“Breaking Barriers: Liberating Health Data to
accelerate High Quality Clinical Research”
Prof. Dr. Georges De Moor

Dept. ...
EuroRec
• The EuroRec Institute (EuroRec) is a European
independent not-for-profit organisation, whose main
purpose is pro...
Introduction

• Amount of information to support medicine and healthcare is exploding
• ICT is transforming both biomedica...
Capture, Combine, Co-interpret Data
from diverse Information Sources
Population Registries,
Clinical Trial Data-Bases,
Bio...
Capture, Combine, Co-interpret Data
from diverse Information Sources

Clinical data

“-Omics” data

Environmental data

(g...
Leveraging Knowledge Discovery
Data
interpretation

Information
(Wisdom)
interpretation

Knowledge

Decision

Action
Monte...
Electronic Health Records & systems: Trends
•
•
•
•
•
•
•
•
•
•
•
•

Patient-centered (gatekeeper?), life long records
Mul...
What is an Electronic Health Record (EHR)?

• “One or more repositories, physically or virtually integrated, of informatio...
Shift from … to … (in care)

Informed Healthcare Professionals

Informed Patient-Care (EBM)

Patient-Informed Care

Monte ...
Shift from … to …

Patient - Trust - Physician
?
?

?

Patient - Trust? -

Health Networks

?

?

Monte Carlo, 21.10.13

P...
Convergence Initiative (of EuroRec)

Monte Carlo, 21.10.13

Prof. Dr. G. De Moor

11 of 66
The Convergence Initiative (March 2013)

To initiate and support cooperation and consensus building among
related e-Health...
(Clinical) Research

Controlled Clinical Trials
…
Pharmaco-vigilance
(non systematic list!)
Epidemiological studies
Public...
Data Sources for Clinical Research
Data sources

Advantages

Disadvantages

Electronic Health Record
(EHR) at a single
ins...
Focus

Focus of this presentation

the EHRs as data sources
and
the (re-)use of data for Clinical Research

Monte Carlo, 2...
EHRs: where are we?

• Rapid expansion in the last years => in some countries 90% of healthcare
records are digital
• OECD...
Challenge: Data Quality

• The Quality of EHR systems and EHR data is important
– Third Party Certification of EHR systems...
The Data Content Issue

• Semantic Interoperability and Data Quality Markers:
-

in CARE: Faithfulness (cf. biases in codi...
EuroRec’s profile for EHRs that are
compliant with Clinical Trials requirements
• Already in December 2009 EuroRec release...
Semantics: an important Challenge
•
•
•
•

Natural Languages (in Europe: 23 official languages!)
Structured versus unstruc...
Semantic Interoperability Resources
• Widespread and dependable access to maintained collections of coherent
and quality-a...
Example of a Representation of a
Clinical Practice Guideline
Refinement of
the above
statement

Diagostic
statement (which...
Layered semantic models (1)
Objective : semantic interoperability between diverse systems
Standards in the domain of patie...
Layered semantic models (2)

In the domain of Clinical Research
• Clinical Data Interchange Standards Consortium (CDISC)
–...
Layered Semantic Models (3)

• Integrating the Healthcare Enterprise (IHE)
– Integration profiles
– IHE domain Quality, Re...
Ethical, Legal and Privacy Protection
challenges to Federated Research

• The use of EHRs for clinical research is inevita...
Pragmatic issues surrounding the
Re-use of EHR data for Clinical Research
Issue

Identified problems

Gaining retrospectiv...
EHR review article

Monte Carlo, 21.10.13

Prof. Dr. G. De Moor

28 of 66
Consent vs. Trust model

• Consent model
– It is debatable whether explicit consent is required for reusing key-coded
(pse...
Privacy Protection and
Security measures
• De-identification
– Microdata vs. aggregated results
– Numerous approaches (e.g...
Important Federated
Clinical Research Initiatives (1)

United States
• i2b2
• eMERGE
• Kaiser Permanente Research Program ...
Important Federated
Clinical Research Initiatives (2)
Europe
• European Medical Information Framework (EMIF)
• Delivering ...
EU Projects Unlocking the Data

Monte Carlo, 21.10.13

Prof. Dr. G. De Moor

33 of 66
The EHR4CR Consortium (1)

