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Standard-based integration profiles for clinical research and patient safety Christel Daniel, MD, PhD 1,2, Gokce Banu Laleci Erturkmen, PhD 3, Ali Anil.

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Presentation on theme: "Standard-based integration profiles for clinical research and patient safety Christel Daniel, MD, PhD 1,2, Gokce Banu Laleci Erturkmen, PhD 3, Ali Anil."— Presentation transcript:

1 Standard-based integration profiles for clinical research and patient safety Christel Daniel, MD, PhD 1,2, Gokce Banu Laleci Erturkmen, PhD 3, Ali Anil Sinaci, MSc 3, Brendan C Delaney, BM, BCh, MD 4, Vasa Curcin, PhD 5, Landen Bain 6 1 INSERM UMRS 872eq20, Paris, France ; 2 AP-HP, Paris France ; 3 Software Research, Development and Consultancy, Ankara, Turkey; 4 Kings College of London, UK; 5 Imperial College London, UK; 6 CDISC, USA 2013 Joint Summits on Translational Science 1

2 Integraton profiles for EHR-enabled research EHRs can now be adapted to integrate seamlessly with existing research platforms – Unique opportunity for many stakeholders, including hospitals, clinical research promoters, pharmaceutical industry and policy makers. – Key challenges, including security and semantic interoperability issues, need to be overcome in order to provide platforms that functions across many EHR systems. IHE Quality, Research and Public Health (QRPH) – Collaboration with CDISC’s Healthcare Link initiative – Set of integration profiles that specifically address EHR- enabled research Joint Summits on Translational Science2

3 Panelists Wayne Kubick – CDISC (US) CDISC’s Healthcare Link initiative & IHE QRPH Christel Daniel, MD, PhD – INSERM AP-HP (France) EHR4CR (Innovative Medicine Initiative (IMI)) Brendan Delaney, BM, BCh, MD – KCL (UK) TRANSFoRm (7th framework programme) Ali Anil Sinaci, MSc – SRDC (Turkey) SALUS (7th framework programme) 2013 Joint Summits on Translational Science 3

4 EHR-enabled research Use cases 2013 Joint Summits on Translational Science EHR4CRTRANSFoRmSALUS Use case #1: Identification of populations of patients based on pre-defined characteristics (eligibility criteria) Protocol feasibility study Patient recruitment Signal detection Use case #2: Extraction for a given patient of a set of individual clinical data predefined by a research protocol/reporting standard Case Report Form (CRF) pre-population Individual case safety report (ICSR) form pre-population Use case #3: Extraction for a given population identified based on pre- defined characteristics (use case #1) of sets of individual clinical data predefined by a research protocol (use case #2) Retrospective observational study Exploratory analysis study 4

5 Question How can we use standard-based IHE profiles to implement EHR-enabled use cases for clinical research and patient safety? 2013 Joint Summits on Translational Science 5

6 Standard-based integration profiles for clinical research and patient safety Wayne Kubick, Landen Bain CDISC’s Healthcare Link initiative & IHE QRPH 2013 Joint Summits on Translational Science 6

7 7 CDISC Standards for Research

8 8 Case Report Form EHR Clinical Research Quality Public Health Quality Measure Outbreak Report Integrating Workflow: EHRs and External Agencies 2013 Joint Summits on Translational Science

9 Linking Research and Healthcare Secondary Use of Big Data – OMOP, mini-Sentinel, I2B2 – Access and pool de-identified, past observational data – Across many subjects – Data mined for feasibility and hypothesis generation – Limited value for new research other than recruitment Primary Research – ASTER, RFD Study Execution – Pseudonymized live data – during patient encounters – One single subject at a time – Protocol driven recruitment and data capture – Limited value for feasibility or recruiting

10 Install Interoperable products in Clinical Settings worldwide 10 Demonstrate at a IHE Call for Proposals Opens Profile Selection by Committees months 1-5 months Publish in IHE’s Product Registry Test at IHE Connectathons IHE Profiles Drafted & Revised months 6-13 TrialImplementationPosted Published For Public Comment IHE Technical Framework Developed 2013 Joint Summits on Translational Science

11 QRPH Overview Overview IHE Quality, Research and Public Health Domain (QRPH) addresses the infrastructure and content necessary to: – Share information relevant to quality improvement in electronic patient care and health care records – Facilitate interoperability between the healthcare system and clinical research – Facilitate interoperability between the healthcare system and public health Sponsors – Healthcare Information and Management Systems Society (HIMSS) Healthcare Information and Management Systems Society (HIMSS) – Radiological Society of North America (RSNA) Radiological Society of North America (RSNA) History – Domain launched in Joint Summits on Translational Science

