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Electronic Health Records for Clinical Research
THE OPPORTUNITIES FOR
REUSING EHRS FOR
RESEARCH
Prof Dipak Kalra
President of i~HD
Age dependency ratio Age-related total spending in % of GDP
Ageing – global challenge
2
EU27
Japan
US
2010 2050
Number of working age population
for one older person (+65)
Multi-morbidity: the scale of the challenge
3
Chris Salisbury, Leigh Johnson, Sarah Purdy, Jose M Valderas and Alan A Montgomery
Br J Gen Pract 2011; 61 (582): e12-e21
Learning from (large scale) health data
▪ Kaiser Permanente is able to track outcomes and develop data driven
algorithms using the EHRs of its 9 million patients
▪ HIV death rate half of national average
▪ Decrease in coronary heart disease death rate by a third
▪ Decrease in pressure ulcers by two thirds
▪ Death due to sepsis reduced by > 50% (for all USA, would save 72,000 lives p.a.)
4
5
Genomic data
Population registries,
Clinical trials databases
Bio-sensorsClinical
applications
Care pathways,
decision support,
trends and alerts
Mobile devicesEnvironmental data
Social networks
The Digital Citizen
Scaling up International Patient Summaries
6
▪ Trillium II is promoting IPS as an active window to a person’s health data:
a landing page securely accessible across locations & jurisdictions
Vaccinations
Medications
Encounters
Identification Health problems
Implantable devices
Care team
Security preferences
Procedures
Treatment plan
Allergies
Great potential for learning health systems
Digital health and care innovation in the
EU Digital Single Market
7
3 multi-stakeholder communities to support digital innovation and transformation
Citizens’ secure access to and sharing of health data across borders
Better data to advance research, disease prevention and personalised health and care
Digital tools for citizen empowerment and person-centred care
Partnerships for large scale deployment of digital solutions for person-centred integrated care
Analysis of high impact scenarios
Twinning to promote successful large scale innovations
Building blocks for scaling up
towards practicetowards policy
Real World Evidence
RWE opportunities for life sciences and medicines
development
▪ Quantify disease diversity and unmet treatment needs
▪ Biomarker discovery and validation
▪ Quantify deeply-stratified populations, for targeted therapies
▪ Accelerate the conduct of clinical trials
▪ Outcomes research, comparative effectiveness research
▪ Safety signal detection and validation
▪ New treatment indication areas
▪ Adaptive trials and licensing
▪ Evidence to underpin value based health care models
▪ Collaborate to maximise health outcomes
8
IMI – Europe’s partnership for health
9
Partnership
2008 - 2024€2.5 bn
> €5 bn
€2.5 bn
IMI2: 2014-2024€3.3 bn budget
More ambitious
More open
Greater scope
IMI1: 2008-2013€2 bn budget
59 projects
Slide courtesy of Pierre Meulien, Executive Director of IMI
Patient recruitment a major cause of trial delays
▪ Identifying and recruiting suitable patients and trial sites are principal causes of trial delays
10
50% of today’s clinical trials fail to achieve the target recruitment rate4
Almost
half of all trial
delays caused by patient recruitment problems2
1. State of the Clinical Trials Industry: A Sourcebook of Charts and Statistics, Center Watch, 2008.2. Study Participant Recruitment and Retention in Clinical Trials: Emerging strategies in Europe, the US and Asia, Business Insights, June 2007.3. Beasley, “Recruiting” 20084. Tufts -http://clinicalperformancepartners.com/wp-content/uploads/2012/07/Fixing-Feasibility-Final-Jan-2012.pdf
Each day a drug is delayed from market, sponsors lose up to
$8m3
The percentage of studies that complete enrolment on time:
18% in Europe,
7% in the US1
The EHR4CR project
▪ EHR4CR – Electronic Health Records for Clinical Research
▪ 4+1 year project (2011-2016), 35 partners, budget >17M€
▪ The mission
▪ Provide a platform for trustworthy re-use of EHR data to support innovation
in clinical research and healthcare operations
▪ The outcome
▪ A platform connecting securely to the data within multiple hospital EHR systems:
▪ predict the number of eligible patients for a candidate clinical trial protocol
▪ assess its feasibility and to locate the most relevant hospital sites
▪ efficiently identify and contact the patients who may be eligible for particular clinical trials
▪ A not for profit institute to drive the success conditions for multi-stakeholder benefits from health data:
i~HD
11
The EHR4CR ambition
▪ Research and develop a trustworthy service platform able to unlock clinical information stored in EHRs for improving clinical research
▪ Clear focus on three (3) relevant use cases
12
SAFETY REPORTINGPROTOCOL FEASIBILITY
PATIENT RECRUITMENT
DATA CAPTURE AND
EXCHANGE
Enabling protocol testing with
real world data in potential trial
sites rather than with guestimates.
