (a) fast progression of knowledge produced in clinical research to its use in routine clinical practice;
– (b) empowerment of a shared responsibility culture; – (c) present the notion of clinical data as a public asset;– (d) empower interoperability with Patient Health Records
(PHR) systems; – (e) facilitate public engagement of patients and doctors.
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Background – The Learning Healthcare System
•The LHS needs efficient data reuse mechanisms that allow to test hypothesis and confirm effects of interventions.
• Data need to flow from systems where originally were captured (EHRs, journals, LIS etc.) to systems that implement inference models (CDS, data analysis etc. )
• Need to find better mechanisms to improve accessibility and processing of clinical data for reuse
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Background – Ingredients for data reuse• Semantic Interoperability (SiOp)
– Latest efforts (Europe, US, Brazil, etc.) have established mechanisms to support the adoption of health interoperability standards
– Several standards available: openEHR, HL7 CDA, ISO 13606, FHIR
• Linkage of EHR with inference models– The ‘impedance mismatch’ between the information and inference model
needs to be resolved– Mechanisms are needed to rise the level of abstraction of the fine grained
data in the EHR to the abstract concepts referenced from inference models (medical logic or data analysis)
– Examples: DW, KDOM, Archetype layers, VMR etc.
• Data reuse pipeline infrastructure– An infrastructure must adequately implement the mechanisms to resolve
the impedance mismatch between EHR and inference models. It must ensure that data is appropriately updated, validated and accessible at the end of the pipeline for reuse.
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Semantic Interoperability
• Integration and harmonization of formats using health information standards– openEHR, HL7 CDA, CIMI, FHIR etc.
• Definition of shared information models and terminology binding– Several national and international initiatives:
Norwegian openEHR CKM, Spanish ISO13606 SOM, International openEHR CKM, CIMI etc.
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Linkage of EHR with inference models
• The ‘impedance mismatch’ between the information and inference model needs to be resolved
• Mechanisms to rise the level of abstraction of the fine grained data in the EHR to the abstract concepts referenced from inference models (medical logic or data analysis)
• Examples: DW, KDOM, Archetype layers, VMR etc.
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Linkage of EHR with inference models
SymptomName=coughTime= 6am-7am
Early morning cough
SymptomName=sputumcolor= salmon
Productive Early morning
cough
EH
RIn
fere
nce
If (Productive_early_morning_cough) then recommend X-ray
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Data reuse pipeline infrastructure• An infrastructure must adequately implement the
mechanisms to resolve the ‘impedance mismatch’ between EHR and inference models
• It must ensure that data is appropriately updated, validated and accessible at the end of the pipeline for reuse
• Transformation from proprietary to EHR standards is the most complex step
• The data model must be generic to ensure that the maximum reuse scenarios are covered
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ChallengeTo define a infrastructure that appropriately enables:
–To gather proprietary clinical data and transform it into standard compliant canonical form (ensures SiOp)
–To query data referencing standard defined clinical models independently from the underlying technological implementation
–To define different views of the openEHR data for different scenarios
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Agenda
1. Background1.1. Learning Healthcare System1.2. Semantic Interoperability1.3. Linkage EHR – Inference models
2. METL2.1. Modelling2.2. Extract2.3. Transform2.4. Load
3. Experiences3.1. Laboratory Service at University Hospital North Norway3.2. NZ Cardiac Registry3.3. Path based queries
METL
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METL
Modelling ExtractTransformLoad
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
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MET
L
Adapted from: Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
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Modelling
CKMClinical Model
Selection
Extension / Adaption
• Archetype reuse must be attempted checking national and international repositories to maximize the reuse of the data structure and queries
• Often CKM archetypes need to be extended to accommodate data reuse requirements (e.g. addition of demographical data*)
• The set of archetypes chosen must guarantee the highest level of reusability
• Archetypes should not be influenced by a particular reuse scenario
• Keep any new or extended Archetypes unconstrained as much as possible (e.g. do not bind value sets or set property units ranges etc.). Constraint at Template level to increase reuse.
• Keep archetypes containing fine grained properties and aggregate using the query languages to accommodate each reuse scenario
* The demographic model is not supported by current tools and demographic properties are modelled with the EHR information model
Set of archetype
s
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Extract
High level architectures for Extraction to DWExtraction of data in the Snow system
ExtractTraditional DW approach
EHR/Lab systems
data warehouse
In a traditional data warehouse, case data is stored both locally and centralized.
Has privacy / trust / autonomy issue
Decentralized approach
EHR/Lab systems / Health institutions
In a decentralized system, case data stay locally, summarized data can be stored centrally.