12 IHE Healthcare Link Profiles A set of profiles and standards to automate the clinical trial process flow of EHR-enabled platforms from patient recruitment thru clinical trial data capture: Clinical research protocol: – Retrieve Process for Execution (RPE) – Clinical Research Process Content (CRPC) – Proposed Research Matching – CDISC SDM-XML Clinical research documents (eCRF or adverse event reports) to be pre-populated by existing clinical data in EHRs: – Retrieve Form for Data Capture (RFD) – Clinical Research Document (CRD) – Drug Safety Content (DSC) – Redaction Service Profile (RSP) – Proposed Data Element Exchange (DEX) and Patient Authored Note – HL7 CCD, CDISC CDASH and ODM for data exchange and archive Confidentiality and security aspects – Consistent Time (CT) – Cross-Enterprise User Assertion (XUA) – Audit Trail Node Authentication (ATNA) 12

13 RFD Roles – EHR and EDC 13 Retrieve FormSubmit FormArchive Form Form Manager B Form Receiver C Form Filler A Form Archiver D EHR EDC Source: Dave Iberson-Hurst eSource © CDISC 2011

14 2013 Joint Summits on Translational Science 14

15 2013 Joint Summits on Translational Science 15

16 A Learning Health System (LHS) “ … one in which progress in science, informatics, and care culture align to generate new knowledge as an ongoing, natural by-product of the care experience, and seamlessly refine and deliver best practices for continuous improvement in health and health care. ” (Institute of Medicine) The LHS relies on “sharing and disseminating what is learned in timely and actionable forms”. (In a manner that is) “sufficiently specific, unambiguous, and standardized to allow encoded logical statements and operations that can be executed by digital computing devices- -and retrieved again for future use.” 16

17 A global, accessible electronic metadata library, which through advanced technology, enables precise and standardized data element definitions and richer metadata that can be used in applications and studies to improve biomedical research and its link with healthcare. CDISC SHARE VISION 17

18 18 Single, trusted, authoritative source for CDISC data standards Concepts, metadata, collections, relationships, value sets across the full spectrum of CDISC content Links research to healthcare concepts to support interoperability Aligned with NCI Semantic Systems Adapted from Source by Sue Dubman, Sanofi-Aventis 18 bridg

19 Standard-based integration profiles for EHR4CR Christel Daniel, MD, PhD; Landen Bain INSERM UMRS 872eq20, Paris, France AP-HP, Paris France CDISC’s Healthcare Link initiative & IHE QRPH 2013 Joint Summits on Translational Science 19

20 IMI EHR4CR project Objectives, scope Executive Summary Innovative Medicine Initiative – Public-Private Partnership between EU and EFPIA focused in research on needs common to the Pharmaceutical Industry and Patients at European level EHR4CR platform to reusing EHR data for supporting medical research A comprehensive business model for governance, acceptance, adoption and sustainability 4 years ( ) 2013 Joint Summits on Translational Science20

21 EHR4CR partners 33 European academic and industrial partners 11 Pharmaceutical Companies (members of EFPIA) & 22 Public Partners (Academia, Hospitals and SMEs) 2013 Joint Summits on Translational Science 21

22 Use cases 2013 Joint Summits on Translational Science22 Protocol feasibility 1 1 Leverage clinical data to design viable trial protocols and estimate recruitment Patient recruitment 2 2 Detect patients elegible for trials to better utilize recruitment potential Clinical trial execution 3 3 Re-use routine clinical data to pre- populate trial CRFs Pharmacovigilance 4 4 Detect adverse events and collect/transmit relevant information A loosely coupled SOA, which interconnects independent services implementing EHR4CR usage scenarios

23 Standard-based IHE profiles for EHR4CR Work Breakdown: RAPID Review existing IHE QRPH integration profiles Assess their applicability Participate in ongoing IHE QRPH work Integrate a selected group of profiles into a coherent test plan (projectathon) Develop a new IHE QRPH profile in order to better address semantic interoperability issues Joint Summits on Translational Science

24 Central Workbench (end user services) Recruitment Status (“Start”) Publish Interest to Sites service Middleware services Orchestrator Registry Semantic services Local Endpoint User Role Management Study Manager Data Relational Manager PFS Services Query builder Result Viz PRS Services Publish Research Protocol Recruitment Monitoring Viz Recruitment Monitoring Viz CTE/PV Services Publish Research Protocol (Forms ID) Data Capture Monitoring Viz Data Capture Monitoring Viz Query builder CENTRAL EHR4CR PLATFORM EHR CDW HOSPITAL SITES

