© CDISC 2012

David Moner, Juan Bru, José A. Maldonado, Montserrat Robles

Technical University of Valencia, Spain

damoca@upv.es

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Clinical Trials powered by

Electronic Health Records

© CDISC 2012

Contents

• Introduction

• Standard information models

• From data to knowledge

• From knowledge to clinical research

• Diabetes Mellitus: a use case

• Benefits

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© CDISC 2012

Introduction

• A big amount of resources and efforts have been

invested toward the adoption of EHR systems.

• This has clearly benefited healthcare delivery but

no so clearly clinical research.

• The reuse of EHR data is a unresolved matter

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© CDISC 2012

Introduction

• There are two main problems to resolve

EHR data quality and availability: we need a good

structure and a clear definition of the data; and tools to

ease its availability.

Different scopes: clinical research requires a greater

level of abstraction for data and concepts.

• Both problems can be solved by using the same

methodology:

An architecture guided by clinical information models.

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© CDISC 2012

Standard information models

• For a good representation of the EHR data we

need to use standards

BUT

• Standards are not the objective, but a means

toward a better description, management, re-use

and semantic interoperability.

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© CDISC 2012

Standard information models

• There are many standards such as HL7 CDA,

CDISC ODM, ISO 13606, openEHR, CCR…

• The important thing is not to choose only one, but

to choose the most appropriate for each

application case.

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© CDISC 2012

Standard information models

• A standard information model will provide basic

pieces and data structures for the persistence and

exchange of data.

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From data to knowledge

• Archetypes are a definition of a clinical model built

upon the pieces provided by a standard

information model.

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Data structure

+

Meaning

Archetype

© CDISC 2012

From data to knowledge

• An archetype defines the specific schema and

combination of data elements to represent an

interoperable dataset for a specific use case.

• We can use archetypes to extract, describe and

normalize existing data needed for each use case.

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Archetype

© CDISC 2012

From knowledge to clinical research

• Data in EHR systems can/must serve more than

the primary purpose of provision of healthcare.

New objective: re-use of data stored in the EHR for

clinical research purposes.

• The linking of clinical care information with clinical

research information systems requires a uniform

access to the existing and possibly distributed and

heterogeneous EHR systems.

Archetypes can help in this duty.

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© CDISC 2012

From knowledge to clinical research

• Clinical research, workflows, clinical guidelines

and decision support systems uses concepts with

a higher level of abstraction.

They are not associated with any specific EHR data.

• High level of abstraction provides independence

from lover-level implementation details that may

change with time or may vary across EHR.

Eg. ACEI (angiotensin-converting-enzyme inhibitor)

intolerant that abstracts away from raw data about

cough, hypotension, …

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© CDISC 2012

Diabetes Mellitus: a use case

• Diabetes Mellitus is becoming the pandemic of the

21st century, with a 7.5% of people diagnosed and

another 7.5% who does not know about their

illness.

• In clinical trial phase 4, monitoring of new

deployed products is an important step in the

clinical trial process.

• Taking into account the number of people who can

be treated by a new product, we need to find a fast

way to report new information and issues from

EHR systems to the clinical trial systems.

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© CDISC 2012

Diabetes Mellitus: a use case

• A Diabetes Mellitus research dataset can be composed of:

Glycated hemoglobin (HbA1c)

Glucose

Urea & electrolytes

Liver function tests

Lipid profile (cholesterol, HDL, LDL, triglycerides)

Thyroid function tests (TSH and free T4)

Albumin/Creatinine ratio

• Plus other relevant data

Problems (250.XX ICD-9 codes)

Adverse reactions

Prescriptions (ATC code, active ingredient, dose)

ECG

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© CDISC 2012

Diabetes Mellitus: a use case

• How can we design a seamless process to feed

the clinical trial information system from the

existing information at the EHR systems?

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© CDISC 2012

Diabetes Mellitus: a use case

• Step 1. Formally describe the needed EHR data

with a formal, computable and reusable format.

By defining archetypes for each information structure of

the EHR we provide a formal description of the concepts

used at the level of clinical care.

These will be clinical oriented archetypes, such as

medication prescription, discharge report and laboratory

result.

Archetypes can be defined and interpreted directly by

clinicians.

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© CDISC 2012

Diabetes Mellitus: a use case

• We use LinkEHR® Studio, a model-independent editor of archetypes.

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HL7 CDA

Patient summary archetype

© CDISC 2012

Diabetes Mellitus: a use case

• Step 2. Normalize existing data into standardized

documents following a specific standard and

archetype.

LinkEHR® Studio also helps in the duty of defining

bindings between a legacy database and an archetype.

It automatically generates a transformation program that

normalizes existing data into standard documents.

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© CDISC 2012

LinkEHR

Diabetes Mellitus: a use case

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Legacy data model

Legacy

data

Archetype Standard model

Transform

script

Standard

data

Follows FollowsGenerates

© CDISC 2012

Diabetes Mellitus: a use case

• Step 3. Abstract and enrich the data to make it

useful for a clinical study.

We create more abstract archetypes, suitable for clinical

research uses.

For example, we can reuse and enrich the prescription

data to create a complete medication archetype by

adding new information, such as the active ingredient,

the ATC code or the side effects of the medication.

Finally we can build a CDISC ODM archetype and use

CDISC CDASH to describe the information of the

diabetes research study.

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© CDISC 2012

Diabetes Mellitus: a use case

• Example of a CDISC ODM archetype defining the

data needed for a Diabetes study.

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© CDISC 2012

Diabetes Mellitus: a use case

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© CDISC 2012

Diabetes Mellitus: a use case

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© CDISC 2012

Benefits

• Clinical benefits

Close involvement of clinical experts.

Clinically-guided data flows.

Enables a quick feed and reuse of Health care data for

clinical research.

• Technical benefits

Quick development and deployment.

Facilitates the correct implementation of health

standards.

Eases the understanding of clinical and research

requirements.

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© CDISC 2012

Benefits

• Business benefits

Lower development and deployment costs.

Faster time-to-market by reducing technical

developments.

Standard-independent approach.

Future-proof solution, easily adaptable to changes.

Easy incorporation of new business cases (CDSS

interconnection, medical guidelines, alerts…).

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© CDISC 2012

David Moner

damoca@upv.es

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Thank you for your attention

Questions?