Avoids the privacy / trust / autonomy issuesThe Snow system is based on this decentralized approach
EHR /Lab production data
EHR/LAB database
Snow server
Snow exporter
Snow importer
exp
Snow dw
Filter
Automatic Extraction of data from local EHR/Lab systems
Transformation rulesData
Aggregater
Aggregated data---------------
Sample data from LIS system
Data Extraction from microbiology laboratories
TLS
Lab Snow serverPost Office Snow
server
Health networkHospital network
DMZ
XMPP Server Port: 5269
XMPP Client
Port 5222
”Retrieve4"
SSH: 22
Move CSV file
To $snow/data/in directory
Source: Snow dev team. Security policy: Pilot Deployment. Version 0.8. 2009
Snow coordination server
Stores aggregated data used to
produce a regional epidemiology
model
Automating Extraction – The Snow mission scheduler
23See further details in: J. G. Bellika, T. Henriksen, and K. Y. Yigzaw, “The Snow System – A Decentralized Medical Data Processing System,” in Data Mining in Clinical Medicine, vol. 1246, Spinger, 2014.
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MET
L
Adapted from: Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
25
Archetype-based DW in UNN - Transform<microlabresult> <id>2350459475284566896</id> <registrationDate>2013-02-24T12:56:00+01:00</registrationDate> <analysisDate>2013-02-25T15:35:20+01:00</analysisDate> <resultSentDate>2013-02-25T15:39:30+01:00</resultSentDate> <testRequesterId>68C17EC6</testRequesterId> <analysisName>Nasopharynx-Chlamydophila pneumoniae DNA</analysisName> <analysisType>VNX-CPP</analysisType> <originalTestResult>NEGATIV</originalTestResult> <material>Nasopharynx</material> <requesterMunicipalityCode>1905</requesterMunicipalityCode> <gender>K</gender> <patientMunicipalityCode>1902</patientMunicipalityCode> <patientId>18E8422AD</patientId> <patientBornYear>1972</patientBornYear> </microlabresult><microlabresult> <id>12769G4560JT284563452</id> <registrationDate>2013-02-24T12:56:00+01:00</registrationDate> <analysisDate>2013-02-25T15:35:20+01:00</analysisDate> <resultSentDate>2013-02-25T15:39:30+01:00</resultSentDate> <testRequesterId>68C17EC6</testRequesterId> <analysisName>H1N1 RNA</analysisName> <analysisType>VNX-H1N1</analysisType> <originalTestResult>NEGATIV</originalTestResult> <material>Nasopharynx</material> <requesterMunicipalityCode>1905</requesterMunicipalityCode> <gender>K</gender> <patientMunicipalityCode>1902</patientMunicipalityCode> <patientId>563G5E8443ER</patientId> <patientBornYear>1942</patientBornYear> </microlabresult>
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
Transform
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Transform• Data extracted must be transformed into
instances compliant with the archetypes defined in the MODELLING stage
• Constraints defined by the archetype must be kept
• Complex transformation mechanisms are needed
• Transformation from proprietary formats into openEHR compliant data is complex
Transform
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Transformations often needed when mapping from proprietary formats to openEHR:• Direct mappings
– If (gender==W) -> gender=1; – If (gender==M) -> gender=0;
• New node values inferred from the extracted data– If(infectiousAgent==ROTA-VIRUS)-> diseaseCategory=Gastrointestinal
• Grouping functions– “group all tests by request code; group all requests by patient id”
• Dependent from external sources*– Mappings that depend on external parties information (e.g. terminology servers, public available data)
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Archetype-based DW in UNN - Transform
<microlabresult> <id>2350459475284566896</id> <registrationDate>2013-02-24T12:56:00+01:00</registrationDate> <analysisDate>2013-02-25T15:35:20+01:00</analysisDate> <resultSentDate>2013-02-25T15:39:30+01:00</resultSentDate> <testRequesterId>68C17EC6</testRequesterId> <analysisName>Nasopharynx-Chlamydophila pneumoniae DNA</analysisName> <analysisType>VNX-CPP</analysisType> <originalTestResult>Test for VNX-CPP was NEGATIV</originalTestResult> <material>Nasopharynx</material> <requesterMunicipalityCode>1905</requesterMunicipalityCode> <gender>K</gender> <patientMunicipalityCode>1902</patientMunicipalityCode> <patientId>18E8422AD</patientId> <patientBornYear>1972</patientBornYear> </microlabresult>
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
Group fields by
test request
If (Nasopharynx-Chlamydophila
pneumoniae DNA)
Set 10652-6
If (gender=K)
Set gender=W
Transform
Direct mapping examples
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Canonical model from extraction
openEHR archetye
If (gender==W) gender=1If (gender==M) gender=0
Canonical model from extraction
openEHR archetye
If (testId==SOD_PLASM) testId=2951-2If (testId==PAP_RESULT) testId=19764-0
Transformation
New inferred values
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Canonical model from extraction
openEHR archetye
If (infectious_agent==FEC-ROTA)
Symptom_group=‘Gastrointestinal’