25 Feasibility study A research worker in charge of designing a new clinical trial seeks to assess the recruitment capability of several healthcare facilities by searching distributed CDWs. – For this use case, typically population-centric, CDW is the preferable data source He/she uses the query builder of the Protocol Feasibility Study module of the EHR4CR platform Joint Summits on Translational Science25 1 1

26 Feasibility studies Since the query builder implements services similar to Data Element Exchange (DEX) service the research worker can drag and drop Data Elements stored in the EHR4CR metadata registry (1) as well as boolean and temporal operators (2) in order to represent formally the inclusion/exculsion criteria of the clinical trial (3) Joint Summits on Translational Science

27 Feasibility studies The electronic query returns only aggregated data (counts) – only if counts are sufficiently large to protect privacy. – might be cross- tabulated by a number of key eligibility criteria 2013 Joint Summits on Translational Science27 1 1

28 Semantic services for Feasibility studies Data Element selection at the Workbench – Template-based approach: 9 query templates (based on ISO data types) – Services are used by the query builder to access the meta data repository (Data Element concept, Value Set, data types, unit, etc) in order to select the appropriate template and parameters for the query specification EHR4CR object-oriented query language – ECLECTIC Query transformation at the Endpoints – Service gets as input a template and the appropriate parameters and delivers as output: SQL queries for relational data bases SPARQL for RDF triple stores/SPARQL Endpoints 2013 Joint Summits on Translational Science28 1 1

29 29 Ontology Modularization User Workbench EC Model Query Transformation & Expansion ECLECTIC Query Templates Terminology Mappings EHR4CR Endpoint 1 Q1Q1 Q2Q2 CDW 1 CDW 2 Query Result Transformation Endpoint Algorithm SPARQL Endpoint Orchestrator EHR4CR Reference Terminologies EHR4CR Terminology UMLS (LOINC, SNOMED CT, ICD-10) PathLex MedDRA Local Terminology Eligibility Criteria Matched Patients 1 1

30 Feasibility studies 10 clinical trials – 11 pilot sites 30 Internal Study ID EFPIA Partner Disease Area AP- HP FAU HUG KCL * MU W U93 6 UCL Univ dun UoG uom WW U Tota l BayerCardiovascular XXX AmgenOncology XX MerckNervous system disorders XXX 3 BIO GSKOncology XX 2 CENA713B2315 NovartisNeurology X 1 COU-AA-301 JanssenOncology XX 2 D3191C00009 AstraZe neca CV/Arrhythmias X 1 D4320C00015 AstraZe neca Oncology XXXX 4 EFC11785 SanofiOncology XXX 3 NC25113 RocheCardiovascular and Metabolic XX 2 Total

31 Case Report Form (CRF) pre-population A patient has given his full informed consent for participating to the clinical trial and for the extraction of data from his/her EHR Only pseudonomized individual patient level data are returned to the organization conducting the clinical research Joint Summits on Translational Science31 3 3

32 Case Report Form (CRF) pre-population The study manager/data manager is in charge of designing computable specification of the eCRF – CDISC Operational Design Model (ODM) defined in 2001,specifies the organization structure of data captured for analysis and reporting over the course of a clinical trial He/she uses an eCRF editor – uses Data Element Exchange (DEX) profile to access to the metadata registry and select the desired Data Elements (such as CDSASH, CSHARE) to annotate the eCRF. – the metadata include a mapping to the corresponding data element in the HL7 specification Continuity of Care Document (CCD) as extended in the Clinical Research Document (CRD) profile. He/she uploads the CDISC ODM format of the eCRF on the EHR-enabled platform Joint Summits on Translational Science32 3 3

33 During a visit, the clinical investigator, using the Retrieve Form for Data Capture (RFD), opens the eCRF An electronic query based on the extraction specification of the Clinical Research Document (CRD) profile automatically pre-populates the forms – For this use case, typically patient-centric, the data source is preferably the EHR. The clinical investigator validates pre-populated data, completes and submits the form to the organization conducting the clinical research Joint Summits on Translational Science33 Case Report Form (CRF) pre-population 3 3

34 Semantic interoperability approach Matching computable query specifications to routinely collected clinical data – Common clinical information model (EHR4CR clinical information model): a unique global as view schema of the heterogeneous EHRs/CDWs distributed over different pilot sites across Europe – Query language representing executable eligibility rules (scenario 1 & 2) and data extraction specification (scenario 3 & 4) – Mapping tools and query transformation services Joint Summits on Translational Science