• 10 Pharmaceutical Companies (members of EFPIA)
• 23 Public Partners (Academia, Hospitals and ...
The EHR4CR Consortium (2)

Monte Carlo, 21.10.13

Prof. Dr. G. De Moor

35 of 66
EHR4CR Outputs

Project outputs:
A robust, scalable and market-ready Technical Platform
An Innovative Business Model and C...
The EHR4CR Services
• Clinical Trial Feasibility, i.e.
• Performing distributed queries
• Patient Recruitment, i.e.
• Dist...
The EHR4CR Platform

• The EHR4CR platform is
– a service platform which aims to unlock EHR data on an European/global
sca...
Architectural Principles
• Distributed Architecture
– Platform provides infrastructure and semantic services
• e.g. identi...
End-points (Recruitment & Feasibility )
• EHR4CR end-points at the clinical sites are crucial components
– Identifying pat...
Architectural Layers

ETL Services
I2B2 Connector
Message
Services

Monte Carlo, 21.10.13

Semantic Query
Expansion &
Medi...
‘Converged’ Clinical Trial Support Platform

• Projects with similar goals, converging on platform architecture through th...
EURECA
Semantic
Solution

…

Security & Privacy
Security & Privacy
Services

EHR4CR
Semantic
Solution

Platform Mgt
Servic...
… and beyond (pragmatic)

EURECA
Semantic
Solution

Model
Adaptors

Model
Adaptors

…

Security & Privacy
Security & Priva...
… Long Term Convergence

EHR4CR
Semantic
Solution

EURECA
Semantic
Solution

…

Security
Security
Services

Platform Mgt
S...
Interoperable Ecosystem

Monte Carlo, 21.10.13

Prof. Dr. G. De Moor

46 of 66
Some Existing Pilot Applications…
Protocol Feasibility

Patient Screening

Cohort Selection

Trial Recruitment

Monte Carl...
Roadmaps
EHR4CR Roadmap towards project (scientific) success
(1)
Protocol Feasibility

(2)
Patient Recruitment

(3)
EDC – ...
EHR4CR Business Model

A business model defines how an organisation
creates, delivers and captures VALUE

Monte Carlo, 21....
EHR4CR Outputs

Value Proposition
• The main reason why customers choose a product/service/provider
• It answers the quest...
EHR4CR Business model
The EHR4CR business model:
•
•
•
•
•
•
•
•
•

Specify in detail the product and service offering;
In...
Vision, Mission, Values

Monte Carlo, 21.10.13

Prof. Dr. G. De Moor

52 of 66
EHR4CR Outputs
Business Model Framework Uses Nine Building Blocks

Create
Value

Deliver
Value

Capture
Value

Source: ICT...
Stakeholders

1.
2.
3.
4.
5.
6.
7.
8.
9.

Patients
Clinicians (in Primary, Secondary and Tertiary Care settings)
Clinical ...
Benefits by stakeholder segment
• Patient perspective
– Improved mechanisms for inclusion in clinical trials
– Faster acce...
Benefits (1)

• Patients: EHR-integrated research platforms will provide a secure environment
to share health data and thu...
Benefits (2)

• Hospitals & healthcare organisations: enhance EHR data quality, management
reporting, performance benchmar...
Stakeholders and Forces in place
Who can influence? … the one who …

pays / invests ?

regulates ?

knows?
(other: e.g. th...
EHR4CR BMI and CBA

Business Model Innovation & Simulation
Forecasts the financial results for a EHR4CR service provider
•...
EHR4CR Outputs
Business Model Simulation Supports Financial Sustainability
• Uses the perspective of a service provider ov...
EHR4CR Outputs
Business Model Simulation Market Assumptions
•

–

•

•

(applied to an estimated market penetration of 5-1...
EHR4CR Outputs
Cost-Benefit Assessment (CBA)
Objective: To establish the value of EHR4CR services compared to current prac...
EHR review article

Monte Carlo, 21.10.13

Prof. Dr. G. De Moor

63 of 66
International Cooperation (1)

Promoting International Cooperation is one of the operational objectives of the
EC’s eHealt...
International Cooperation (2)
TRANS ATLANTIC PROJECT

Foreword by Herman Van Rompuy- E. Council President
Memorandum of Un...
Conclusions

• EHRs have a great potential to support clinical research
• There are a number of challenges to achieving th...
End

THANK YOU!
Prof. Dr. Georges J.E. De Moor
georges.demoor@ugent.be
http://www.eurorec.org
http://www.custodix.com
http...
ANY
QUESTIONS?