<microlabresult> <id>2350459475284566896</id> <registrationDate>2013-02-24T12:56:00+01:00</registrationDate> <analysisDate>2013-02-25T15:35:20+01:00</analysisDate> <resultSentDate>2013-02-25T15:39:30+01:00</resultSentDate> <testRequesterId>68C17EC6</testRequesterId> <analysisName>Rotavirus DNA</analysisName> <analysisType>ROTA-VIRUS</analysisType>
…</microlabresult>
Canonical model from extraction
openEHR archetye
If (infectious_agent==FEC-ROTA)
sub_category=‘Virus’
Transform
Grouping functions
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Group fields by
test request
<microlabresult> <id>2350459475284566896</id> <registrationDate>2013-02-24T12:56:00+01:00</registrationDate> <analysisDate>2013-02-25T15:35:20+01:00</analysisDate> <resultSentDate>2013-02-25T15:39:30+01:00</resultSentDate> <testRequesterId>68C17EC6</testRequesterId> <analysisName>Nasopharynx-Chlamydophila pneumoniae DNA</analysisName> <analysisType>VNX-CPP</analysisType> <originalTestResult>NEGATIV</originalTestResult> <material>Nasopharynx</material> <requesterMunicipalityCode>1905</requesterMunicipalityCode> <gender>K</gender> <patientMunicipalityCode>1902</patientMunicipalityCode> <patientId>18E8422AD</patientId> <patientBornYear>1972</patientBornYear> </microlabresult>
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Extract and Transform techs• Technologies available to extract and transform:– LinkEHR (archetype-based) Transform - Commercial– Pentaho Data Integration (Kettle) ETL - Open Source– Altova Mapforce (Mapping between models) ETL -
Commercial– Informatica - Commercial– …– Ad-hoc solutions (e.g. java + Drools)• Load needs to be ad-hoc: no commercial
openEHR connectors available
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Load
After transformation, openEHR extracts are interoperable with other openEHR systems
However, appropriate query mechanisms based on archetypes need to guarantee openEHR extracts availability and appropriate response times
Performing transformations on demand would not ensure efficient responses neither allow the appropriate filtering
An openEHR persistence platform needs to be loaded to enable queries
Such platform will allow the retrieval of the standard extracts any time with AQL
34
MET
L
Adapted from: Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
Load
35
AQ
L openEHR persistence platform
Extract server
OpenEHR compliant extract
OpenEHR compliant extract
OpenEHR compliant extract
Other openEHR sources
CDS
Clinical research
Public health
Surveillance
Adapter ( groups different archetypes into the emplates and submits them to the DW
using it’s API )
Transform
OpenEHR compliant extract
OpenEHR compliant extract
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LoadReconciliation of formats unveils the need a connectathon to test real SiOp
Load processes are long lasting (hours)
Load should be implemented as batch scheduled tasks that do not interfere the query load of the DW
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LoadIdeally the openEHR EXTRACT IM should be used to encapsulate compositions. This guarantees appropriate version control for data updates
However, the EXTRACT model has not catch on in industrial implementations and direct COMPOSITION serializations are used
Since data sources are not openEHR systems, even with the EXTRACT IM, versioning control would present challenges
Data updates of the DW must be carefully performed
38
Load
The load process can be treated as a global transaction
Global transactions not properly managed may incur in wrong inferences when querying the DW
The control of data validity across the whole pipeline is still an open issue
39
MET
L
Adapted from: Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
40
Query - AQL Section Data to be specified in the sectionSELECT Data elements to be returned and aggregation functions to use
over it
FROM EHR Id of the EHR to be queriedContainment Criteria
Archetype sections that need to be contained in the specified EHR
WHERE Criteria that needs to be applied to the result values in order to be returned
ORDER BY Order criteria to apply to the result setTIME WINDOW Date from which the specified data will be queried ignoring those
older
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
41
AQL - Query samples
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
SELECT o/data/events/data/items[at0078.13]/value AS WhiteCellCount FROM EHR[ehr_id=1ADC27]
CONTAINS COMPOSITION c [openEHR-EHR-COMPOSITION.encounter.v1]
CONTAINS OBSERVATION o [openEHR-EHR- OBSERVATION.lab_test_full_blood_count.v1] WHERE o/data/events/data/items[at0078.13]/value > 11000000000AND o/data/events/data/items[at0078.13]/value < 17000000000 TIME WINDOW P1Y/2014-02-12
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Advantages
• Modeling capabilities provided by openEHR standards
• Archetype vs. snowflake schema/OLAP cube
• Snowflake schemas or OLAP cubes would replicate modeling already validated by domain experts
• Queries are independent of the underlying infrastructure
43
Limitations
• Limited control over ETL stages. Global transactions need to be implemented.