35 Semantic interoperability approach Semantic resources EHR4CR clinical information model – HL7 RIM based generic model Templates/Archetypes Data Elements – Clinical entities (including observations (diagnosis, findings, familial and medical history, lab tests, etc), procedures, substance administration (including medication), etc) represented in the EHR4CR information model. – Reference clinical terminologies (e.g ICD-10, ATC, LOINC, SNOMED CT, RadLex, PathLex, etc) Joint Summits on Translational Science

36 Collaborative Data Element Editor Joint Summits on Translational Science36 EHR4CR semantic resources: Data Elements, Value Sets Reference terminologies (BioPortal) Edition of a new resource (Data Element) Collaborative model editor Data Element artifacts -> serialization to ISO MDR Model to model transformation (HL7 MIF -> OWL)

37 The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° [No ], resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/ ) and EFPIA companies’ in kind contribution Joint Summits on Translational Science37 INSERM team : Sajjad Hussain, Michel Kapoko, David Ouagne, Eric Sadou WP4 members : Richard Bache, Kerstin Forsberg, Thomas Ganslandt, Julie James, Sebastian Mate, Marc Mc Gilchrist, Dirk Schwarz-hertzner, Eric Zapletal, … WPG2 members Acknowledgments

38 Standards-based integration profiles for TRANSFoRm Brendan C Delaney, BM, BCh, MD 1, Vasa Curcin, PhD 2 On behalf of the TRANSFoRm consortium 1 Kings College of London, UK; 2 Imperial College London, UK 2013 Joint Summits on Translational Science 38 TRANSFoRm This project is partially funded by the European Commission under the 7th Framework Programme. Grant Agreement No Translational Research and Patient Safety in Europe (TRANSFoRm)European Commission7th Framework Programme

39 Aims of TRANSFoRm To develop methods, models, services, validated architectures and demonstrations to support: – Epidemiological research using GP records, including genotype-phenotype studies and other record linkages – Research workflow embedded in the EHR – Decision support for diagnosis 39

40 TRANSFoRm Consortium

41 Use case 1: Type 2 Diabetes Research Question: In type 2 diabetic patients, are selected single nucleotide polymorphisms (SNPs) associated with variations in drug response to oral antidiabetic drugs (Sulfonylurea)? Design: Case-control study Data: primary care databases (phenotype data) and genomic databases (genetic risk factors) – data federation Requirements: Data quality filter, cohort selection, data linkage 41

42 Use case 2: Gastro-oesophageal reflux disease (GORD) Research Question: What gives the best symptom relief and improvement in QoL: continuous or on demand PPI use? Design: Randomised Controlled Trial (RCT) Data: Collection through eHR & eCRF Requirements: Cohort selection, randomisation, eSource, PROM via web and mobile platform, event based triggering from EHR 42

43 Use case 3: Diagnostic decision support Research Question: Experimental study of a DSS with standardized patients Design: within subjects experiment Data: Ontology of Clinical Prediction Rules, diagnostic performance Requirements: Triggering via captured ‘Reason for Encounter’ 2013 Joint Summits on Translational Science43

44 Standard-based IHE profiles for Transform Background: BRIDG 3.0, ISO11179, CDISC (STDM, CDASH, ODM), LexEVS DM Use case requires functionality of the Retrieve Form for Data Cature profile GORD use case additionally requires the functionality of the Clinical Research Process Content and Retrieve Process for Execution profiles. DSS requires incorporation of diagnostic ontology However: – TRANSFoRm is model-based – We do not load all source data via an ETL process but manage queriy expressions at run-time. – Workflow representation – Provenance 2013 Joint Summits on Translational Science 44

45 caDSR/ISO11179 A complex model difficult to extend or adapt, e.g. not easy to incorporate CDISC domain model and variable role model. Heavyweight implementation – any change of the model will require a complete rebuild of the whole stack from database schema up to client API. Focuses solely on data element, lack of formal definition of data element relationships. 45

46 CDISC SDTM 46

47 CDISC SDTM CDISC SDTM is more focused on defining a standard representation structure of collected datasets. Use of CDISC standard variables in CRFs are outside CDISC scope – subject to vendor implementation specifics. CDISC (CDASH) standard variables are often generic, so TRANSFoRm needs a mechanism to extend CDISC model and be able to define more study specific data elements which are based on or can be mapped to CDISC standard variables where possible. 47

48 CDISC Operational Data Model Standard for the description of metadata associated with a clinical trial. Allows exchange of datasets. Allows vendor extensions. Does not allow groups within groups on a form in its unextended format. ODM instance would be an xml document with bound terminology and descriptors for text, value, value range, code etc.

