Enabling Clinical Data Reuse with openEHR Data Warehouse Environments Luis Marco-Ruiz, Pablo Pazos Gutirrez, Koray Atalag, Johan Gustav Bellika, Kassaye Yitbarek Yigzaw 2 Agenda 1.Background 1.1. Learning Healthcare System 1.2. Semantic Interoperability 1.3. Linkage EHR Inference models 2.METL 2.1. Modelling 2.2. Extract 2.3. Transform 2.4. Load 3.Experiences 3.1. Laboratory Service at University Hospital North Norway 3.2. NZ Cardiac Registry 3.3. Path based queries 3 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. g3725g3725, 2014. 4 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. 5 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 6 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. 7 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. 8 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. 9 Linkage of EHR with inference models Symptom Name=cough Time= 6am-7am Early morning cough Symptom Name=sputum color= salmon Productive Early morning cough EHR Inference If (Productive_early_morning_cough) then recommend X-ray 10 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 11 Challenge To 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 12 Agenda 1.Background 1.1. Learning Healthcare System 1.2. Semantic Interoperability 1.3. Linkage EHR Inference models 2.METL 2.1. Modelling 2.2. Extract 2.3. Transform 2.4. Load 3.Experiences 3.1. Laboratory Service at University Hospital North Norway 3.2. NZ Cardiac Registry 3.3. Path based queries METL 14 METL M odelling E xtract T ransform L oad 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), 15 METL 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), 16 M odelling CKM Clinical 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 archetypes 17 E xtract High level architectures for Extraction to DW Extraction of data in the Snow system E xtract Traditional 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 issues The 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 rules Data Aggregater Aggregated data Sample data from LIS system Data Extraction from microbiology laboratories Source: Snow dev team. Security policy: Pilot Deployment. Version Snow coordination server Stores aggregated data used to produce a regional epidemiology model Automating Extraction The Snow mission scheduler 23 See 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. 24 METL 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), 25 Archetype-based DW in UNN - Transform T12:56:00+01: T15:35:20+01: T15:39:30+01:00 68C17EC6 Nasopharynx-Chlamydophila pneumoniae DNA VNX-CPP NEGATIV Nasopharynx 1905 K E8422AD G4560JT T12:56:00+01: T15:35:20+01: T15:39:30+01:00 68C17EC6 H1N1 RNA VNX-H1N1 NEGATIV Nasopharynx 1905 K G5E8443ER 1942 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),Transform 26 T ransform 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 T ransform 27 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) 28 Archetype-based DW in UNN - Transform T12:56:00+01: T15:35:20+01: T15:39:30+01:00 68C17EC6 Nasopharynx-Chlamydophila pneumoniae DNA VNX-CPP Test for VNX-CPP was NEGATIV Nasopharynx 1905 K E8422AD 1972 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),Group fields by test request If (Nasopharynx- Chlamydophila pneumoniae DNA) Set If (gender=K) Set gender=W T ransform Direct mapping examples 29 Canonical model from extraction openEHR archetye If (gender==W)gender=1 If (gender==M)gender=0 Canonical model from extraction openEHR archetye If (testId==SOD_PLASM)testId= If (testId==PAP_RESULT)testId= T ransformation New inferred values 30 Canonical model from extractionopenEHR archetye If (infectious_agent==FEC-ROTA)Symptom_group=Gastrointestinal T12:56:00+01: T15:35:20+01: T15:39:30+01:00 68C17EC6 Rotavirus DNA ROTA-VIRUS Canonical model from extractionopenEHR archetye If (infectious_agent==FEC-ROTA)sub_category=Virus T ransform Grouping functions 31 Group fields by test request T12:56:00+01: T15:35:20+01: T15:39:30+01:00 68C17EC6 Nasopharynx-Chlamydophila pneumoniae DNA VNX-CPP NEGATIV Nasopharynx 1905 K E8422AD 1972 32 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 33 L oad 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 METL 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), L oad 35 AQL 36 L oad Reconciliation 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 37 L oad Ideally 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 L oad 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 METL 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), 40 Query - AQL SectionData to be specified in the section SELECTData elements to be returned and aggregation functions to use over it FROMEHRId of the EHR to be queried Containment Criteria Archetype sections that need to be contained in the specified EHR WHERECriteria that needs to be applied to the result values in order to be returned ORDER BYOrder criteria to apply to the result set TIME WINDOWDate 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), 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),SELECT o/data/events/data/items[at ]/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[at ]/value > AND o/data/events/data/items[at ]/value < TIME WINDOW P1Y/ 42 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. Rodrguez-Solano, Integrating reasoning and clinical archetypes using OWL ontologies and SWRL rules, J. Biomed. Inform. 44(April (2)) (2011) 343353. [4] J.T. Fernndez-Breis, J.A. Maldonado, M. Marcos, M.D.C. Legaz-Garca, D. Moner, J. Torres-Sospedra, et al., Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts, J. Am. Med. Inf. Assoc. JAMA (August 9) (2013) 45 Agenda 1.Background 1.1. Learning Healthcare System 1.2. Semantic Interoperability 1.3. Linkage EHR Inference models 2.METL 2.1. Modelling 2.2. Extract 2.3. Transform 2.4. Load 3.Use cases 3.1. Laboratory Service at University Hospital North Norway 3.2. NZ Cardiac Registry 3.3. 