“Breaking Barriers: Liberating Health Data to accelerate High Quality Clinical Research”

1. “Breaking Barriers: Liberating Health Data to accelerate High Quality Clinical Research” • Prof.Dr. Georges De Moor • Dept. of Medical Informatics and Statistics, • Ghent University, Belgium & - RAMIT - • European Institute for Health Records - EuroRec - • - Custodix -

2. EuroRec • The EuroRec Institute (EuroRec) is a European independent not-for-profit organisation, whose main purpose is promoting the real use of high quality Electronic Health Record systems (EHRs) in Europe. • EuroRec is overarching a permanent network of national ProRec centres and provides services to industry (developers and vendors), healthcare systems and providers (buyers), policy makers and patients. • EuroRec produced and maintains a substantial resource with ± 1700 functional quality criteria for EHR-systems, categorised, indexed and translated in 19 European languages. The EuroRec Use Tools help users to handle this resource.

3. Introduction • Amount of information to support medicine and healthcare is exploding • ICT is transforming both biomedical research and healthcare (e-Health) • The way scientists ‘do science’ is changing (a revolution) • Electronic Health Records (EHRs) are gaining - in combination with emerging infrastructures - an important novel supporting role for clinical research

4. Capture, Combine, Co-interpret Datafrom diverse Information Sources Care Pathways Systems, Decision Support Systems, Trends and Alerting Systems Population Registries, Clinical Trial Data-Bases, Bio-Bank data Data Information Knowledge EHRs, PHRs, Ancillary DBs and other Clinical Applications Mobile Devices, Apps (medical/well-being) Bio-sensors and Body Implants Social Networks

5. Capture, Combine, Co-interpret Data from diverse Information Sources Clinical data “-Omics” data (genomics, proteomics, metabolomics…) Environmental data (pollution, nutrition…)

6. Leveraging Knowledge Discovery Data interpretation Information (Wisdom) interpretation Knowledge Decision Action

7. Electronic Health Records & systems: Trends • Patient-centered (gatekeeper?), life long records • Multi-disciplinary / multi-professional / participative • Transmural, distributed and virtual • Structured and coded cf. semantic interoperability • More metadata (tagging and coding) at a “granular “ level • Natural language interfaces • Intelligent cf. decision support, clinical practice guidelines… • Predictive e.g. genetic data, physiological models (cf. ethics!) • More sensitive content (cf. privacy protection!) • Personalised • Integrative • Certified

8. What is an Electronic Health Record (EHR)? • “One or more repositories, physically or virtually integrated, of information in computer processable form, relevant to the wellness, health and health care of an individual, capable of being stored and communicated securely and of being accessible by multiple authorised users, represented according to a standardised or commonly agreed logical information model.  Its primary purpose is the support of life-long, effective, high quality and safe integrated health care” • (Kalra D. Editor. Requirements for an electronic health record reference architecture. ISO 18308. International Organisation for Standardisation, Geneva, 2011) • Personalised Medicine means that Research no longer only needs data but will use highly specific data from individual patients… hence the importance of getting access to the EHRs…

9. Shift from … to … (in care) Informed Healthcare Professionals Informed Patient-Care (EBM) Patient-Informed Care

10. Shift from … to … Patient - Trust - Physician ? ? ? Patient - Trust? - Health Networks ? ?

11. Convergence Initiative (of EuroRec) SmartCare

12. The Convergence Initiative (March 2013) To initiate and support cooperation and consensus building among related e-Health projects (cf. data reuse, semantic interoperability…) To identify opportunities To identify and share results To identify challenges … towards a pan-EU e-Health Info-structure

13. (Clinical) Research Controlled Clinical Trials … Pharmaco-vigilance (non systematic list!) Epidemiological studies Public Health Research Observational Research Disease Management studies Comparative Effectiveness Research (older drugs, multiple diseases…) Diagnostic Research Continued Surveillance Health Technology Assessment Health Systems Research Cost Effectiveness Research …

