“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
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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.
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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
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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
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Care Pathways Systems,
Decision Support Systems,
Trends and Alerting Systems
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Mobile Devices,
Apps (medical/well-being)
Bio-sensors and Body Implants
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Capture, Combine, Co-interpret Data
from diverse Information Sources
Clinical data
“-Omics” data
Environmental data
(genomics, proteomics, metabolomics…)
(pollution, nutrition…)
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Leveraging Knowledge Discovery
Data
interpretation
Information
(Wisdom)
interpretation
Knowledge
Decision
Action
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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
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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…
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Shift from … to … (in care)
Informed Healthcare Professionals
Informed Patient-Care (EBM)
Patient-Informed Care
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Shift from … to …
Patient - Trust - Physician
?
?
?
Patient - Trust? -
Health Networks
?
?
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Convergence Initiative (of EuroRec)
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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
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(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
…
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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.
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Focus
Focus of this presentation
the EHRs as data sources
and
the (re-)use of data for Clinical Research
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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
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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
…
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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…
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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…
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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?
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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
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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
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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)
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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)
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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
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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)
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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
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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
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EHR review article
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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
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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
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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)
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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
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EU Projects Unlocking the Data
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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)
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The EHR4CR Consortium (2)
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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).
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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)
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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
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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)
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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
Interfaces
Direct
Query
Interface
EHR4CR Data Source End-Point
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Central tools &
services
(e.g. protocol feasibility
workbench)
Local tools &
services
(e.g. patient
recruitment
workbench)
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Architectural Layers
ETL Services
I2B2 Connector
Message
Services
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Semantic Query
Expansion &
Mediation
EHR4CR
CDW
AuthN & IDM
Terminology Services
Trusted Third
Party (TTP)
Services
Infrastructure
Services
Trial
Execution
(EDC - CDMS)
AuthZ
Data Access
Services
Patient Recruitment
Workbenches
@ End-points
Audit
Semantic
Integration
Services
SAE Reporting
Platform Management
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
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‘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
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EURECA
Semantic
Solution
…
Security & Privacy
Services
EHR4CR
Semantic
Solution
Platform Mgt
Services
Same technical platform,
different semantic integration
approaches (and applications)
Platform Convergence
Infrastructure Services
EHR4CR CDW
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EURECA CDW
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tranSMART
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I2B2
43
… and beyond (pragmatic)
EURECA
Semantic
Solution
Model
Adaptors
Model
Adaptors
…
Security & Privacy
Services
EHR4CR
Semantic
Solution
Platform Mgt
Services
Pragmatic
approach
now
happening…
Infrastructure Services
EHR4CR CDW
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EURECA CDW
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tranSMART
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I2B2
44
… Long Term Convergence
EHR4CR
Semantic
Solution
EURECA
Semantic
Solution
…
Security
Services
Platform Mgt
Services
Common Semantic Interface
Infrastructure Services
EHR4CR CDW
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EURECA CDW tranSMART
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I2B2
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Interoperable Ecosystem
CDMS
EHR
CDMS
EHR4CR
Interconnect
EHR4CR Data
Source
Environment
EHR4CR
Interconnect
EHR4CR Data
Source Environment
EHR4CR Data
Source Environment
EHR4CR
Interconnect
EHR4CR
Interconnect
EHR4CR
Interconnect
Trusted Third
Party (TTP)
Service
Provider X
EHR4CR
End-Service
Provider
(instance 1)
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EHR4CR
Interconnect
EHR
EHR4CR
Interconnect
Shared
Repository
EHR4CR Data
Source
Environment
EHR
EHR4CR
Interconnect
EHR4CR
Interconnect
Instance of EHR4CR Platform
EHR4CR
End-Service
Provider
(instance N)
EHR4CR
Interconnect
Terminology
Translation
Service
EHR4CR
Interconnect
EHR
EHR4CR Data
Source
Environment
Instance of EHR4CR Platform
Interop Service
Y
Security
Service Z
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Some Existing Pilot Applications…
Protocol Feasibility
Patient Screening
Cohort Selection
Trial Recruitment
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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
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EHR4CR Business Model
A business model defines how an organisation
creates, delivers and captures VALUE
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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
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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
clinical units and 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.
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Vision, Mission, Values
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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
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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
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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.
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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
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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
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Stakeholders and Forces in place
Who can influence? … the one who …
pays / invests ?
regulates ?
knows?
(other: e.g. the one who owns the data?…)
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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
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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)
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Estimated Average of 1.78 (yr1) - 6.3 (yr5)
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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
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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%
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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:
Benefits
EHR4CR Annual Meeting
BMI-Strategic Forum
November 18-21, 2013, Berlin
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Costs
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EHR review article
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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
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International Cooperation (2)
TRANS ATLANTIC PROJECT
Foreword by Herman Van Rompuy‐ Eur. 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…
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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
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ANY
QUESTIONS?
Prof. Dr. Georges J.E. De Moor
[email protected]
http://www.eurorec.org
http://www.custodix.com
http://www.ehr4cr.eu
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