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 battery Test battery= 1..n individual tests to detect an infectious agent 51 Archetype-based DW at UNN - Introduction Individual test id registrationDate analysisDate resultSentDate testRequesterId analysisName analysisType originalTestResult material requesterMunicipalityCode gender patientMunicipalityCode patientId patientBornYear 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), 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), 55 Archetype-based DW at UNN - Transformation T12:56:00+01: T15:35:20+01: T15:39:30+01:00 68C17EC6 Nasopharynx-Chlamydophila pneumoniae DNA VNX-CPP The test was NEGATIV for VNX-CPP Nasopharynx 1905 K E8422AD 1972 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),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 extractionopenEHR archetye If (infectious_agent==FEC-ROTA)Symptom_group=Gastrointestinal T12:56:00+01: T15:35:20+01: T15:39:30+01:00 68C17EC6 Rotavirus DNA FEC-ROTA Canonical model from extractionopenEHR archetye If (infectious_agent==FEC-ROTA)sub_category=Virus Archetype-based DW at UNN - Transform Grouping functions 58 Group fields by test request T12:56:00+01: T15:35:20+01: T15:39:30+01:00 68C17EC6 Nasopharynx-Chlamydophila pneumoniae DNA VNX-CPP The test was NEGATIV for VNX-CPP Nasopharynx 1905 K E8422AD 1972 Archetype-based DW at UNN - Transform 59 Extraction and caching Legacy data extracts Canonical extracts XQuery transformation script EHR EXE Archetype Transformation, aggregation and mapping rules Canonical Data schema References LinkEHR Feeds GeneratesFeeds Generates 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),Archetype-based DW at UNN - Transform T ransformation 60 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), 61 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),Transform Archetype Archetype-based DW at UNN - Load 62 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) 63 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) 64 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), 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 >= ' ' and o1/data[at0001]/events[at0002]/data[at0003]/items[at0024]/value < ' ' 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),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 >= ' ' and o1/data[at0001]/events[at0002]/data[at0003]/items[at0024]/value < ' ' Archetype-based DW at UNN 66 Archetype-based DW at UNN Work 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),Special thanks to: This work was supported by Helse Nord [grant HST and 9057/HST ]; 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 ]. 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 support for 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 67 68 Future plans distributed analysis of OpenEHR data using secure multiparty computations 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. 766770, 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 1517, 2015, Troms, Norway 69 A two-phase solution 1.Dataset creation 2.Statistical computation OpenEHR in Norway The current strategic plan of Norwegian Health authorities is encouraging EHR vendors to adopt openEHR 1 DIPS ASA, which is the provider of more than 70% of hospital EHRs in Norway, is using OpenEHR 2 Norwegian CKM 1 Ellingsen G, Christensen B, Silsand L. Developing Large-scale Electronic Patient Records Conforming to the openEHR Architecture. Procedia Technology. 2014;16:12816. 2 Virtual dataset Dataset creation openEHR Data1 openEHR Data2 Researcher Hospital 1 Hospital 2 Hospital 3 Coordinator AQL The EHRs are OpenEHR based openEHR Data3 Web-klient 72 Virtual dataset Statistical computation Hospital 1 Hospital 2 Hospital 3 Researcher Coordinator Query Result 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. 702714, Sep 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, 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. 766770, 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 1517, 2015, Troms, Norway(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. 31329, 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. 66476, 77 Agenda 1.Background 1.1. Learning Healthcare System 1.2. Semantic Interoperability 1.3. Linkage EHR Inference models 2.METL 2.1. Modelling 2.2. Extract 2.3. Transform 2.4. Load 3.Use cases 3.1. Laboratory Service at University Hospital North Norway 3.2. NZ Cardiac Registry 3.3. Path based queries ANZACS-QI * openEHR Modelling for Datawarehousing Koray Atalag Jane 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 Atalag K. Using a single content model for eHealth interoperability and secondary use. Stud Health Technol Inform. 2013;193:28296 A Canonical Model using National Standards ACSCathlab Device (PCI) Content Model Subject Areas Health Information Exchange Content Model Architecture Building Block HISO View of the EHR From 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) 90 Agenda 1.Background 1.1. Learning Healthcare System 1.2. Semantic Interoperability 1.3. Linkage EHR Inference models 2.METL 2.1. Modelling 2.2. Extract 2.3. Transform 2.4. Load 3.Use cases 3.1. Laboratory Service at University Hospital North Norway 3.2. NZ Cardiac Registry 3.3. Path based queries Path-based queries in action Test available in EHRServer https://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 action Results: + 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 openEHR-EHR-COMPOSITION.signos.v1 event true true :06:44.0 EDT Signos e152b2c2-7dbe-44b6-9ec6-2cd openEHR-EHR-COMPOSITION.signos.v1 event true true :07:06.0 EDT Signos f0a8d192-0f f954a47a Path-based queries in action { "uid": "70764d85-4e4b f71-3a294f35e704", "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 Pablo Koray Johan G. Kassaye Y.