14. Data Sources for Clinical Research

15. Focus • Focus of this presentation • the EHRs as data sources • and • the (re-)use of data for Clinical Research

16. EHRs: where are we? • Rapid expansion in the last years => in some countries 90% of healthcare records are digital • OECD HCQI Country Survey 2012:(http://www.oecd.org/els/healthsystems/strengtheninghealthinformationinfrastructure.htm) • In 13/25 countries + 70% physicians use EMRs • In 15/25 countries + 70% of the hospitals use EPRs • In 22/25 countries National plan to implement EHRs • In 18/25 countries a Minimum Data Set has been defined • However…many legacy EHR systems do not provide at present a sufficient basis for clinical research

17. Challenge: Data Quality • The Quality of EHR systems and EHR data is important • Third Party Certification of EHR systems is essential • Quality assurance is needed • Quality has many dimensions • Correctness • Completeness • Accuracy • Currency • Validity • …

18. The Data Content Issue • Semantic Interoperability and Data Quality Markers: • in CARE: Faithfulness (cf. biases in coding, window dressing for reimbursement…) • in RESEARCH: Faithfulness and Consistency • Context Sensitivity and Specificity: depending on the context in which data are captured, the meaning and the value of the data may vary… hence the importance of “context specific” tags (and of metadata) in EHRs…

19. EuroRec’sprofile for EHRs that are compliant with Clinical Trials requirements • Already in December 2009 EuroRec released a profile identifying the functionalities required of an EHR system in order to be considered as a reliable source of data for regulated clinical trials. • Details of the profile, including information designed to support use, are accessible from the EuroRec website. A sister profile has been endorsed by Health Level Seven® (HL7®). • As both the EuroRec and HL7 profiles draw upon the same standard requirements for clinical trials, ”conforming to one” will mean, in principle conformance to both. • These requirements have contributed into a Work Item in ISO (TC/215), to help shape a future International Standard. • The EHR4CR Project expands the set of quality criteria for EHRs to be used for research…

20. Semantics: an important Challenge • Natural Languages (in Europe: 23 official languages!) • Structured versus unstructured (narrative) records/messages • Many medical concepts and relations between concepts (many views!) • Terms (many medical terminologies!) • Ontologies • Information Models (e.g. EHR reference models…) • Semantic resources (detailed clinical models/ clinical archetypes/ templates) • Design an overall info-structure (a virtual platform and services) that can publish or reference resources and manage their maintenance… How to represent and convert “meaning” from a “human understandable” form in a “computer processable” form?

21. Semantic Interoperability Resources • Widespread and dependable access to maintained collections of coherent and quality-assured semantic resources • detailed clinical models, such as archetypes and templates • rules for decision making and monitoring • workflow logic • which are • mapped to EHR interoperability standards • bound to well specified multi-lingual terminology value sets • indexed and correlated with each other via ontologies • referenced from modular (re-usable) care pathway components • establishes good practices in developing such resources

22. Exampleof a Representation of a • Clinical Practice Guideline

23. Layered semantic models (1) Objective : semantic interoperability between diverse systems Standards in the domain of patient care (collective international efforts): • ISO EN 13606 • Generic and comprehensive representation for the exchange of EHR information (including fine-grained parts of EHRs) • OpenEHR foundation • Maintains a more detailed model, catering for the widest set of use cases for patient level data • HL7 Reference Information Model (RIM) and HL7 Clinical Document Architecture (CDA) • To communicate a single clinical document as a message (e.g. a discharge summary)

24. Layered semantic models (2) In the domain of Clinical Research • Clinical Data Interchange Standards Consortium (CDISC) • Protocol Representation Model (PRM) • Study Design Model (SDM) • Operational Data Model (ODM) • Clinical Data Acquisition Standards Harmonisation (CDASH) • Biomedical Research Integrated Domain Group (BRIDG) model Achieving S.I. across multiple domains requires the integration of multiple standards