49 Ontology Framework The TRANSFoRm mediation framework is based on a general information model (GIM) derived from a recognised sets of initiatives in the biomedical domain. This GIM is instantiated as the Clinical Data Integration Model for the purposes of TRANSFoRm. The major role of CDIM is to support data interoperability. CDIM is the only model with which TRANSFoRm modules interact with the data. This allows the framework to abstract a significant part of the of the complexity created by heterogeneity between each data source

50 Ontology Framework Upper ontology: Basic Formal Ontology (BFO) Middle (domain) ontologies: OGMS (Ontology of General Medical Science) IAO (Information Artefact Ontology) VSO (Vital Signs Ontology) Biodynamic Ontology: Applying BFO in the Biomedical Domain, D. M. Pisanelli (ed.), Ontologies in Medicine, Amsterdam: IOS Press, 2004, 20–38 R. H. Scheuermann et al, Toward an Ontological Treatment of Disease and Diagnosis, Proceedings of the 2009 AMIA Summit on Translational Bioinformatics, San Francisco, CA, p Albert Goldfain et al, Vital Sign Ontology, Proceedings of the Workshop on Bio-Ontologies, ISMB, Vienna, June 2011,

51 Thing Entity Continuant IndependentDependent Material Chemical -> Form -> Product Molecular -> DNA -> SNP Object -> Human -> Patient Information Content Document -> Rx Directive -> Act -> Rx item Directive -> Condition -> Rule Label -> Measurement unit -> Unit label Data item Measurement datum Systolic measurement Lab measurement Pulse rate measurement … Clinical finding -> Phys Exam Clinical finding -> Lab finding Diagnosis Prognosis … length -> human height Mass -> dose phenotype -> gender pressure -> diastolic pressure Quality Clinical Data Integration Model (ontology)

52 Thing Entity Occurrent Scattered temporal Connected temporal Drug utilisation frequency Instant Birth Death Diagnosis confirmation Disease course onset Lab specimen obtention Vital sign -> BP time taken Vital sign -> height time taken Interval Processual Lab ordering Lab result confirm Lab specimen obtention Process Process boundary Clinical Data Integration Model (ontology)

53 General model of diagnosis

54 Extended PCROM 54 Area for data collection Care related concepts

55 Eligibility Criteria Model (ECM)* 55 *Input to implementation of the information model in WP5.3 (query and data extraction work bench)

56 Distributed Nodes Infrastructure and Services Infrastructure and Services TRANSFoRm Architecture End User Tools and Services 12. Decision Support Prototype 4. Data Quality Tool 9. Electronic Case Report Forms (eCRFs) 10. Data Federation 1. Semantic Mediator Support Services 1. Semantic Mediator 7. Clinical Prediction Rules WS 3. Provenance 5. SQA and Integration 8. Research Data Integration Model 2. Privacy Model 56

57 57 Reference Terminologies and mappings CDIM CRIM Workbench Data node connector DS model (DSM) CDIM-DSM mappings Middleware Provenance and security models Study/Trial db used by

58 OpenEHR Archetype In the OpenEHR “archetypical” approach, clinical contents are defined through archetypes. OpenEHR archetype is a constraint model, which constrains a reference information model to formulate domain contents. The archetype model follows object-oriented semantics, which allows specialization and composition – much more powerful and flexible than caDSR model. Some other features, e.g. a human readable archetype definition language (ADL), a XPATH/SQL style archetype query language (AQL), etc. OpenEHR archetype is now an ISO standard (ISO ). 58

59 Archetype and Forms 59

60 Archetypes constrain data elements 60

61 Conclusion European Learning Healthcare System Complex and challenging use case requirements Only a small fraction of the project presented here Embedding constrained models within standards to allow flexibility and standards compliance Need to work with IHE and other EU and US projects 61

62 Acknowledgments King’s College London: Adel Taweel Imperial College: Vasa Curcin University of Rennes: Jean Francois Ethier, Anita Burgin-Parenthoine University of Dundee: Mark McGilchrist University of Birmingham: Theodoros Arvanitis, James Rossiter, Lei Zhao RCSI, Dublin: Derek Corrigan Karolinska Institute: Anna Nixon Andreasson, Lars Agreus University of Antwerp: Paul van Royen, Hilde Bastiens, Johan Wens NIVEL: Robert Verheij CPRD: John Parkinson, Tjeerd van Staa Trinity College Dublin: Siobhan Clarke 2013 Joint Summits on Translational Science62

63 Standard-based integration profiles for SALUS Gokce B. Laleci Erturkmen, PhD A. Anil Sinaci, MSc SRDC Software Research, Development and Consultancy Ankara, Turkey 2013 Joint Summits on Translational Science 63