• Synchronization and version control issues can arise when integrating several sources and deciding which entities need to be updated
• Load not rolled back will lead to wrong inferences
• Rules involving time cannot be easily implemented
44
Limitations• When very complex aggregations
(subquerying, constructs…) are needed AQL may not suffice
• Ontological representations and SPARQL could be an alternative but transformations openEHR - ontologies are very expensive [3,4]
[3] L. Lezcano, M.-A. Sicilia, C. Rodríguez-Solano, Integrating reasoning andclinical archetypes using OWL ontologies and SWRL rules, J. Biomed. Inform.44(April (2)) (2011) 343–353.
[4] J.T. Fernández-Breis, J.A. Maldonado, M. Marcos, M.D.C. Legaz-García, D.Moner, J. Torres-Sospedra, et al., Leveraging electronic healthcare recordstandards and semantic web technologies for the identification of patientcohorts, J. Am. Med. Inf. Assoc. JAMA (August 9) (2013)
45
Agenda
1. Background1.1. Learning Healthcare System1.2. Semantic Interoperability1.3. Linkage EHR – Inference models
2. METL2.1. Modelling2.2. Extract2.3. Transform2.4. Load
3. Use cases3.1. Laboratory Service at University Hospital North Norway3.2. NZ Cardiac Registry3.3. Path based queries
Use cases
Infectious diseases tests monitoring at University Hospital
of North Norway
• Population information for general practitioners (GPs) is usually limited by the patients they are assigned and their personal communications with colleagues
• They seldom have access to real time population test results or colleagues requests
• Access to anonymized and aggregated population data about laboratory interventions of other colleagues and laboratory personnel can empower their environmental awareness of communicable infectious diseases and help them to determine which set of tests should be ordered
48
Archetype-based DW at UNN - Introduction
• Laboratory test results of a population of 230,000 patients belonging to Troms and Finnmark counties in Norway requested between January 2013 and November 2014 were normalized to openEHR
• Test records normalization has been performed by defining transformation and aggregation functions to automatically generate openEHR compliant data.
• These data were loaded into an archetype-based data warehouse
49
Archetype-based DW at UNN - Introduction
• Indicators linked to the data in the warehouse to monitor test activity of Salmonella and Pertussis were defined with AQL
50
Archetype-based DW in UNN - Introduction
Laboratory test request = patient demographical data + requesters demographical data + tests batteryTest battery= 1..n individual tests to detect an infectious agent
51
Archetype-based DW at UNN - Introduction
Individual testidregistrationDateanalysisDateresultSentDatetestRequesterIdanalysisNameanalysisTypeoriginalTestResultmaterialrequesterMunicipalityCodegenderpatientMunicipalityCodepatientIdpatientBornYear
52
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
• Reuse was attempted checking the international openEHR CKM
• 2 possible candidates were identified– openEHR-EHR-OBSERVATION.lab _test.v1 – openEHR-EHR-OBSERVATION.lab test-microbiology.v1
• The need of demographical information and fields like infectious agent or symptom group forced the definition of new archetypes
53
Archetype-based DW at UNN - Modelling
Specialize
54
Archetype-based DW at UNN - Modelling
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
55
Archetype-based DW at UNN - Transformation
<microlabresult> <id>2350459475284566896</id> <registrationDate>2013-02-24T12:56:00+01:00</registrationDate> <analysisDate>2013-02-25T15:35:20+01:00</analysisDate> <resultSentDate>2013-02-25T15:39:30+01:00</resultSentDate> <testRequesterId>68C17EC6</testRequesterId> <analysisName>Nasopharynx-Chlamydophila pneumoniae DNA</analysisName> <analysisType>VNX-CPP</analysisType> <originalTestResult>The test was NEGATIV for VNX-CPP</originalTestResult> <material>Nasopharynx</material> <requesterMunicipalityCode>1905</requesterMunicipalityCode> <gender>K</gender> <patientMunicipalityCode>1902</patientMunicipalityCode> <patientId>18E8422AD</patientId> <patientBornYear>1972</patientBornYear> </microlabresult>
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
Transform
56
Transformations needed:• Direct mappings
• New node values inferred from the extracted data
• Grouping functions
Archetype-based DW at UNN - Transform
New inferred values
57
Canonical model from extraction
openEHR archetye
If (infectious_agent==FEC-ROTA)
Symptom_group=‘Gastrointestinal’
<microlabresult> <id>2350459475284566896</id> <registrationDate>2013-02-24T12:56:00+01:00</registrationDate> <analysisDate>2013-02-25T15:35:20+01:00</analysisDate> <resultSentDate>2013-02-25T15:39:30+01:00</resultSentDate> <testRequesterId>68C17EC6</testRequesterId> <analysisName>Rotavirus DNA</analysisName> <analysisType>FEC-ROTA</analysisType>
…</microlabresult>
Canonical model from extraction
openEHR archetye
If (infectious_agent==FEC-ROTA)