25. Layered Semantic Models (3) • Integrating the Healthcare Enterprise (IHE) • Integration profiles • IHE domain Quality, Research and Public Health (QRPH) • Cancer Data Standards Repository (caDSR) • CDISC Shared Health and Research Electronic Library (CSHARE)

26. Ethical, Legal and Privacy Protection challenges to Federated Research • The use of EHRs for clinical research is inevitably challenged both by legal, ethical and privacy protection considerations • Ethical issues are generally similar across different cultures and healthcare systems • Laws and regulations differ substantially • Differences in law and ethical approaches and their interpretations create a number of pragmatic issues

27. Pragmatic issues surrounding the Re-use of EHR data for Clinical Research

28. EHR review article

29. Consent vs. Trust model • Consent model • It is debatable whether explicit consent is required for reusing key-coded (pseudonymised) EHR data for research and statistical purposes • Special legislation may require primary EHR data to be submitted for public health purposes without the need for consent of the data subject • Trust model • Reduce the information content so identification is no longer possible (‘effectively anonymised’) • Uncertainties of the legal position of ‘nearly anomymised’ data • Finding a common approach is very difficult

30. Privacy Protection and • Security measures • De-identification • Microdata vs. aggregated results • Numerous approaches (e.g. generalisation, suppression, global recoding, etc …) • K-anonymity • Contextual anonymity • Security • ‘Basic’ security (authentication, authorisation and audit) is a fundamental requirement of any IT system • Access control management and enforcement • Consent management

31. Important Federated • Clinical Research Initiatives (1) United States • i2b2 • eMERGE • Kaiser Permanente Research Program on Genes, Environment and Health (RPGEH) • Million Veteran Program • Stanford Translational Research Integrated Database Environment (STRIDE)

32. Important Federated • Clinical Research Initiatives (2) Europe • European Medical Information Framework (EMIF) • Delivering European translational information & knowledge management services (eTRIKS) • Enabling information reuse by linking clinical research and care (EURECA) • Integrative cancer research through innovative biomedical infrastructures (INTEGRATE) • Linked2Safety • Scalable, Standard based Interoperability Framework for Sustainable Proactive Post Market Safety Studies (SALUS) • Translational Research and Patient Safety in Europe (TRANSFoRm) • Electronic Health Records for Clinical Research: EHR4CR

33. EU Projects Unlockingthe Data

34. The EHR4CR Consortium (1) • 10 Pharmaceutical Companies (members of EFPIA) • 23 Public Partners (Academia, Hospitals and SMEs) • 5 Subcontractors • One of the largest European public-private partnerships • March 2011-February 2015: 4 years • Budget: € +16 Million (EC DG Research & EFPIA)

35. The EHR4CR Consortium (2)

36. EHR4CR Outputs Project outputs: • A robust, scalable and market-ready Technical Platform • An Innovative Business Model and Cost Benefit Analysis • Pilots (in 11 hospital networks) for validating the solutions (by April 2014: target of 100 hospitals) • for different scenarios (e.g. patient recruitment); • across different therapeutic areas (e.g. oncology); • across several countries (under different legal frameworks).

37. The EHR4CR Services • Clinical Trial Feasibility, i.e. • Performing distributed queries • Patient Recruitment, i.e. • Distributing trial protocols to sites • Collecting follow-up information on recruitment status from sites • Actual patient recruitment  local applications (supported by the platform services) • Clinical Trial Execution & Serious Adverse Events Reporting, i.e. • Mainly EHR extraction & pre-filling of forms • Across • Different therapeutic areas (oncology, inflammatory diseases, neuroscience, diabetes, cardiovascular diseases etc.) • Different legal frameworks (several countries)

38. The EHR4CR Platform • The EHR4CR platform is • a service platform which aims to unlock EHR data on an European/global scale for research purposes, while ensuring compliance with data protection and patient rights legislation • Primarily an architectural specification (blueprint) • Open, modular architecture • Opening the road to certification • “In-project” proof-of-concept implementation • Pilot stage with 12 participating clinical sites • “Post-project” exploitation trajectory • Operational infrastructure • Multiple private or shared instances

