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

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

“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 -


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.


• 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

Introduction


Capture, Combine, Co-interpret Datafrom diverse Information Sources

Population Registries,Clinical Trial Data-Bases,Bio-Bank data

EHRs, PHRs, Ancillary DBs and other Clinical Applications

Care Pathways Systems, Decision Support Systems, Trends and Alerting Systems

Mobile Devices,Apps (medical/well-being)Bio-sensors and Body ImplantsSocial Networks

DataInformationKnowledge


Capture, Combine, Co-interpret Data from diverse Information Sources

“-Omics” data (genomics, proteomics, metabolomics…)

Environmental data(pollution, nutrition…)

Clinical data


Leveraging Knowledge Discovery

Data

Information

Knowledge

interpretation

interpretation

Decision

Action

(Wisdom)


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


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…


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)

SmartCare


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 studiesPublic Health ResearchObservational ResearchDisease Management studiesComparative Effectiveness Research (older drugs, multiple diseases…)Diagnostic ResearchContinued SurveillanceHealth Technology AssessmentHealth Systems ResearchCost Effectiveness Research

…

(Clinical) 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 andunstructured data. Possibly samesemantics 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 andlarger 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. Morecomplicated 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 regulatedclinical 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 ofelectronic health recordsand special researchproject 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

the EHRs as data sourcesand

the (re-)use of data for Clinical Research

Focus


• 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

EHRs: where are we?


• 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 …

Challenge: Data Quality


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…


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…


• 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” formin a “computer processable” form?

Semantics: an important Challenge


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


Example of a Representation of a Clinical Practice Guideline

This is a CGP (which is, ontologically a plan, an information entity) tobe used in a clinicalcontext of thediagnosis "SuspectedHeart Failure)

Diagosticstatement (which isan IE) withattributesuspected, on Heart Failure

Refinement ofthe abovestatement

Echo order(plan)

ECG Process

Diagosticstatement (which isan IE) withattribute unlikely, on Heart Failure


Objective : semantic interoperability between diverse systemsStandards 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)

Layered semantic models (1)


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

Layered semantic models (2)


• 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)

Layered Semantic Models (3)


• 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

Ethical, Legal and Privacy Protection challenges to Federated Research


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 themto 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

• 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

Consent vs. Trust model


• 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

Privacy Protection and Security measures


United States

• i2b2• eMERGE• Kaiser Permanente Research Program on Genes, Environment and Health

(RPGEH)• Million Veteran Program• Stanford Translational Research Integrated Database Environment (STRIDE)

Important Federated Clinical Research Initiatives (1)


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

Important Federated Clinical Research Initiatives (2)


EU Projects Unlocking the Data


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)


The EHR4CR Consortium (2)

http://www.uni-duesseldorf.de/

http://www.embl.de/index.php

http://www.eclinicalforum.org/main/Home/tabid/36/Default.aspx

http://www.dundee.ac.uk/

http://www.inserm.fr/

http://www.hug-ge.ch/index.html

http://www.xclinical.com/en/home_de/

http://www.manchester.ac.uk/index.php

http://www.google.se/imgres?imgurl=http://ceoworld.biz/ceo/wp-content/uploads/2009/09/novartis-logo.jpg&imgrefurl=http://ceoworld.biz/ceo/2009/09/03/novartis-cell-culture-based-h1n1-swine-flu-vaccine-celtura-may-work-on-fewer-doses&usg=__rhZisG-3JYL2r9FHqlMMClbgRZ8=&h=446&w=1846&sz=57&hl=sv&start=1&zoom=1&itbs=1&tbnid=iy0utMYOJqhEPM:&tbnh=36&tbnw=150&prev=/images?q=Novartis&hl=sv&sa=G&gbv=2&tbs=isch:1


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).


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)

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

• 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

The EHR4CR Platform


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)


EHR or CDW

End-points (Recruitment & Feasibility )

ETL

NLP

EHR4CR CDW

EHR4CR End-point Interfaces

Prot. Feas.

