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

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

Prof. Dr. Georges De Moor

Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 1 of 66

Dept. of Medical Informatics and Statistics,Ghent University, Belgium & - RAMIT -

European Institute for Health Records - EuroRec -

- Custodix -

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


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.

• Amount of information to support medicine and healthcare is exploding

• ICT is transforming both biomedical research and healthcare (e-Health)

Introduction


• 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

Capture, Combine, Co-interpret Data

from diverse Information Sources

Population Registries,

Clinical Trial Data-Bases,

Bio-Bank data

Care Pathways Systems,

Decision Support Systems,

Trends and Alerting Systems

Data


EHRs, PHRs, Ancillary DBs

and other Clinical Applications

Mobile Devices,

Apps (medical/well-being)

Bio-sensors and Body ImplantsSocial Networks

DataInformationKnowledge

Capture, Combine, Co-interpret Data

from diverse Information Sources

Clinical data


“-Omics” data

(genomics, proteomics, metabolomics…)

Environmental data

(pollution, nutrition…)

Leveraging Knowledge Discovery

Data

Information

interpretation

interpretation

(Wisdom)


Knowledge

Decision

Action

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


• 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

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


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…

Shift from … to … (in care)

Informed Healthcare Professionals


Informed Patient-Care (EBM)

Patient-Informed Care

Shift from … to …

Patient - Trust - Physician

?

?

?


Patient - Trust? - Health Networks

?

??

Convergence Initiative (of EuroRec)


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

Controlled Clinical Trials

…Pharmaco-vigilance (non systematic list!)

Epidemiological studies

Public Health Research

Observational Research

(Clinical) Research


Disease Management studies

Comparative Effectiveness Research (older drugs, multiple diseases…)

Diagnostic Research

Continued Surveillance

Health Technology Assessment

Health Systems Research

Cost Effectiveness Research

…

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.

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.


“Quality Registers”). larger samples.

Well-defined data variables.

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.

Focus of this presentation

Focus


the EHRs as data sources

and

the (re-)use of data for Clinical Research

• 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

EHRs: where are we?


� 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

• 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

Challenge: Data Quality


� Correctness

� Completeness

� Accuracy

� Currency

� Validity

� …

The Data Content Issue

• Semantic Interoperability and Data Quality Markers:

- in CARE: Faithfulness (cf. biases in coding, window dressing for

reimbursement…)


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…

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


• 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…

• 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…)

Semantics: an important Challenge


• 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” formin a “computer processable” form?

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


• 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

Example of a Representation of a

Clinical Practice Guideline

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)

Diagostic

statement (which is

an IE) with

attribute

suspected, on

Heart Failure

Refinement of

the above

statement

ECG

Process


Echo order

(plan)Diagostic

statement (which is

an IE) with

attribute unlikely,

on Heart Failure

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)

Layered semantic models (1)


• 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)

In the domain of Clinical Research

• Clinical Data Interchange Standards Consortium (CDISC)

– Protocol Representation Model (PRM)

– Study Design Model (SDM)

– Operational Data Model (ODM)

Layered semantic models (2)


– 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

• Integrating the Healthcare Enterprise (IHE)

– Integration profiles

– IHE domain Quality, Research and Public Health (QRPH)

Layered Semantic Models (3)


• Cancer Data Standards Repository (caDSR)

• CDISC Shared Health and Research Electronic Library (CSHARE)

• 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

Ethical, Legal and Privacy Protection

challenges to Federated Research


systems

• Laws and regulations differ substantially

• Differences in law and ethical approaches and their interpretations create a

number of pragmatic issues

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

EHR review article


• 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

Consent vs. Trust model


• 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

• De-identification

– Microdata vs. aggregated results

– Numerous approaches (e.g. generalisation, suppression, global recoding,

etc …)

– K-anonymity

– Contextual anonymity

Privacy Protection and

Security measures


– Contextual anonymity

• Security

– ‘Basic’ security (authentication, authorisation and audit) is a fundamental

requirement of any IT system

– Access control management and enforcement

– Consent management

United States

• i2b2

• eMERGE

Important Federated

Clinical Research Initiatives (1)