68
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Evolution 2013: Prof. Dr. Georges De Moor, EuroRec on Liberating Health Data to accelerate High Quality Clinical Research

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Evolution 2013: Prof. Dr. Georges De Moor, EuroRec on Liberating Health Data to accelerate High Quality Clinical Research

  1. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 1 of 66
  2. 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. Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 2 of 66
  3. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 3 of 66
  4. 4. Capture, Combine, Co-interpret Data from diverse Information Sources Population Registries, Clinical Trial Data-Bases, Bio-Bank data EHRs, PHRs, Ancillary DBs and other Clinical Applications Data Information Knowledge Social Networks Monte Carlo, 21.10.13 Care Pathways Systems, Decision Support Systems, Trends and Alerting Systems Prof. Dr. G. De Moor Mobile Devices, Apps (medical/well-being) Bio-sensors and Body Implants 4 of 66
  5. 5. Capture, Combine, Co-interpret Data from diverse Information Sources Clinical data “-Omics” data Environmental data (genomics, proteomics, metabolomics…) (pollution, nutrition…) Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 5 of 66
  6. 6. Leveraging Knowledge Discovery Data interpretation Information (Wisdom) interpretation Knowledge Decision Action Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 6 of 66
  7. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 7 of 66
  8. 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… Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 8 of 66
  9. 9. Shift from … to … (in care) Informed Healthcare Professionals Informed Patient-Care (EBM) Patient-Informed Care Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 9 of 66
  10. 10. Shift from … to … Patient - Trust - Physician ? ? ? Patient - Trust? - Health Networks ? ? Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 10 of 66
  11. 11. Convergence Initiative (of EuroRec) Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 11 of 66
  12. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 12 of 66
  13. 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 … Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 13 of 66
  14. 14. Data Sources for Clinical Research Data sources Advantages Disadvantages Electronic Health Record (EHR) at a single institution. Easy management of rights and consents. Full clinical content, structured and unstructured data. Possibly same semantics for all. Too few cases for many important studies. No general purpose research tools. Special Disease Registers at a regional or national level (often termed “Quality Registers”). Collect data from several institutions. Allow comparisons of results and larger samples. Well-defined data variables. Limited and relatively fixed data set. Changed rarely at the most yearly. No analyses of types of variables other than those collected. More complicated rights and consent management. Extra work to record data. In some cases possible to transfer data from an EHR. Often double registration in EHR and Quality Register. Special research database systems for specific projects (e.g. a regulated clinical trial). Very well-controlled variables including functions to ensure project process support and reasonable compliance. Expensive to set up for one project. Extra work because data cannot be retrieved from EHRs and extra work for clinical staff to transfer data from screen or paper to the research system. Federated system of electronic health records and special research project tools. May allow very large case populations, especially if federation across national borders. Semantic interoperability and consent are difficult to manage. Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 14 of 66
  15. 15. Focus Focus of this presentation the EHRs as data sources and the (re-)use of data for Clinical Research Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 15 of 66
  16. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 16 of 66
  17. 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 … Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 17 of 66
  18. 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… Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 18 of 66
  19. 19. EuroRec’s profile 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… Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 19 of 66
  20. 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? Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 20 of 66
  21. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 21 of 66
  22. 22. Example of a Representation of a Clinical Practice Guideline Refinement of the above statement Diagostic statement (which is an IE) with attribute suspected, on Heart Failure ECG Process Diagostic statement (which is an IE) with attribute unlikely, on Heart Failure Monte Carlo, 21.10.13 This is a CGP (which is, ontologically a plan, an information entity) to be used in a clinical context of the diagnosis "Suspected Heart Failure) Echo order (plan) Prof. Dr. G. De Moor 22 of 66