64 2013 Joint Summits on Translational Science 64 SALUS: Scalable, Standard based Interoperability Framework for Sustainable Proactive Post Market Safety Studies (http://www.salusproject.eu/)http://www.salusproject.eu/ A STREP funded under Objective ICT b – Tools and environments enabling the re-use of electronic health records which aims to – Enable effective integration and utilization of electronic health record (EHR) data to improve post-market safety activities on a proactive basis – Pilots in Lombardia Region (Italy) and Eastern Saxony (Germany) WHO-UMC and ROCHE are actively involved in pilot studies  Partners  SRDC Ltd, Turkey (coordinator)  EUROREC, France  WHO- UMC, Sweden  OFFIS, Germany  AGFA Healthcare, Belgium  ERS, Netherlands  LISPA, Italy  INSERM, France  TUD, Germany  ROCHE, Switzerland

65 Joint Summits on Translational Science How SALUS extends current spontaneous reporting system to seamlessly exploit the already existing clinical data at EHRs An ideal system for ADR surveillance would combine the strengths of case reports with those of EHRs

66 Use Cases Enabling Semi-automatic Notification of Suspected ADEs and Reporting ADEs within a Hospital – Enabling Notification of Suspected ADEs – Enabling Semi-automatic ADE Reporting Supporting Clinical Evaluation of a Potential Signal through Accessing the EHRs – Characterizing the cases and contrasting them to a background population What differs between the patients having a myocardial infarction within two weeks of Nifedipine intake to all the other patients taking Nifedipine? – Temporal pattern characterisation Is there a temporal association between a drug of interest and a medical event of interest – By comparing the actual and expected counts of events in different time periods relative to the first prescription of a drug – Can be used for evaluating ADEs – Can be used to assess positive impacts of drugs Joint Summits on Translational Science

67 Running Exploratory Analysis Studies over EHRs for Signal Detection – Temporal association screening on EHRs – What does the Medical Event profile look like for Nifedipine? – Are there any drugs that might be associated with causing Myocardial Infarction? – Open ended analysis, no prior hypothesis – Generates associations that might become signals – Manual clinical review of relevant medical history Using EHRs as secondary use data sources for Post Marketing safety studies – Estimate incidence rates of congestive heart failure (CHF) in diabetic patients with a recent acute coronary syndrome (ACS) event on different diabetic medications Use Cases Joint Summits on Translational Science

68 SALUS Semantic & Technical Interoperability 2013 Joint Summits on Translational Science 68

69 Standard-based IHE profiles for SALUS SALUS Technical Interoperability – Subscription/Query Based Interoperability Profiles – heterogeneous EHR systems – population based – eligible patient group (inclusion/exclusion) – continuous screening Related available interoperability approaches have been examined – From ITI: IHE RFD, From QRPH: IHE DSC, CRD Form based interaction, not query/subscription based, focusing on case safety reports – From ITI: IHE XDS (MS), From PCC: IHE QED, IHE CM Subscription/query based, yet not specialized for population based queries Not only HL7 CCD, SALUS would support patient summaries expressed in EN13606 artefacts – Representing eligibility queries: HL7 HQMF queries CDISC Study Design Model (SDM) 2013 Joint Summits on Translational Science 69

70 Standard-based IHE profiles for SALUS For the subscription/query based profiles of SALUS Extended IHE CM (subscription) and IHE QED (query) population based eligibility criteria use HL7 HQMF Carry EN13606 EHR EXTRACTs in addition to ASTM/HL7 CCD. For ADE Reporting (ICSR) QED, DSC or IHE DEX are possible solutions DEX: Modular, dynamic, interoperable Study Design for Observational Studies DEX 2013 Joint Summits on Translational Science 70

71 IHE DEX For the reuse of EHRs for clinical research – E.g. CCD  CDASH annotated ODM Can be achieved through existing IHE profiles – RFD, CRD, Redaction – The problem: one size fits all – XSLT mappings Power of an MDR – apply mappings earlier in the process During the form design, data elements of the form have already been mapped to the corresponding elements in the EHR export – The MDR to maintain the exact correspondences between the research and healthcare data elements DEX is to support study feasibility, patient eligibility and recruiting, adverse event reporting, retrospective observational studies as well as case report form pre-population – existing standards for patient summaries – ASTM/HL7 CCD 2013 Joint Summits on Translational Science 71

72 IHE DEX – Actors and Transactions 2013 Joint Summits on Translational Science 72 Retrieve Metadata [QRPH-Y1] This transaction is used by the Metadata Consumer to retrieve the metadata of a selected Data Element from the Metadata Repository. The Metadata Consumer has previously obtained the UUID of the Data Element she is looking by means outside of the scope of this transaction DEX Volume 1 is complete Volume 2 is underway Release for Public comment in May Trial Implementation in July