Speeding up recruitment by making
EHR data searchable for
investigators and establishing a
unified communication path
between sponsors and sites.
Facilitating EHR data extraction for
applications used during trial
execution (e.g. prefilling of CRFs and
of SAE reports).
Electronic Health Records for Clinical Research 13
Confirming public acceptance
▪ High percentage of respondents were in favour of re-using EHR
data for research
A European inventory of
common electronic health
record data elements for
clinical trial feasibility
Justin Doods, Florence Botteri, Martin Dugas,
Fleur Fritz and on behalf of EHR4CR WP7
Trials 2014, 15:18
http://www.trialsjournal.com/content/15/1/18
14
Confirming dataavailability
Confirming a robust and sustainable business model
15
Value to hospitals + value to all health stakeholders
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Better data access, and tools, to analyse their own data
Efficient capability to conduct research
Stronger drive to improve data quality
Ability to measure health outcomes and improve care
PROTOCOL
FEASIBILITY
PATIENT
RECRUITMENT
Enabling protocol testing with
real world data in potential trial
sites rather than with guestimates.
Speeding up recruitment by making
EHR data searchable for
investigators and establishing a
unified communication path
between sponsors and sites.
Facilitating EHR data extraction for
applications used during trial
execution (e.g. pre-filling of CRFs
and of SAE reports).
EHR2EDC
EHR2EDC is the final step to realise
the original ambition
17
SAFETY REPORTING
DATA CAPTURE AND
EXCHANGE
18Dr Peter Arlett, EMA, 2016
Discoveries from >1m patient clinical data repositories
▪ Validating >200 novel biomarkers predicting cardiovascular risk
▪ Investigating variation of 174,000 observed national prescribing
patterns to national guidelines for COPD
▪ Comparing ~8,000 treatment outcomes for leukaemia by age:
uncovering a major unmet treatment need
▪ Developing new cancer risk stratification algorithms by mining >700
million records
19
To become the
trusted European
hub for health care
data intelligence,
enabling new
insights into diseases
and treatments
EMIF’s mission
20
Discover
Assess
Reuse
EMIF catalogue: meta-data of available datasets (emif-
catalogue.eu)
21
Catalogue – data source profiles
22Source: Rudi Verbeeck - Janssen
Data harmonization
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Cohort 1
Cohort 2
Cohort n
C1 C2 … Cn
V1
V2
⁞
Vn
c
c
c
c
c
c
c
c
c
c
c
c
C
C
C
C
C
?
?
?
