the opportunities for reusing ehrs for research › media › container › 1892_tors_a… · 2008...

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

16

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

23

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

24

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:

27

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

30

Personalised Health

Personalised care

Personalised medicine

32

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…


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


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


AUTOVALIDATE- study

▪ To use existing clinical outcome study to validate

▪ patient identification

▪ data collection

▪ Outcomes (efficacy and safety) collected in EHR platform.


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.


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%).


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


Patients who had their first Dx of AF before their first Dx of TIA

CONFIDENTIAL


Medication- anticoagulation prior to stroke (NOACs)

CONFIDENTIAL


Risk stratification, CHA2DS2-VASc, AF + stroke

CONFIDENTIAL


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

the opportunities for reusing ehrs for research › media › container › 1892_tors_a… · 2008...

Documents