eit health is supported by eit, a body of the european union · • increased adoption of ehrs in...

From Electronic Health Records

to Electronic Data Capture

systems

EIT Health is supported by EIT,a body of the European Union

Using EHRs data as eSource; Preliminary results from the

EHR2EDC project and the TransFAIR study

Data Management Biomedical Association – 19 Octobre 2019

Laurent Luttenauer

World Wide Data Collection Data Steward Leader, Sanofi

Not to be used without written approval from EHR2EDC Consortium

Agenda

1. General principle of the project

2. Benefits

3. Objectives and Scope

4. Evaluation Study – TransFAIR study

5. Next steps and conclusion

2019 Assemblée Générale DMB | Paris | 19 Octobre 2019

eithealth.eu / #EITHealthEIT Health is supported by EIT,a body of the European Union

General principle of the project


Introduction

• Increased adoption of EHRs in hospitals➢ Large volume of electronic health data

➢ Increased availability for medical research

➢ Source of real-world evidence- Pharmacovigilance

• Challenges in clinical trials➢ Increase of study complexity

➢ Increase of study cost

➢ Complete enrollment on time

➢ Protocol amendments

➢ Almost 50% data available in EHRs is duplicated during clinical trial



Introduction

• Challenges to use EHR data in clinical research➢ Changing existing processes

➢ Different EHR systems at different sites

➢ Training personnel

➢ Data security

➢ Will regulatory agencies accept the data?

• FDA Guidance for Industry - Use of Electronic Health Record Data in

Clinical Investigations➢ Facilitate the use of EHR data in clinical investigations

➢ Promote the interoperability of EHR and EDC systems



Electronic Health Records to Electronic Data Capture

EHR2EDC project

• Consortium project ➢ EIT Health funded➢ Collaborative effort driven by industry partners, hospital partners, an

SME and a not-for-profit organization➢ Enable the transfer of clinical information from hospital EHRs to EDC

systems, so that EHR data may be reused during clinical trials

• Continuity of the EHR4CR project, which ended in a clinical research technology platform (InSiteTM). ➢ Optimize clinical protocols ➢ Support patient identification and recruitment➢ Data Inventory-Common data elements in clinical trials have been

identified and their availability in hospital systems elucidated.



Continuity of the projects

2011 2016

Champion Program

2018

Champion Program

S1S2

Evaluation

S3

Development

& Evaluation

2017

Market Access

S1S2

commercialization

2019

Research Project

S1S2

Developement

Automatic

transfer of

15%of manually

entered

protocol data


Objectives and Organization



Electronic Health Records to Electronic Data Capture,

EHR2EDC

• A new InSiteTM platform Service will ➢ enable direct data capture from EHRs to eCRF/EDC systems

➢ enable working across multiple EHR and EDC system vendors and

products

➢ ensure reliable exchange and re-use of EHR data for multiple research

initiatives

➢ fully comply with the European Union General Data Protection

Regulation

➢ fully comply with clinical trial regulatory requirements

➢ deliver a library of interoperability specifications connecting HL7 and

CDISC standards.


Hospital

EHRs

Hospital Data Warehouse

InSite

Research

Platform

Pseudo-

Anonymized data

Sponsor

EDC

Clinical Trial

Only Pseudo-

anonymized &

selected high

quality data

or

EHR2EDC Data Flow


Regulatory

GDPR

➔ If a clinical trial data process is already compliant with GDPR, EHR2EDC

won’t disturb it

FDA Guidances

• Electronic Source Data in Clinical Investigations (sept 2013)

• Use of Electronic Health Record Data in Clinical Investigation (July 2017)

All the project is build around and taking into account these guidances

and regulations



Benefits


Benefits - Sponsors

• Access to real time data➢ Continuous monitoring

• Avoid double data entry

• Avoid source data verification

• Enhanced quality

• Enhanced speed

• Reduced cost



Benefits – Hospital Partners

• Faster clinical setup

• Faster patient recruitment

• Reduced site burden (time and effort) for data collection in eCRF

• Data initially captured in different EHRs system within the hospital is

consolidated into one platform

• Improve data quality and value for research

• Enable multi-hospital collaborative research

• Provide new insights, research opportunities



Benefits expected for clinical trials

↓ Data entry

↓ Monitoring

↓ Data cleaning

↑ Quality

↑ Safety

Clinical ProjectManager

Sponsor DataManager

Monitor Data EntryTechnicien

Hospital EHRInteroperability

Manager

Workload

WithoutEHR2EDC

With EHR2EDC

Time & Cost

Savings



Objectives and Scope

EHR2EDC components

17

eSource Clinical data and Standards

Regulatory

Requirement Architecture and Technical Components

Hospital Engagement

Evaluation, Business and Sustainability

Project Management and Oversight



Objectives - eSource clinical data and standards

• How to implement the data flow?