Data custodians• Identify local concepts• Specify mappings
• Define security
Community• Specify global and derived concepts
• Define research groups
Local concepts Global concepts
Source: Rudi Verbeeck and Michel Van Speybroeck - Janssen
EHDEN
European Health Data & Evidence Network
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Institute Country
Erasmus MC The Netherlands
Synapse Research Management Spain
Oxford University UK
Tartu Ulikool Estonia
University of Aveiro Portugal
The Hyve The Netherlands
Odysseus Data Services Czech Republic
European Patients Forum Luxembourg
National Institute for Health and Care Excellence (NICE) UK
Stiftelsen WHO Collaborating Centre for International Drug Monitoring
Sweden
International Consortium for Health Outcomes Measurement (ICHOM)
UK
Slide courtesy of Nige Hughes, Janssen
i~HD is registered in Belgium as a not-for-profit organisation
It is financed by membership fees,
by providing services e.g. data quality, certification and governance
and through funded projects
The European Institute for Innovation through Health Data (i~HD)
was created as an outcome of European R&D projects, to address needs
confirmed by multiple healthcare and research stakeholders
Developing solutions for improving health data and its trustworthy use
Enriching knowledge and enhancing care through health data
i~HD targets a convergence of opportunity from
health data
Need to collaborate to improve access to combined
health data from multiple sources
Clinical Research
• Conduct faster, more efficient, clinical
research
• Demonstrate the benefit from
innovative products
• Create better Real World Evidence
• Generate new evidence for precision
medicine and value based models
Healthcare
• Improve quality, safety and
connectedness of care
• Empower patients in self-care and
health maintenance
• Use outcomes to improve services
• Have better evidence for public health
strategies
26
i~HD is bringing stakeholders together:
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Citizen and
patient
associations
Clinical and
biomedical
research
companies
Health data
aggregators
and analytics
companies
ICT
companies,
standards
developers
Scientific
centres,
Reference
Networks
Health system
funders, care
commissioners
Multi-national
decision
makers
Healthcare
providers and
provider
organisations
to co-create solutions for:
• the capture and sharing of
better quality health data
• its trustworthy use for
smarter health care and
efficient research
i~HD is part of a governance landscape emerging across Europe
28
GDPR
IMI code of practice on secondary use of medical data in scientific research projects
BBMRI-ERIC Code of Conduct
on processing of personal data
for purposes of scientific
research in the area of health
EMIF data sharing code of
practice
CORBEL data sharing of
academic clinical trial data
RD-CONNECT code of
practice for data and
samples
ENCePP Code of Conduct
ADVANCE Code of Conduct
using health data for research sharing health data for research
i~HD: implementation of codes of practice, compliance with the GDPR
——————
Educate
research and ICT
staff
Certify
EHR and research
systems
Promote consistent
practices across
Europe
Develop principles
and operational
practices
Promoting multi-stakeholder engagement in
standards profiling and adoption
29
Clinician and patient involvement
Technical specifications
Clinical specifications
Slide courtesy of Robert Vander Stichele
modelling, terminology, ontology
& workflow representations
to produce harmonised semantic
resources
Research and public health involvement
i~HD Data Quality assessment of hospital EHRs
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Personalised Health
Personalised care
Personalised medicine
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Clinical
understanding of
disease
Molecular
understanding of
disease
Patient
preferences,
goals, lifestyle
Health system
capability and
capacity
Evidence Based
Medicine
Predictive
modelling
Innovative
medicines
Targeted therapy
New integrated
care models
New
reimbursement
models
Citizen
empowerment
(promoting
wellness)
…need all stakeholders to collaborate,
to maximise the value we bring from health data
and Learning Health Systems…
Electronic Health Records for Clinical Research
ACCEPTANCE CRITERIA
FOR HOSPITALS TO
CONNECT TO A FEDERATED
RESEARCH PLATFORM
Slides from:
Ole Fröbert, Örebro University
A new tool – The InSite platform
for identifying eligible patients for randomized trials
InSite – Örebro University Hospital case example
Implementation
• Involving IT experts, IT architects, patient safety representatives, lawyers, heads of R&D
• 2-year process
• Securing GDPR compliance and data safety
• Costs (hours of work by hospital staff)
• Test phase offline
• Going live
Challenges
• Explaining to stakeholders what InSite is and that identifiable person data are not shared
• Explaining that InSite puts the hospital on the map for pharma identifying us as a research intensive institution
• Once up and running InSite was put on hold by a government office fearing data leaks (very Swedish)
InSite experience
• Very smooth handling after implementation
• Study queries are emailed to local PI´s and approved
• We have approved all study requests and received two proposals for actual trial participation after that
• Personal view: all university hospitals and research active non-academic hospitals should join
• Some would say that we should not “help” Big Pharma – but InSite helps to bring down costs which could later be used as leverage for keeping down medical expenses
• InSite can be used for internal quality checks and as a tool to prepare own studies
InSite perspective
• The platform is suitable for EHR-based randomized clinical trials
• With InSite no dedicated eCRF for baseline and outcome data is needed – all can theoretically be handled within the system:
– Cheap– Fast– Less work– Quality data checks will be needed
• We have previously used a similar concept when introducing the Registry-based randomized clinical trial concept (four guideline-changing studies in NEJM)
Ole Fröbert, MD, PhD,
FESC
Electronic Health Records for Clinical Research
PRACTICAL EXAMPLE FROM
UTILISING ELECTRONIC HEALTH
RECORDS IN CLINICAL
RESEARCH
Juuso Blomster, MD, PhD
Associate Professor in Cardiology
Chief Physician, Research Services
Turku University Hospital, Turku, Finland
Electronic Health Records for Clinical Research
AUTOVALIDATE- study
▪ To use existing clinical outcome study to validate
▪ patient identification
▪ data collection
▪ Outcomes (efficacy and safety) collected in EHR platform.