sub_category=‘Virus’
Archetype-based DW at UNN - Transform
Grouping functions
58
Group fields by
test request
<microlabresult> <id>2350459475284566896</id> <registrationDate>2013-02-24T12:56:00+01:00</registrationDate> <analysisDate>2013-02-25T15:35:20+01:00</analysisDate> <resultSentDate>2013-02-25T15:39:30+01:00</resultSentDate> <testRequesterId>68C17EC6</testRequesterId> <analysisName>Nasopharynx-Chlamydophila pneumoniae DNA</analysisName> <analysisType>VNX-CPP</analysisType> <originalTestResult>The test was NEGATIV for VNX-CPP</originalTestResult> <material>Nasopharynx</material> <requesterMunicipalityCode>1905</requesterMunicipalityCode> <gender>K</gender> <patientMunicipalityCode>1902</patientMunicipalityCode> <patientId>18E8422AD</patientId> <patientBornYear>1972</patientBornYear> </microlabresult>
Archetype-based DW at UNN - Transform
59
Extraction and caching
Legacy data extracts
Canonical extracts
XQuery transformatio
n script
EHREXE
Archetype
Transformation, Transformation, aggregation and aggregation and mapping rulesmapping rules
Canonical Data schema
References LinkEHR
Feeds Feeds
GeneratesFeeds
Gene
rate
s
OpenEHR compliant extract
Feeds
Compliant with
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
Archetype-based DW at UNN - Transform
Transformation
60
## Virus:# Sets the infectious agent based on the letters after the '-' in analysisType. This is implemented in a Java class.#rule "Gastrointestinal symptoms related to virus"
no-loop true salience 1when
dataModel : SampleData (analysisType == "FEC-ROTA" || analysisType == "FEC-ADNO" || analysisType == "FEC-NORO"|| analysisType == "OPP-NORO" || analysisType == "FEC-SAPO" || analysisType == "OPP-SAPO")
thendataModel.setSymptomGroup(SymptomGroup.GASTROINTESTINAL.getName());dataModel.setSubCategory("Virus");dataModel.setInfectiousAgentFromAnalysisTypeAddVirus();update(dataModel);
end
(/microlabresultscollection/microlabresult/analysisType = "FEC-ROTA") OR (/microlabresultscollection/microlabresult/analysisType = "FEC-ADNO") OR (/microlabresultscollection/microlabresult/analysisType = "FEC-NORO") OR (/microlabresultscollection/microlabresult/analysisType = "OPP-NORO") OR (/microlabresultscollection/microlabresult/analysisType = "FEC-SAPO") OR (/microlabresultscollection/microlabresult/analysisType = "OPP-SAPO")
Condition Mapping function
"Gastrointestinalt"
Drools rule
LinkEHR mapping
Target archetype attribute
Simple test Symptom group Value
(/microlabresultscollection/microlabresult/analysisType = "FEC-ROTA") OR (/microlabresultscollection/microlabresult/analysisType = "FEC-ADNO") OR (/microlabresultscollection/microlabresult/analysisType = "FEC-NORO") OR (/microlabresultscollection/microlabresult/analysisType = "OPP-NORO") OR (/microlabresultscollection/microlabresult/analysisType = "FEC-SAPO") OR (/microlabresultscollection/microlabresult/analysisType = "OPP-SAPO")
Simple test Subcategory Value
"Virus"
(/microlabresultscollection/microlabresult/analysisType = "FEC-ROTA") OR (/microlabresultscollection/microlabresult/analysisType = "FEC-ADNO") OR (/microlabresultscollection/microlabresult/analysisType = "FEC-NORO") OR (/microlabresultscollection/microlabresult/analysisType = "OPP-NORO") OR (/microlabresultscollection/microlabresult/analysisType = "FEC-SAPO") OR (/microlabresultscollection/microlabresult/analysisType = "OPP-SAPO")
Simple test Infectious agent Value
@concat(@substring-after(/microlabresultscollection/microlabresult/analysisType,"-"),"virus")
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Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
TransformArchetyp
e
Archetype-based DW at UNN - Load
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Archetype-based DW at UNN - Load• Load was performed sequentially for each patient calling the extracts service from the Transform
stage
• The extracts where simple COMPOSITION serializations. The EXTRACT IM has not been used
• Some differences between the seriations from the Transformation stage and the serializations accepted by the DW were found (namespaces, message wrapping…)
• Format reconciliation was needed
• The openEHR project Connectathon should guarantee openEHR tooling to interoperate seamlessly
• We defined several indicators to monitor infectious diseases (salmonella and pertussis)
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Archetype-based DW at UNN - Query
• After loading the DW we were able to query data creating different data sets for different scenarios
• As use case we defined several indicators to monitor infectious diseases (salmonella and pertussis)
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Archetype-based DW at UNN
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
65
SELECT count(o1/data[at0001]/events[at0002]/data[at0003]/items[at0022]) - - count (patientId)
FROM EHR e
CONTAINS COMPOSITION c
CONTAINS (OBSERVATION o1[openEHR-EHR-OBSERVATION.micro_lab_test.v1])