Module

Module X

Direct Query

Interface

Local tools & services(e.g. patient recruitment workbench)

Central tools & services(e.g. protocol feasibility workbench)

• 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)

Data Access EHR4CR Data Source End-PointData Source


Architectural Layers

Semantic Integration Services

Application Services & End-user Applications

Security &Privacy Services

Data AccessServices

InfrastructureServices

Platform

Mgt

Services

Message Services

Service Registry

Central Protocol

Feasibility

Protocol Feasibility Query

End-points

+

Terminology Services

AuthN &

IDM

AuthZAudit

Platform M

anagement

Service & Console

Trial Registry

Central Trial Recruitment

Patient Recruitment Workbenches @ End-points

SAE Reporting

Semantic Query Expansion & MediationETL Services

I2B2 Connector EHR4CR CDW

Trusted Third Party (TTP)

Services

Trial Execution

(EDC - CDMS)


• 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

‘Converged’ Clinical Trial Support Platform


Platform Convergence

EHR4CR Semantic Solution

EURECA Semantic Solution

…

Security & Privacy

Services

Platform M

gtServices

Infrastructure Services

tranSMARTEURECA CDWEHR4CR CDW

Sam

e te

chni

cal p

latfo

rm,

diffe

rent

sem

antic

inte

grati

on

appr

oach

es (a

nd a

pplic

ation

s)

I2B2


… and beyond (pragmatic)

EHR4CR Semantic Solution


…

Security & Privacy

Services

Platform M

gtServices


tranSMARTEURECA CDWEHR4CR CDW I2B2

Model Adaptors

Model Adaptors

Pragmatic approach nowhappening…


… Long Term ConvergenceSecurity Services

Platform M

gtServices


tranSMARTEURECA CDWEHR4CR CDW I2B2

Common Semantic InterfaceEHR4CR

Semantic Solution


…


Interoperable Ecosystem

EHR

EHR

EHR4CR Data Source Environment

EHR

EHR4CR End-Service

Provider(instance 1)

Shared Repository

EHR4

CR D

ata

Sour

ce

Envir

onm

ent

CDMS

EHR

EHR4

CR D

ata

Sour

ce

Envir

onm

ent

EHR4CR Interconnect

EHR4

CR

Inte

rcon

nect

EHR4

CR

Inte

rcon

nect

Trusted Third Party (TTP)

Service Provider X

EHR4

CR

Inte

rcon

nect

EHR4CR End-Service

Provider(instance N)EH

R4CR

In

terc

onne

ct

EHR4CR Interconnect

Instance of EHR4CR Platform

Instance of EHR4CR Platform

Interop Service Y

EHR4CR Interconnect

Security Service Z

EHR4CR Interconnect

EHR4CR Data Source Environment

EHR4CR Interconnect

Terminology Translation

Service EHR4

CR

Inte

rcon

nect

EHR4

CR D

ata

Sour

ce

Envir

onm

ent

EHR4

CR

Inte

rcon

nectCDMS


Some Existing Pilot Applications…

Protocol Feasibility Patient Screening

Cohort Selection Trial Recruitment

https://cohort-integrate.internal.custodix.com:8443/CohortSelectionClient/index.jsf

https://vr164023.univ-rennes1.fr/login

https://recruit-eureca.custodix.com/trc/index.jsf


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– 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:

• 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 Outputs


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.


Vision, Mission, Values


Source: ICTechnoloage 2013 Study on Business and Financing Models Related to ICT for Ageing Well

Adapted from Osterwalder & Pigneur 2010

Deliver Value

Create Value

CaptureValue

Business Model Framework Uses Nine Building Blocks

EHR4CR Outputs


Stakeholders

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

• 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• 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

Benefits (1)


• 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

Benefits (2)


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 & SimulationForecasts 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 AssessmentEstablishes the value of EHR4CR services versus current standards

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

EHR4CR BMI and CBA


• 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

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

EHR4CR Outputs


• 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)

• 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

• 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 (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%

Business Model Simulation Market Assumptions

EHR4CR Outputs


EHR4CR Outputs

Objective: To establish the value of EHR4CR services compared to current practicesPerspective: 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 EHR4CRPreliminary Results:

EHR4CR Annual Meeting BMI-Strategic Forum November 18-21, 2013, Berlin

Benefits

Costs

Cost-Benefit Assessment (CBA)


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 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 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

TRANS ATLANTIC PROJECT

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

• Advanced EHR-integrated platforms will provide truly innovative solutions which promise to optimise clinical research

Conclusions

ANY QUESTIONS?

67

Prof. Dr. Georges J.E. De Moor

[email protected]

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

http://www.ehr4cr.eu

mailto:[email protected]

http://www.eurorec.org/

http://www.custodix.com/

http://www.ehr4cr.eu/

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

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