Electronic Health Records for Clinical Research
FibStroke study as reference data
▪ Stroke can be first sign of undetected atrial fibrillation in up to 2/5
of stroke cases.
▪ To analyze the associations between stroke and the time of
diagnosis of atrial fibrillation and to identify the circumstances
predisposing factor to stroke.
▪ Methods: The FibStroke registry includes 4311 patients with
previously diagnosed AF, who suffered 3252 ischemic strokes,
956 transient ischemic attacks and 794 intracranial bleeds during
2003-2012.
Electronic Health Records for Clinical Research
Fibstroke results
▪ 1) Almost half (49.1%) of the ischemic strokes and transient ischemic attacks occurred in patients who were not using OAC
▪ 2) In patients with paroxysmal AF, 6.4% of the strokes occurred after cardioversion of AF. Of these strokes, 78.2% occurred after cardioversion of acute AF, while 65.4% occurred to patients who were not using OAC
▪ 3) Postoperative ischemic strokes accounted for 6.0% of all strokes in patients with AF. Previously used OAC was interrupted for 81.2% of the operations preceding ischemic stroke. Of the postoperative intracranial bleeds, LMWH bridging was used in 54.5% of the operations.
▪ 4) Mortality during the 30 days following a stroke was significantly lower in patients with paroxysmal AF compared to patients with chronic AF (10.2% vs 20.3%).
Electronic Health Records for Clinical Research
Methods
▪ Patient identification; demographics, risk factors
▪ Outcome follow-up; “retrospective” 2003-2011 and “prospective” 2012-2016
▪ Co-morbidities, medications and adverse events
▪ INCLUSION CRITERIA
▪ Adults (>18 years) on 1.1.2003
▪ Disturbances in cerebral blood flow during 2003-2012:
▪ Ischemic stroke, aivoinfarkti I60.0-I60.9, I61.0 - I61.9, I62.0 - I62.9, I63.0 -I63.9, I65.0-I65.9, I66.0-I66.9, I69.0 - I69.9 or
▪ TIA G45.0 - G45.9, G46.0 - G46.9 or
▪ Intracranial bleeding S06.0- S06.9
▪ Had ever been diagnosed with Atrial Fibrillation (AF) or atrial flutter I48
Electronic Health Records for Clinical Research CONFIDENTIAL
Patients who had their first Dx of AF before their first Dx of Stroke
Electronic Health Records for Clinical Research
Patients who had their first Dx of AF before their first Dx of TIA
CONFIDENTIAL
Electronic Health Records for Clinical Research
Medication- anticoagulation prior to stroke (NOACs)
CONFIDENTIAL
Electronic Health Records for Clinical Research
Risk stratification, CHA2DS2-VASc, AF + stroke
CONFIDENTIAL
Electronic Health Records for Clinical Research
Conclusions
▪ EHR platforms addressing study feasibility and patient recruitment
functionality is well established
▪ Electronic data capture directly from EHRs would complete the
EHR services and facilitate clinical research to better level of
efficiency
▪ Based on our results, extraction of event, risk factor and
medication data is possible in a cohort setting
▪ Additional development is required from the platform providers to
enable fluent follow-up and data collection