WHERE (o1/data[at0001]/events[at0002]/data[at0003]/items[at0010]/items[at0043]/items[at0036]/value='Kikhoste‘ and
o1/data[at0001]/events[at0002]/data[at0003]/items[at0010]/items[at0043]/items[at0037]/value='Positiv')
and o1/data[at0001]/events[at0002]/data[at0003]/items[at0024]/value >= '2013-01-04'
and o1/data[at0001]/events[at0002]/data[at0003]/items[at0024]/value < '2013-01-05'
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
AQL 1: Count positive tests of Pertussis for the day specified in the parameter
AQL 2: Salmonella cases in the specified municipality (same as patient just confirmed) in the first 2 weeks of January
SELECT count(o1/data[at0001]/events[at0002]/data[at0003]/items[at0022]/value) - - count (patientId)
FROM EHR e
CONTAINS COMPOSITION c
CONTAINS (OBSERVATION o1[openEHR-EHR-OBSERVATION.micro_lab_test.v1] and OBSERVATION o2[openEHR-EHR-OBSERVATION.micro_lab_test.v1])
WHERE (o1/data[at0001]/events[at0002]/data[at0003]/items[at0010]/items[at0043]/items[at0036]/value='Salmonella‘ and
o1/data[at0001]/events[at0002]/data[at0003]/items[at0010]/items[at0043]/items[at0037]/value='Positiv')
and o1/data[at0001]/events[at0002]/data[at0003]/items[at0020]/value='1917'
and o1/data[at0001]/events[at0002]/data[at0003]/items[at0024]/value >= '2013-01-01'
and o1/data[at0001]/events[at0002]/data[at0003]/items[at0024]/value < '2013-01-15'
Archetype-based DW at UNN
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Archetype-based DW at UNNWork available at:
Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG,Archetype-based data warehouse environment to enable the reuse of electronic health record data, International Journal of Medical Informatics (2015), http://dx.doi.org/10.1016/j.ijmedinf.2015.05.016
Special thanks to:This work was supported by Helse Nord [grant HST1121-13 and 9057/HST1120-13]; the NILS Science and Sustainability Programme [grant number 005-ABEL-IM-2013] from Iceland, Liechtenstein and Norway through the EEA Financial Mechanism, operated by Universidad Complutense de Madrid; and by the Spanish Ministry of Economy and Competitiveness [grant PTQ-12-05620]. We would like to thank to Marand d.o.o. and Torje S. Henriksen for the products provided and their assistance and support during this work. We would like to acknowledge Gunnar Skov Simonsen and Marit Wiklund at the microbiology laboratory service of the University Hospital of North Norway for their supportfor this work.
Recent developments and future plans
• Additional microbiology labs have joined Snow• Complete coverage of Northern Norway• Soon partly coverage of whole Norway • Tasks involved in setting up a new laboratory:
– Establishing network connection– Setting up a physical / virtual Snow Server– Defining laboratory analysis code mapping rules– Initiating data extracts– Defining data import transformations– Setting up Snow data consumption missions for epidemiology
model generation– Preparing visualisation of epidemiology model data
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68
Future plans – distributed analysis of OpenEHR data using secure multiparty
computations
27.05.15
More details available in: •M. A. Hailemichael, L. Marco-Ruiz, and J. G. Bellika, “Privacy-preserving Statistical Query and Processing on Distributed OpenEHR Data,” Stud Health Technol Inform, vol. 210, pp. 766–770, 2015. •Meskerem Asfaw Hailemichael, Kassaye Yitbarek Yigzaw, Johan Gustav Bellika (2015). Emnet: a System for Privacy-Preserving Statistical Computing on Distributed Health Data, SHI 2015, Proceedings from The 13th Scandinavien Conference on Health Informatics, June 15–17, 2015, Tromsø, Norway http://www.ep.liu.se/ecp_article/index.en.aspx?issue=115;article=006
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• A two-phase solution1. Dataset creation2. Statistical computation
27.05.15 70
OpenEHR in Norway• The current strategic plan of Norwegian
Health authorities is encouraging EHR vendors to adopt openEHR1
– DIPS ASA, which is the provider of more than 70% of hospital EHRs in Norway, is using OpenEHR2
• Norwegian CKM
1 Ellingsen G, Christensen B, Silsand L. Developing Large-scale Electronic Patient Records Conforming to the openEHR Architecture. Procedia Technology. 2014;16:1281–6.“
2 http://www.dips.no/eng/about-us/customers?lang=eng
Dataset creation
27.05.15 71
openEHR Data1
openEHR Data2
Researcher
Hospital 1
Hospital 2
Hospital 3Coordinator
AQL AQLAQL
AQL
The EHRs are OpenEHR based
openEHR Data3
Web-klient
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Statistical computation
27.05.15 73
Hospital1
Hospital2
Hospital3
Researcher Coordinator
Query QueryQuery
Query
ResultResult Secure multi-party
computation (SMC)
74
Architecture
75
coordinator
Relevant publications• L. Marco-Ruiz, D. Moner, J. A. Maldonado, N. Kolstrup, and J. G. Bellika, “Archetype-based
data warehouse environment to enable the reuse of electronic health record data,” International Journal of Medical Informatics, vol. 84, no. 9, pp. 702–714, Sep. 2015.
