This document discusses how clinical trials can be powered by electronic health records (EHRs). It outlines a methodology using standard information models and archetypes to extract and normalize EHR data, abstract it to a higher level of meaning for clinical research, and integrate it into clinical trial systems. As a use case, it describes how diabetes patient data stored in EHRs could be formalized, transformed, and enriched to populate a clinical trial dataset using this approach. The benefits include closer clinical involvement, quicker reuse of EHR data for research, reduced costs and time to market, and a future-proof, standards-independent solution.

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2. Clinical Trials powered by
Electronic Health Records
David Moner, Juan Bru, José A. Maldonado, Montserrat Robles
Technical University of Valencia, Spain
damoca@upv.es
© CDISC 2012 2

3. Contents
• Introduction
• Standard information models
• From data to knowledge
• From knowledge to clinical research
• Diabetes Mellitus: a use case
• Benefits
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4. 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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5. 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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6. 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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7. 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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8. 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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9. From data to knowledge
• Archetypes are a definition of a clinical model built
upon the pieces provided by a standard
information model.
Data structure
+
Meaning
Archetype
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10. 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.
Archetype
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11. 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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12. 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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13. 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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14. 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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15. 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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16. 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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17. Diabetes Mellitus: a use case
• We use LinkEHR® Studio, a model-independent editor of archetypes.
HL7 CDA
Patient summary archetype
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18. 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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19. Diabetes Mellitus: a use case
LinkEHR
Legacy data model Archetype Standard model
Follows Generates Follows
Transform Standard
Legacy
script data
data
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20. 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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21. Diabetes Mellitus: a use case
• Example of a CDISC ODM archetype defining the
data needed for a Diabetes study.
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22. Diabetes Mellitus: a use case
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23. Diabetes Mellitus: a use case
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24. 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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25. 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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26. Thank you for your attention
Questions?
David Moner
damoca@upv.es
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