  23. 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) Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 23 of 66
  24. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 24 of 66
  25. 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) Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 25 of 66
  26. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 26 of 66
  27. 27. Pragmatic issues surrounding the Re-use of EHR data for Clinical Research Issue Identified problems Gaining retrospective consent Too difficult, too costly or requires disproportionate effort (e.g. patients may have moved or changed their names) Gaining broad prospective consent Difficult to ensure data subject is ‘fully informed’. Also, research methods and detailed research questions may change. Is broad consent still valid? Gaining dynamic consent Model in which the data subjects are continuously informed about the project progress and asked to reaffirm their consent with new directions seems to be the solution in the Internet age, but there are also good arguments against close inclusion of patients in research project steering Gaining early consent (as part of treatment) May be deemed ‘coercive’ Legal position of ‘nearly anonymised’ data It would help scientists to understand what is really expected from them to ensure compliancy when reusing EHRs for research Use of the ‘precautionary principle’ by data ‘gatekeepers’ Practical interpretation will be more restrictive than legislators intended Lack of consistency in interpretation of legal position between regulators or approval bodies, such as research ethics committees This is especially important where the consent process may be affected Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 27 of 66
  28. 28. EHR review article Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 28 of 66
  29. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 29 of 66
  30. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 30 of 66
  31. 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) Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 31 of 66
  32. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 32 of 66
  33. 33. EU Projects Unlocking the Data Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 33 of 66
  34. 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) Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 34 of 66
  35. 35. The EHR4CR Consortium (2) Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 35 of 66
  36. 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 and 5 countries) 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). Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 36 of 66
  37. 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 platform services) local applications (supported by the • 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) Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 37 of 66
  38. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 38 of 66 38
  39. 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) Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 39 of 66 39
  40. 40. End-points (Recruitment & Feasibility ) • EHR4CR end-points at the clinical sites are crucial components – Identifying patient information remains local on site – EHR integration relies on shadow systems, Clinical Data Warehouses (CDWs) Prot. Feas. Module EHR4CR CDW ETL EHR or CDW Data Source Module X NLP Data Access EHR4CR End-point EHR4CR End Interfaces Direct Query Interface EHR4CR Data Source End-Point Monte Carlo, 21.10.13 Prof. Dr. G. De Moor Central tools & services (e.g. protocol feasibility workbench) Local tools & services (e.g. patient recruitment workbench) 40 of 66 40
  41. 41. Architectural Layers ETL Services I2B2 Connector Message Services Monte Carlo, 21.10.13 Semantic Query Expansion & Mediation EHR4CR CDW AuthN & IDM & IDM Terminology Services Trusted Third Party (TTP) Services Infrastructure Services Trial Execution (EDC - CDMS) AuthZ AuthZ Data Access Services Patient Recruitment Workbenches @ End-points Audit Semantic Integration Services SAE Reporting Platform Management Service & Console Service & Console Protocol Feasibility Query End-points Central Trial Recruitment Security & Privacy Services Trial Registry Central Protocol Feasibility + Platform Mgt Services Application Services & End-user Applications Service Registry Prof. Dr. G. De Moor 41 of 66 41
  42. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 42 of 66 42
  43. 43. EURECA Semantic Solution … Security & Privacy Security & Privacy Services EHR4CR Semantic Solution Platform Mgt Services rvices Same technical platform, different semantic integration approaches (and applications) Platform Convergence Infrastructure Services EHR4CR CDW Monte Carlo, 21.10.13 EURECA CDW Prof. Dr. G. De Moor tranSMART 43 of 66 I2B2 43