• J. G. Bellika, T. Henriksen, and K. Y. Yigzaw, “The Snow System – A Decentralized Medical Data Processing System,” in Data Mining in Clinical Medicine, vol. 1246, Spinger, 2014.
• M. A. Hailemichael, L. Marco-Ruiz, and J. G. Bellika, “Privacy-preserving Statistical Query and Processing on Distributed OpenEHR Data,” Stud Health Technol Inform, vol. 210, pp. 766–770, 2015.
• Meskerem Asfaw Hailemichael, Kassaye Yitbarek Yigzaw, Johan Gustav Bellika (2015). Emnet: a System for Privacy-Preserving Statistical Computing on Distributed Health Data, SHI 2015, Proceedings from The 13th Scandinavien Conference on Health Informatics, June 15–17, 2015, Tromsø, Norway http://www.ep.liu.se/ecp_article/index.en.aspx?issue=115;article=006 (accessed 8/18/2015)
• J. G. Bellika, T. Hasvold, and G. Hartvigsen, “Propagation of program control: a tool for distributed disease surveillance,” Int J Med Inform, vol. 76, no. 4, pp. 313–29, 2007.
• J. G. Bellika, H. Sue, L. Bird, A. Goodchild, T. Hasvold, and G. Hartvigsen, “Properties of a federated epidemiology query system,” Int J Med Inform, vol. 76, no. 9, pp. 664–76, 2007.
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Agenda
1. Background1.1. Learning Healthcare System1.2. Semantic Interoperability1.3. Linkage EHR – Inference models
2. METL2.1. Modelling2.2. Extract2.3. Transform2.4. Load
3. Use cases3.1. Laboratory Service at University Hospital North Norway3.2. NZ Cardiac Registry3.3. Path based queries
ANZACS-QI* openEHR Modelling for
DatawarehousingKoray AtalagJane Farris
*All NZ Acute Coronary Syndrome Quality Improvement programme
National clinical registry for acute coronary syndrome (ACS) events and cardiac procedures
ANZACS-QI Wiki: Created by Johan Strydom – Aug 2014
Current Architecture
Current Situation• Flat files transferred from Enigma• Heavily dependent on Data Dictionary
for meaning (Word & Excel files)• No view ‘across’ datasets• Requirement for extensive clinical input
for report development and on-going support
Future State
What is a Content Model?• IT IS A REFERENCE LIBRARY - for enabling
consistency in HIE Payload• Superset of all clinical dataset definitions
– normalised using a standard EHR record organisation (openEHR)
– Expressed as reusable and computable models – Archetypes
• Top level organisation follows CCR• Further detail provided by:
– Existing relevant sources (CCDA, Nehta, epSoS, HL7 FHIR etc.)