39. Architectural Principles • Distributed Architecture • Platform provides infrastructure and semantic services • e.g. identity management, service registries, trial repository, terminology & vocabulary services, etc. • Platform provides central tools • Typical users: trial sponsors • e.g. protocol feasibility workbench, etc. • Data sources reside at clinical sites • Tools are provided for local usage • Tools benefit from the EHR4CR data integration • Typical users: local healthcare professionals • e.g. patient recruitment • Technically: a standards based Service Oriented Architecture (SOA)

40. End-points (Recruitment & Feasibility ) • EHR4CR end-points at the clinical sites are crucial components • Identifyingpatient information remains local on site • EHR integration relies on shadow systems, Clinical Data Warehouses (CDWs) Central tools & services (e.g. protocol feasibility workbench) Direct Query Interface Prot. Feas. Module EHR4CR CDW ETL EHR4CR End-point Interfaces Module X Local tools & services (e.g. patient recruitment workbench) EHR or CDW NLP Data Source EHR4CR Data Source End-Point Data Access

41. Architectural Layers Application Services & End-user Applications + Trial Registry Central Protocol Feasibility Central Trial Recruitment SAE Reporting Audit Protocol Feasibility Query End-points Patient Recruitment Workbenches @ End-points Trial Execution (EDC - CDMS) Security & Privacy Services AuthN & IDM Platform Management Service & Console Platform • Mgt • Services Semantic Integration Services AuthZ Semantic Query Expansion & Mediation Terminology Services ETL Services Trusted Third Party (TTP) Services Data Access Services EHR4CR CDW I2B2 Connector Infrastructure Services Message Services Service Registry

42. ‘Converged’ Clinical Trial Support Platform • Projects with similar goals, converging on platform architecture through the same technical partner (Custodix) • Platform aims to provide: • Connectivity • Security & privacy (compliance) • Infrastructure Management • Support for semantic integration, transparent to the technological implementation

43. Platform Convergence Same technical platform, different semantic integration approaches (and applications) EURECA Semantic Solution EHR4CR Semantic Solution … Platform Mgt • Services Security & Privacy Services Infrastructure Services EHR4CR CDW EURECA CDW tranSMART I2B2

44. … and beyond (pragmatic) Pragmatic approach now happening… EURECA Semantic Solution EHR4CR Semantic Solution … PlatformMgt • Services Security & Privacy Services Model Adaptors Model Adaptors InfrastructureServices EHR4CR CDW EURECA CDW tranSMART I2B2

45. … Long Term Convergence Common Semantic Interface EHR4CR Semantic Solution EURECA Semantic Solution … PlatformMgt • Services Security Services InfrastructureServices EHR4CR CDW EURECA CDW tranSMART I2B2

46. Interoperable Ecosystem

47. Some Existing Pilot Applications… Patient Screening Protocol Feasibility Trial Recruitment Cohort Selection

48. Roadmaps EHR4CR Roadmap towards project (scientific) success Roadmap towards operational success • Full automation should not be the goal (80-20 rule) • Increase efficiency of humans in the existing processes • Computer Aided Protocol Feasibility & Trial Recruitment, etc • Incremental adoption through quick wins • Example patient recruitment • Step 1: Use the platform to optimize communication between sponsor & centers (protocol exchange & updates , status reports, Q&A, provide dashboards, …) • Step 2: Gradually introduce recruitment tools, connecting them to the same platform (for retrieving eligibility criteria, reporting number of recruited patients, etc.) • Similar for enriching the used information models

49. EHR4CR Business Model A business model defines how an organisation creates, delivers and captures VALUE

50. EHR4CR Outputs • Value Proposition • The main reason why customers choose a product/service/provider • It answers the question: “What’s in it for them?” • A value proposition must be: • Uniquely differentiating (perceived distinct benefits) • Highly relevant to customers (addresses unmet needs) • Substantiated with quantified value (versus current standards), e.g. • Cost-benefit assessment (“Value for money”) • Budgetary impact A Value Proposition is Central to Any Business Model