  44. 44. … and beyond (pragmatic) EURECA Semantic Solution Model Adaptors Model Adaptors … Security & Privacy Security & Privacy Services EHR4CR Semantic Solution Platform Mgt Services rvices Pragmatic approach happening… Infrastructure Services EHR4CR CDW Monte Carlo, 21.10.13 EURECA CDW Prof. Dr. G. De Moor tranSMART 44 of 66 I2B2 44
  45. 45. … Long Term Convergence EHR4CR Semantic Solution EURECA Semantic Solution … Security Security Services Platform Mgt Services rvices Common Semantic Interface Infrastructure Services EHR4CR CDW Monte Carlo, 21.10.13 EURECA CDW tranSMART Prof. Dr. G. De Moor I2B2 45 of 66 45
  46. 46. Interoperable Ecosystem Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 46 of 66
  47. 47. Some Existing Pilot Applications… Protocol Feasibility Patient Screening Cohort Selection Trial Recruitment Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 47 of 66
  48. 48. Roadmaps EHR4CR Roadmap towards project (scientific) success (1) Protocol Feasibility (2) Patient Recruitment (3) EDC – EHR Integration (4) Drug Safety Surveillance 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 48 of 66 48
  49. 49. EHR4CR Business Model A business model defines how an organisation creates, delivers and captures VALUE Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 49 of 66
  50. 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 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 50 of 66
  51. 51. EHR4CR Business model The EHR4CR business model: • • • • • • • • • Specify in detail the product and service offering; Include analyses and an impact analysis on multiple stakeholders; Deliver a self-sustaining economic model including sensitivity analysis; Define appropriate governance arrangements for the platform services and for pan-European EHR4CR networks; Define operating procedures and trusted third party service requirements; Identify the value proposition and incentives for each of the key players and stakeholders impacted by EHR4CR; Define accreditation and certification plans/programs for EHR systems capable of interfacing with the platform; Provide a framework to define public and private sector roles in reusing EHRs for clinical research; Define a roadmap for pan-European/global adoption and for funding future developments. Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 51 of 66
  52. 52. Vision, Mission, Values Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 52 of 66
  53. 53. EHR4CR Outputs Business Model Framework Uses Nine Building Blocks Create Value Deliver Value Capture Value Source: ICTechnoloage 2013 Study on Business and Financing Models Related to ICT for Ageing Well Adapted from Osterwalder & Pigneur 2010 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 53 of 66
  54. 54. Stakeholders 1. 2. 3. 4. 5. 6. 7. 8. 9. Patients Clinicians (in Primary, Secondary and Tertiary Care settings) Clinical Investigators Contract Research Organisations (CROs) Pharmaceutical Industry Hospital Administrators Academia EHR Systems Vendors Trusted Third Parties (TTPs) and Trusted Services Providers (TSPs) 10. Health Authorities 11. Health Care Planners 12. Regulators Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 54 of 66
  55. 55. Benefits by stakeholder segment • Patient perspective – Improved mechanisms for inclusion in clinical trials – Faster access to innovative and safer treatments • Academic perspective – Increased efficiency of academic clinical studies – Enabled multi-center protocol designs • Pharmaceutical perspective – Increased clinical trial efficiency – Observational and outcomes research in real-world settings • Healthcare perspective – Enabling clinician participation in more clinical trials – Adding an additional revenue stream. Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 55 of 66
  56. 56. Benefits (1) • Patients: EHR-integrated research platforms will provide a secure environment to share health data and thus for advancing clinical research • Research Community: optimise research, processes and timelines • Pharmaceutical Industry: maximize R&D value chain • Contract Research Organisations: maximise value to customers and diversify revenue streams • Clinical investigators & Physicians: enable participation in a larger number of clinical trials • Regulatory Agencies: generate clinical evidence more rapidly for assisting regulatory decision-making • Public & Private Payers: enable further cost-effectiveness research Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 56 of 66
  57. 57. Benefits (2) • Hospitals & healthcare organisations: enhance EHR data quality, management reporting, performance benchmarking, image and revenues … • Academic Centres: generate more research opportunities and funding • ICT industry: open new business opportunities In general: the reuse of EHR data for clinical research will optimise clinical development towards achieving faster access to innovative medicines Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 57 of 66
  58. 58. Stakeholders and Forces in place Who can influence? … the one who … pays / invests ? regulates ? knows? (other: e.g. the one who owns the data?…) Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 58 of 66