73 Semantic Metadata Registry (MDR) Data within each system is stand-alone and not interoperable – “have a common understanding of the meaning and descriptive characteristics (e.g. representation) of data” Metadata for Semantic Interoperability – annotate the information models of different systems with the same set of data elements residing in the metadata registries – early approach: bottom-up takes root from several other disciplines like linguistics, compilers etc… 2013 Joint Summits on Translational Science 73 MDR ISO/IEC Any other Metadata

74 Disparate Data Elements, Content Models There are many different efforts to define Data Elements, and binding them to actual data sources (like CCD documents) Examples: – Health Information Technology Standards Panel (HITSP) has defined the C154: Data Dictionary Component HITSP C83 marks the elements in CCD document with the corresponding HITSP C154 data elements – The Federal Health Information Model (FHIM) develops a common Computationally Independent Model (CIM) for EHRs – GE/Intermountain Healthcare Clinical Element Models (CEM) – The Transitions of Care Initiative (ToC) maintains the S&I Clinical Element Data Dictionary (CEDD) Mappings to I2B2, PopMedNet, HQuery implementations, FHIM Model, HITSP C154 when possible – available in separate excel sheets, PDFs… – CDISC SDTM, CDASH – Mini Sentinel Common Data Model (CDM) – I2B2 data model – Observational Medical Outcomes Project (OMOP) Common Data Model (CDM) 2013 Joint Summits on Translational Science 74

75 Linked Metadata Federated Semantic MDRs Maintain & Manage – Data Elements – the relations between Data Elements – the components of Data Elements – the relations between the components of Data Elements Different Data Elements from different Content Models – their relations and mappings are managed semantically A set of Data Elements with lots of relations – Semantic Resource Set – The relations can be through the LOD cloud – Linked Metadata! The relations may point to native representations of the Content Models – Extraction Specification 2013 Joint Summits on Translational Science 75

76 An example Execution in SALUS Local Semantic MDR links to SDTM CDEs Preparing Study design for an observational study CDISC ODM Study Design Document Search and use CDEs from the local MDR Metadata Source implementing DEX ODM is annotated with SDTM HITSP Semantic MDR Search for CCD Extraction s of the SDTM CDEs BRIDG Semantic MDR CDISC Semantic MDR XPATH of corresponding CCD element is returned Ask for Extraction Specification of HITSP CDE:”Result Value” Receives the URI of HITSP CDE:“Result.Value” Queries federated MDR Cloud for SDTM “LBORRES” 1 BRIDG Semantic MDR maintains a skos:exactMatch mapping from LBORRES to “Result.Value” through “PerformedObservationResult.valu e.Any” = ' '] /cda:value Metadata Consumer 2013 Joint Summits on Translational Science 76

77 Participation to a projectathon? Using DEX? SALUS is implementing a semantic MDR for the semantic interoperability Semantic MDR can implement IHE DEX in order to provide – extraction specifications during ICSR Reporting – population of data collection sets for eligible patients during observational studies SALUS – EHR4CR collaboration through DEX CDISC SHARE can be extended… 2013 Joint Summits on Translational Science 77 DEX Interface Retrieve Metadata SALUS MDR (Metadata Source) EHR4CR (Metadata Consumer)

78 Conclusion Standard-based IHE profiles for TRANSFoRm, EHR4CR, SALUS use cases Collaborating to test IHE profiles during a joint projectathon will be a first step towards global interoperability between EHR4CR, TRANSFoRM and SALUS platforms and towards a pan-EU capability for clinical research and patient safety Joint Summits on Translational Science78

79 2013 Joint Summits on Translational Science79 EHR4CR / TrRANSFoRM use cases Clinical Research SALUS use cases Patient Safety Use case #1: Identification of populations of patients Protocol feasibility study Patient recruitment Signal detection IHE QRPH:Retrieve Process for Execution [RPE], Clinical Research Process Content [CRPC], Data Element Exchange [DEX] (under development) – IHE PCC Query for Existing Data [QED], Care Management [CM] Use case #2: Extraction of a set of individual clinical data Case Report Form (CRF) pre- population Individual case safety report (ICSR) form pre-population IHE ITI: Retrieve Form for Data Capture [RFD], IHE QRPH: Retrieve Process for Execution [RPE], Clinical Clinical Research Process Content [CRPC], Data Element Exchange [DEX] (under development) Research Document [CRD]Drug Safety Content [DSC] Use case #3: Extraction of sets of individual clinical Retrospective observational study Use case #1+ Use case #2 Data Element Exchange [DEX] (under development) Security & confidentiality IHE ITI: XUS, CT, ATNA Standards : CDISC (CDASH, Operational Design Model (ODM), Study Design Model (SDM), CSHARE, BRIDG) - HL7 (RIM, DCM, Clinical Statement model, CDA (templates e.g. CCD), Vocabulary) – ISO (ISO 21090, ISO 11179)