➢ To identify the data items

• most relevant to clinical trial execution

• most likely to be documented in hospital EHR systems.

➢ To define standardized mappings, for these data items, from data structures and terminologies

• EHR systems CDISC-compliant EDC systems.

➢ To enable safe interpretation and acceptable traceability of EHR data acting as eSource

➢ To ensure that only data of sufficient quality is transferred from EHR to EDC systems.

➢ To develop tools for curating and maintaining data item mappings, and for making them more widely available after the project, under agreed terms.



Standards to use

• CDISC and FHIR

➢ Required by regulatory authorities

➢ Widely adopted in pharmaceutical industry

➢ CDISC standards support data traceability

➢ Adoption of HL7 FHIR is increasing in hospitals

➢ FHIR to CDISC mapping is in progress in industry, CDISC E2C

workgroup



EHR2EDC Mediation Model

20

• Hospital model 1

• Hospital model 2

EHR

• FHIR based CIM

• CIM to CDISC

CIM2CDISC Mapping • Pharma

model 1

• Pharma model 2

EDC



Data Element Repository

• Building Data element repository

➢ First phase – Data element inventory (commonly used in clinical trials and collected in EHRs)

➢ Second phase - Common Information Model (CIM) to formalize the data elements and make them available to support mapping activities.

• Data element Inventory

➢ Generic approach - Some of the standards studied in EHR4CR, pharmaceutical company standards resources were considered

➢ Study specific approach – eCRF of selected clinical trial is used to extract important data elements, and their availability at the project pilot hospitals will be assessed. This will lead to a last set of data elements to focus on within this project

• Common Information Model (CIM): Formal representation of the data items,

FHIR based

➢ Defined 7 FHIR profiles and associated standardized value sets to formally represent the data elements.




• Building Data element repository

22

Identify

• eCRF

• Data standard libraries

Standardize

• Reference standards terminologies

• CDASH coding

Evaluation and Integration to

CIM

• Occurrence

• Relevance

• Availability in EHR

• Quality

Data Elements Mapping

• CDISC-FHIR

mapping




• Data element Inventory example

23

• CDISC-FHIR

mapping




• Common Information Model example

24

• CDISC-FHIR

mapping



Data Element mapping to CDISC

• CDISC domain prioritization

➢ Domains most useful for pharma companies implied in EHR2EDC and the most available in hospital information system

➢ For each domain 6 step method

➢ Selection of the CDISC variables of interest for the project

➢ Structural mapping between the Common Information Model (attributes of the EHR2EDC FHIR Profiles) and CDISC (variables)

➢ Restriction of the semantic scope (restriction of the corresponding code lists to the frequently collected data element)

➢ Terminology mapping from the CIM terminologies to restricted CDISC codelist

➢ Validation of the whole work by pharma

➢ Validation of the whole work by hospital partners.




• The mappings are provided in Excel files. XX is CDISC domain name

• XX_model: list of CDISC variables of interest associated with the

corresponding attributes of the FHIR profiles of the EHR2EDC CIM and

the corresponding attributes in InSite model.

• XX_DE_list: list of relevant data elements for the CDISC domain, the

corresponding concepts in the reference terminologies, and the

mapping between these concepts and CDISC controlled terminology.




• XX_model example

27

• CDISC-FHIR

mapping




• XX_DE_list example

28

• CDISC-FHIR

mapping




• The EHR2EDC project proposes a standardization process allowing disparate information models and coding systems of participant sites to be harmonized to a standardized model and standard terminologies.

• Once hospital EHRs are connected to the EHR2EDC platform and source information models mapped to the EHR2EDC Common Information Model, distributed queries can be specified based on the EHR2EDC CIM and executed over heterogeneous sources.

• Routinely collected clinical data can be used for automatic pre-population of clinical trials eCRF.