– Extensions (of above) and new Archetypes (NZ specific)• Each HIE payload (CDA) will correspond to a subset
(and conform)
Usage of the Content Model
Exploiting Content Model for Secondary Use
Single Content Model
CDA
FHIR
HL7 v2/3
EHR Extract
UML
XSD/XMI
Mindmap
PAYLOAD
System A
Data Source A
MapTo
Content Model
System B
Data Source B
Native openEHR Repository
Secondary Use
MapTo
Content Model
Automated Transforms
No Mapping
Atalag K. Using a single content model for eHealth interoperability and secondary use. Stud Health Technol Inform. 2013;193:282–96
A Canonical Model using National Standards
ACS Cathlab
Device (PCI)
Content Model Subject Areas – Health Information Exchange Content Model Architecture Building Block – HISO 10040.2
View of the EHRFrom an ACS viewpoint
Overview of ANZACS-QI Models
Benefits• Single point of reference
– Faithful representation of the ‘forms’– Standards based
• Extensible• Flexible• Reusable
– Clear and unambiguous data definition– Enables single source metadata management
• Data Dictionary• Rules within and between forms• Rules to other data sources (e.g. Linking datasets)
– Export for reuse– Holds the clinical viewpoint – ACS part of the
EHR
Future: Shared Health Information Platform (SHIP)
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Agenda
1. Background1.1. Learning Healthcare System1.2. Semantic Interoperability1.3. Linkage EHR – Inference models
2. METL2.1. Modelling2.2. Extract2.3. Transform2.4. Load
3. Use cases3.1. Laboratory Service at University Hospital North Norway3.2. NZ Cardiac Registry3.3. Path based queries
Path-based queries in action
Test available in EHRServerhttps://cabolabs-ehrserver.rhcloud.com/ehr-0.3/query/list
EHRServer Query Builder
Path-based queries in action
{ "uid": "9c5da334-4b81-4d60-92e2-aa96a722b4ac", "name": "Documents with high BP", "format": "xml", "type": "composition", "criteriaLogic": "OR", "criteria": [ { "archetypeId": "openEHR-EHR-OBSERVATION.blood_pressure.v1", "path": "/data[at0001]/events[at0006]/data[at0003]/items[at0004]/value", "conditions": { "magnitude": { "gt": [ 140 ] }, "units": { "eq": "mm[Hg]" } } }, { "archetypeId": "openEHR-EHR-OBSERVATION.blood_pressure.v1", "path": "/data[at0001]/events[at0006]/data[at0003]/items[at0005]/value", "conditions": { "magnitude": { "gt": [ 90 ] }, "units": { "eq": "mm[Hg]" } } } ]}
Path-based: + Get clinical documents (compositions) + With high BP
JSON expression of EHRServer
queries
Path-based queries in actionResults: + in XML (or JSON if specified on the query or as a parameter) + just the index, no data, can get a specific document using the index
<?xml version="1.0" encoding="UTF-8"?><list> <compositionIndex id="8"> <archetypeId>openEHR-EHR-COMPOSITION.signos.v1</archetypeId> <category>event</category> <dataIndexed>true</dataIndexed> <ehrId>11111111-1111-1111-1111-111111111111</ehrId> <lastVersion>true</lastVersion> <startTime>2015-08-14 03:06:44.0 EDT</startTime> <subjectId>11111111-1111-1111-1111-111111111111</subjectId> <templateId>Signos</templateId> <uid>e152b2c2-7dbe-44b6-9ec6-2cd698561140</uid> </compositionIndex> <compositionIndex id="9"> <archetypeId>openEHR-EHR-COMPOSITION.signos.v1</archetypeId> <category>event</category> <dataIndexed>true</dataIndexed> <ehrId>11111111-1111-1111-1111-111111111111</ehrId> <lastVersion>true</lastVersion> <startTime>2015-08-14 03:07:06.0 EDT</startTime> <subjectId>11111111-1111-1111-1111-111111111111</subjectId> <templateId>Signos</templateId> <uid>f0a8d192-0f68-4501-8373-f954a47a7385</uid> </compositionIndex> ... </list>
Path-based queries in action{ "uid": "70764d85-4e4b-4548-8f71-3a294f35e704", "name": "Vital Signs", "format": "json", "type": "datavalue", "group": "path", "projections": [ { "archetypeId": "openEHR-EHR-OBSERVATION.blood_pressure.v1", "path": "/data[at0001]/events[at0006]/data[at0003]/items[at0004]/value" }, { "archetypeId": "openEHR-EHR-OBSERVATION.blood_pressure.v1", "path": "/data[at0001]/events[at0006]/data[at0003]/items[at0005]/value" }, { "archetypeId": "openEHR-EHR-OBSERVATION.body_temperature.v1", "path": "/data[at0002]/events[at0003]/data[at0001]/items[at0004]/value" }, { "archetypeId": "openEHR-EHR-OBSERVATION.body_weight.v1", "path": "/data[at0002]/events[at0003]/data[at0001]/items[at0004]/value" }, { "archetypeId": "openEHR-EHR-OBSERVATION.pulse.v1", "path": "/data[at0002]/events[at0003]/data[at0001]/items[at0004]/value" }, { "archetypeId": "openEHR-EHR-OBSERVATION.respiration.v1", "path": "/data[at0001]/events[at0002]/data[at0003]/items[at0004]/value" } ] }
Path-based: + Get clinical data for all vital signs measures + Result in JSON format, grouped by path (type of data)
JSON expression of EHRServer
queries
Q & A
Contact:Luis Marco [email protected] Pazos [email protected],
Koray Atalag [email protected], Johan G. Bellika [email protected],
Kassaye Y. Yigzaw [email protected]