• eMERGE

• Kaiser Permanente Research Program on Genes, Environment and Health

(RPGEH)

• Million Veteran Program

• Stanford Translational Research Integrated Database Environment (STRIDE)

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

Important Federated

Clinical Research Initiatives (2)


• 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

EU Projects Unlocking the Data


The EHR4CR Consortium (1)

• 10 Pharmaceutical Companies (members of EFPIA)

• 23 Public Partners (Academia, Hospitals and SMEs)

5 Subcontractors


• 5 Subcontractors

• One of the largest European public-private partnerships

• March 2011-February 2015: 4 years

• Budget: € +16 Million (EC DG Research & EFPIA)

The EHR4CR Consortium (2)


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).

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)


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)

• 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)

The EHR4CR Platform

Monte Carlo, 21.10.13 Prof. Dr. G. De Moor 38 of 66 38

• 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

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.


• 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)

End-points (Recruitment & Feasibility )

EH

R4

CR

En

d

Prot.

Feas.

Module

Direct

Query

Interface

Central tools &

services

(e.g. protocol 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)


EHR or

CDW

ETL

NLP

EHR4CR

CDW

EH

R4

CR

En

d-p

oin

t

Inte

rface

s

Module

Module

X Local tools &

services

(e.g. patient

recruitment

workbench)

(e.g. protocol feasibility

workbench)

Data Access EHR4CR Data Source End-PointData Source

Architectural Layers

Application Services & End-user Applications

Se

curity

&

Priv

acy

Se

rvice

s

Pla

tform

Mg

t

Se

rvice

sCentral

Protocol

Feasibility

Protocol

Feasibility Query

End-points

+

Au

thN

& ID

M

Au

thZ

Au

dit

Pla

tform

Ma

na

ge

me

nt

Se

rvice

& C

on

sole

Trial

Registry

Central Trial

Recruitment

Patient Recruitment

Workbenches

@ End-points

SAE Reporting

Trial

Execution

(EDC - CDMS)


Semantic

Integration

Services

Data Access

Services

Infrastructure

ServicesMessage

Services

Service

Registry

Terminology Services

& ID

M

Au

thZ

Pla

tform

Ma

na

ge

me

nt

Se

rvice

& C

on

sole

Semantic Query

Expansion &

MediationETL Services

I2B2 Connector EHR4CR

CDW

Tru

sted

Th

ird

Pa

rty (T

TP

)

Se

rvice

s

‘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

Platform Convergence

EHR4CR

Semantic

EURECA

Semantic …

Se

curity

& P

riva

cy

Se

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s

Pla

tform

Se

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s

Sa

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ica

l p

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orm

,

dif

fere

nt

sem

an

tic

inte

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n

ap

pro

ach

es

(an

d a

pp

lica

tio

ns)


Semantic

Solution

Semantic

Solution

…

Se

curity

& P

riva

cy

Se

rvice

s

Pla

tform

Mg

t

rvice

s

Infrastructure Services

tranSMARTEURECA CDWEHR4CR CDW

Sa

me

te

chn

ica

l p

latf

orm

,

dif

fere

nt

sem

an

tic

inte

gra

tio

n

ap

pro

ach

es

(an

d a

pp

lica

tio

ns)

I2B2

… and beyond (pragmatic)

EHR4CR

Semantic

EURECA

Semantic …

Se

curity

& P

riva

cy

Se

rvice

s

Pla

tform

Se

rvice

s

Pragmatic

approach


Semantic

Solution

Semantic

Solution

…

Se

curity

& P

riva

cy

Se

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s

Pla

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Mg

t

rvice

s


tranSMARTEURECA CDWEHR4CR CDW I2B2

Model

Adaptors

Model

Adaptors

approach

happening…

… Long Term ConvergenceS

ecu

rity

Se

rvice

s

Pla

tform

Se

rvice

s

Common Semantic Interface

EHR4CR EURECA


Se

curity

Se

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s

Pla

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Mg

t

rvice

s


tranSMARTEURECA CDWEHR4CR CDW I2B2

EHR4CR

Semantic

Solution

EURECA

Semantic

Solution

…

Interoperable Ecosystem


Some Existing Pilot Applications…

Protocol Feasibility Patient Screening


Cohort Selection Trial Recruitment

EHR4CR Roadmap towards project (scientific) success

Roadmaps

(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


– 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

EHR4CR Business Model

A business model defines how an organisation

creates, delivers and captures VALUE


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:

EHR4CR Outputs


• 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

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


• 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.

Vision, Mission, Values


Deliver Value

Create Value

Business Model Framework Uses Nine Building Blocks

EHR4CR Outputs


Source: ICTechnoloage 2013

Study on Business and Financing Models Related to ICT for Ageing Well

Adapted from Osterwalder & Pigneur 2010

CaptureValue

Stakeholders

1. Patients2. Clinicians (in Primary, Secondary and Tertiary Care settings)3. Clinical Investigators4. Contract Research Organisations (CROs)5. Pharmaceutical Industry6. Hospital Administrators


6. Hospital Administrators7. Academia8. EHR Systems Vendors9. Trusted Third Parties (TTPs) and Trusted Services Providers

(TSPs)10. Health Authorities11. Health Care Planners12. Regulators

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


– 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.

• 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

Benefits (1)


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

• 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

Benefits (2)


In general: the reuse of EHR data for clinical research will optimise clinical

development towards achieving faster access to innovative medicines

Stakeholders and Forces in place

Who can influence? … the one who …

pays / invests ?


regulates ?

knows?

(other: e.g. the one who owns the data?…)

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)

EHR4CR BMI and CBA


Cost-Benefit Assessment

Establishes the value of EHR4CR services versus current standards

• Estimated costs and benefits from the perspective of the primary payer

• 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

Business Model Simulation Supports Financial Sustainability

EHR4CR Outputs


Estimated Average of 3.9M € (yr1) - 27.3M € (yr 5) Estimated Average of 1.78 (yr1) - 6.3 (yr5)

• Perspective

– Service Provider

• Time Horizon

– 5 years (incl. yearly estimates)

• Customer Segments

– Tier I: PRO (Pharmaceutical Research)

– Tier II: CRO (Contract Research Organisations)

– Tier III: CRU (Clinical Research Units)

• EHR4CR Services

– 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

Business Model Simulation Market Assumptions

EHR4CR Outputs


– Tier III: CRU (Clinical Research Units)

• EU Market Landscape

– 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

• Estimated CT Costs

– Per-pt cost/CT: ~10,000 €/pt

• EHR Data Access Cost

– 1.0-2.5% per-pt cost/CT/yr (fixed fee model)

– Includes certification/accreditation margins

• Estimated SP Yearly Target Objectives

(applied to an estimated market penetration of 5-10%)

– Protocol Feasibility

• Yr 1-2: 3-7%

• Yr 3-5: 7-20%

– Patient Identification

• Yr 1-2: 15-30%

• Yr 3-5: 30-60%

– Study Conduct/SAE

• Yr 1-2: 1-5%

• Yr 3-5: 5-30%

EHR4CR Outputs

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

Cost-Benefit Assessment (CBA)


- 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

BMI-Strategic Forum

November 18-21, 2013, Berlin

Benefits

Costs

EHR review article


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 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

International Cooperation (2)

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

TRANS ATLANTIC PROJECT


• 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…

• EHRs have a great potential to support clinical research

• There are a number of challenges to achieving this on a larger scale

Conclusions


• Advanced EHR-integrated platforms will provide truly innovative solutions

which promise to optimise clinical research

End

THANK YOU!

Prof. Dr. Georges J.E. De Moor


Prof. Dr. Georges J.E. De Moor

[email protected]

http://www.eurorec.orghttp://www.custodix.com

http://www.ehr4cr.eu

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

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