  59. 59. EHR4CR BMI and CBA Business Model Innovation & Simulation Forecasts the financial results for a EHR4CR service provider • Based on estimated expenses and revenues • Balance sheets (revenues minus expenses) • Profitability ratio (revenues divided by expenses) Cost-Benefit Assessment Establishes the value of EHR4CR services versus current standards • Estimated costs and benefits from the perspective of the primary payer Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 59 of 66
  60. 60. EHR4CR Outputs Business Model Simulation Supports Financial Sustainability • Uses the perspective of a service provider over a 5-year time horizon • Pharmaceutical industry/CROs and clinical research units as primary customers • Based on willingness to pay and current market value (EU market) • Conservative assumptions generated by multidisciplinary expert task force • “Monte Carlo” simulations (10,000 iterations across all distribution ranges) as robust probabilistic sensitivity analysis Estimated Average of 3.9M € (yr1) - 27.3M € (yr 5) Monte Carlo, 21.10.13 Estimated Average of 1.78 (yr1) - 6.3 (yr5) Prof. Dr. G. De Moor 60 of 66
  61. 61. EHR4CR Outputs Business Model Simulation Market Assumptions • – • • (applied to an estimated market penetration of 5-10%) – Protocol Feasibility 5-yr Estimated # CT(Phase II-IV) in Europe Est. 250-500pts /CT 5-yr EHR4CR Market Uptake: 5-10% Est. # of Service Providers: 5-15 • • – Per-pt cost/CT: ~10,000 €/pt – 1.0-2.5% per-pt cost/CT/yr (fixed fee model) Includes certification/accreditation margins Monte Carlo, 21.10.13 Prof. Dr. G. De Moor Yr 1-2: 3-7% Yr 3-5: 7-20% Patient Identification • • EHR Data Access Cost – – EHR4CR platform annual registration fee EHR4CR fee per service (% per-pt cost/CT) • Protocol feasibility: 2-4% • Patient identification: 3-5% • Study conduct: 5-10% • SAE Reporting: 0.5% Estimated SP Yearly Target Objectives Estimated CT Costs – • Tier I: PRO (Pharmaceutical Research) Tier II: CRO (Contract Research Organisations) Tier III: CRU (Clinical Research Units) EU Market Landscape – – – – • 5 years (incl. yearly estimates) Customer Segments – – – • EHR4CR Services – – Service Provider Time Horizon – • • Perspective Yr 1-2: 15-30% Yr 3-5: 30-60% Study Conduct/SAE • • Yr 1-2: 1-5% Yr 3-5: 5-30% 61 of 66
  62. 62. EHR4CR Outputs Cost-Benefit Assessment (CBA) Objective: To establish the value of EHR4CR services compared to current practices Perspective: Pharmaceutical industry (primary payer) Focus: Oncology State-of-the-art: Multidisciplinary expert panel (health economists, academia, pharma) Methods: - Advanced simulation modelling & health technology assessment best practices - 20 models managing data variability (Monte-Carlo probabilistic sensitivity analyses) Data Sources: Resource utilization assessment validated by 6 EFPIA partners Monetary Benefits: Potential gains of actual development time saved with EHR4CR Preliminary Results: EHR4CR Annual Meeting Benefits BMI-Strategic Forum November 18-21, 2013, Berlin 62 Monte Carlo, 21.10.13 Prof. Dr. G. De Moor Costs 62 of 66
  63. 63. EHR review article Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 63 of 66
  64. 64. International Cooperation (1) Promoting International Cooperation is one of the operational objectives of the EC’s eHealth Action Plan 2012-2020, e.g.: With WHO and OECD: data collections and benchmarking With the US: building on the Memorandum of Understanding with the US on eHealth on Interoperable eHealth systems and ICT skills in Health Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 64 of 66
  65. 65. International Cooperation (2) TRANS ATLANTIC PROJECT Foreword by Herman Van Rompuy- E. Council President Memorandum of Understanding signed by: • Neelie Kroes - Eur. Commission Vice-President • Kathleen Sebelius – Secretary of HHS Policy briefs for Transatlantic cooperation • The current status of Certification of Electronic Health Records in the US and Europe • Semantic interoperability • Modeling and simulation of human physiology and diseases with a focus on the Virtual Physiological Human • Policy Needs and Options for a Common Approach towards Measuring Adoption, Usage and Benefits of eHealth • eHealth Informatics Workforce challenges Future TRANS ATLANTIC Cooperation? … on Reuse of Health data for Research… Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 65 of 66
  66. 66. Conclusions • EHRs have a great potential to support clinical research • There are a number of challenges to achieving this on a larger scale • Advanced EHR-integrated platforms will provide truly innovative solutions which promise to optimise clinical research Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 66 of 66
  67. 67. End THANK YOU! Prof. Dr. Georges J.E. De Moor georges.demoor@ugent.be http://www.eurorec.org http://www.custodix.com http://www.ehr4cr.eu Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 67 of 66
  68. 68. ANY QUESTIONS? 68

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