80 Perspectives Semantic resources – “Model of use” : Agreed clinical data structure definitions (Templates,archetypes, Data elements (e.g SHARE (CDISC)) Based on generic reference models for representing clinical data (e.g. ISO EN Part 1, the ISO/HL7 RIM, openEHR Reference Model) and standard data types (ISO 21090) – Explicitly bound to “Models of Meaning” : reference clinical terminologies (e.g. LOINC, SNOMED-CT, ICD-10) through value sets Services for accessing semantic resources – Implemented into standard-based “Transactions” between “Actors” Most healthcare IT standards are now available in RDF/OWL – Could semantic interoperability be now only an issue of shared ontologies? – Are they therefore magically transformed into meaningful and computable standards?

81 Questions ? 2013 Joint Summits on Translational Science81

82 Patient recruitment The study manager/data manager is in charge of designing the computable specification of the research protocol – CDISC Study Design Model (SDM) released in 2012,provides machine-readable, interchangeable descriptions of the design of clinical studies Extension to the existing CDISC Operational Data Model (ODM) specification He/she uploads the CDISC SDM format of the research protocol on the EHR-enabled platform Joint Summits on Translational Science82 2 2

83 CDISC Study Design Model (SDM) Clinical study concepts 83 XML-SDM

84 CDISC Study Design Model (SDM) Eligibility criteria 2013 Joint Summits on Translational Science 84

85 Patient recruitment The study manager uses the EHR4CR query builder (using Data Element Exchange (DEX) profile) for representing formally the inclusion/exclusion criteria of the clinical trial 2013 Joint Summits on Translational Science85 2 2

86 Patient recruitment At the hospital, once the clinical trial is set up (approvals obtained, clinical investigators recruited and contract completed) The principal investigator runs the queries on a local workbench – For this use case, typically population-centric, CDW is the preferable data source 2013 Joint Summits on Translational Science86 2 2

87 Patient recruitment No individual patient level data would be returned to the organization conducting the clinical research prior to patient consent. Only treating physicians have access to a re-identified list of patients and may, when appropriate, invite patients to participate to the trial Joint Summits on Translational Science87 2 2

88 Case Report Form (CRF) pre-population The data manager is in charge of designing computable specification of the eCRF (CDISC Operational Design Model (ODM)). He uses the Data Element Exchange (DEX) profile to access to the on-line metadata registry and select the desired from a core Common Data Elements (such as CDSASH, CSHARE) to annotated eCRF. The metadata includes the exact specification, using XPath, to find the corresponding data element in any standard EHR export document such as a CCD or CDA (including the HL7 specification Continuity of Care Document (CCD) as extended in the Clinical Research Document (CRD) profile. Both CDISC ODM format of the eCRF and extraction specification are avialable on the EHR-enabled platform Joint Summits on Translational Science88 3 3

89 Case Report Form (CRF) pre-population The LOCAL data manager creates an extraction specification to extract specific data elements included in the eCRF either from the EHR or from a standard EHR export document such as a CCD or CDA Joint Summits on Translational Science89 3 3

90 90 Form Filler/ Document Source Redactor Form Archiver Retrieve Form [ITI-34] Submit Form [ITI-35] Archive Form [ITI-36 ] Form Manager Extraction Specification Manager Send Export document [QRPH-31] Retrieve Extraction Specification [QRPH-33] Return redacted document [QRPH-32] Form Receiver/ Document Consumer Figure 2-1: CRD+RSP Swimlane Diagram

91 Providing RFD formID to the EHR 91

92 Providing Adverse Event formID to the EHR 92

93 Providing RSP ExtractionID to the EHR 93

94 Providing the RFD orgID to the EHR 94

95 The need of standard-based integration profiles EHR Clinical Research Functional Profile (A NSI/HL7 standard) can be used by EuroRec in certifying EHRs for the purpose of research considered Joint Initiative Council (JIC) for Global Harmonization of Standards (which includes ISO, CEN, CDISC, HL7, IHTSDO, GS1) Certification based on open model & standard compliance encourage wide adoption – Both accredited EDCs/EHRs vendors (certified products) & hospitals (certified source data) will have a competitive advantage 2013 Joint Summits on Translational Science95 Platform services shall implement established, vendor neutral integration profiles tested during projectathons


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