• Consensus amongst companies to deliver data standards that works for all➢ Demographics, Vital Signs, Laboratory Data, Medications

• Initial governance and alignment for sustainability

30

Common Information Model

(InSite / FHIR)

Hospital A

Information model

Hospital B

Information model

Hospital C

Information model

Pharma A

Information model

Pharma B

Information model

Pharma B

Information model

Interoperability & Data Mapping Model



Evaluation Study – TransFAIR study


Goal of the evaluation protocol

• Several studies were selected to be managed in parallel with standard eCRF process and with EHR2EDC process➢ From different sponsors➢ In different sites

• Creation of mirror studies to collect EHR2EDC data

• At the end of the observation period, the databases are compared to identify:➢ Missing data➢ Transcription errors


EHR2EDC Evaluation protocol: TransFAIR study

Sanofi

MIR

RO

R s

tudy

CLIN

ICA

L s

tudy

MIR

RO

R s

tudy

CLIN

ICA

L s

tudy

Anonymized

data set

Janssen

MIR

RO

R s

tudy

CLIN

ICA

L s

tudy

MIR

RO

R s

tudy

CLIN

ICA

L s

tudy

AstraZenecaM

IRR

OR

stu

dy

CLIN

ICA

L s

tudy

MIR

RO

R s

tudy

CLIN

ICA

L s

tudy

Anonymized

data set

Anonymized

data set Anonymized

data set

Anonymized

data set Anonymized

data set

C

O

N

D

U

C

T

Study level of

analyze

Sponsor level of

analyze


TransFAIR Study – Evaluation protocol

Clinical study protocol

EHR2EDC Evaluation protocol - Sub Study

Information

Submission& Approvals

Endpoints- 15% of data correctly

transferred- Workload reduction- Quality increased

Screening, inclusion

Visit

Data collection on source document

Manual data entry into the eCRF

Semi-Automatic pre-population of an other eCRF under PI

control

EHRs

Sponsor Clinical Study Data Base

Sponsor EHR2EDC Data Base

12 de Octobre IRST

AP-HP

Consent



EHR2EDC Evaluation protocol – Strategy

De-risking the prinicipal clinical study ➔ Mirror study

Only in Hospital partners – With InSite already set-up

12 de Octobre – Madrid (Spain)

IRST – Meldola (Italy)

AP-HP – Paris (France)

2 different Studies = Different Budget + Different Timelines + Different

Objectives

No impact on the classical clinical study, in term of Cost, Quality,

Timelines, Recruitment



Budget – Timelines – Quality

• Budget : No impact ➔ EHR2EDC contract and budget

• Timelines : No impact ➔ Conducted in parallel, no depending of the classical clinical study

➔ No impact on the inclusions, 2 different studies : We will proposed the

participation to the mirror study once the patient has been already

included into the classical clinical study.

• Quality : No impact ➔ data not used for PV, regulatory …

2 different Studies = Different Budget + Different Timelines + Different

Objectives



Criteria to select the studies

• Conducted in at least one of our hospital partners

• Included patient during evaluation period (July to December 2019)

• High amount of local lab

• The PIs are well known by our hospital partners



Study Status

Timelines:

• 3 Local IRB approval:: between July and September

• First go live: 16 September (IRST – Italy)

• Data collection end: 22 December

• Analysis of discrepancies: February

• Publication of the results: End Q3 2020

Number of patients enrolled:

• Patients included (Consent obtained): 40

• Total number of patients planned: 46

Data collected:

• Domain : DM, VS, LB

• Issue in CM as medication are collected daily in EHR, need additional mapping to have by priod



Next steps and conclusion


Next steps

• With these first studies we prove that mapping is possible between EHR

and clinical database, we apply the strategy only theoretically define for the

moment.

• Continuing discussion with standard organizations➢ EIT: end November

➢ HL7 – CDISC: Interaction between HL7 and CDISC in December

• For 2020, the goal is to increase the number of domain to be transferred,

focus mainly on Oncology domains



Conclusion – New era in clinical research begins…

• High-quality data residing in EHR’s demonstrate a tremendous potential to support diverse research activities in pharma and health care industry

• CDISC standards will increase interoperability across multiple data sources ➢ data can be analyzed faster

• Pivotal change in clinical trials and patient care

• Reduced cost

➢ Promote new innovations in healthcare

➢ Medicines can reach patients faster



Thank you !

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