After many years of existence, Electronic Health Record Systems (EHRS) adoption in both hospital and primary care centers is close to 100% in some European countries. Millions of personal health records, containing valuable clinical information, are ready to be used in more and more health processes. Pharmaceutical Clinical Trials are one of this health related processes which have very high expectations in the use of EHRS data. Clinical Trial Management Systems (CTMS) and Clinical Data Systems (CDS) would improve their processes by accessing this EHRS data. Nevertheless, legal and technical aspects are making difficult this use. Focusing on technical issues, there exist standards for representing both the EHR information (such as HL7 CDA, CEN/ISO 13606 or openEHR), and standards for clinical trial studies (such as CDIS CDASH and CDISC ODM). But there is a lack of interoperability between them all, and an imprecise way for the definition of the data sets to be shared. This paper will present an ICT infrastructure to enable the semantic interoperability of EHRS and CTMS by means of scalable and standardised Virtual Health Records (VHR) and by a clear definition of the data to be exchanged. The infrastructure focuses on generic methods in order to simplify and standardise the way in which clinical research systems acquire data from heterogeneous EHRS. A VHR mediator system connects both sides through a hub where processes are able to transfer data in both senses. Data structures will be described through CDISC ODM and CDISC CDASH in the form of computable semantic concept definitions. The presented model will include methodology, processes, architecture and existing software components. Advantages of this model are: (1) It is independent of existing standards, software and architecture of EHRS. (2) Allows reach level 3 making EHR and CR systems fully interoperable. (3) Allows fast solution development adaptable to fit different scenarios. A model like this can be keystone in the way to reach fully collaboration between health and clinical research domains, assuring data quality and improving processes. Publication: CDISC International Interchange Conference 18th & 19th April 2012, Stockholm

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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

2. © 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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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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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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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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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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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

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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

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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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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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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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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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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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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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Diabetes Mellitus: a use case
• We use LinkEHR® Studio, a model-independent editor of archetypes.
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HL7 CDA
Patient summary archetype

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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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LinkEHR
Diabetes Mellitus: a use case
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Legacy data model
Legacy
data
Archetype Standard model
Transform
script
Standard
data
Follows FollowsGenerates

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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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Diabetes Mellitus: a use case
• Example of a CDISC ODM archetype defining the
data needed for a Diabetes study.
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Diabetes Mellitus: a use case
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Diabetes Mellitus: a use case
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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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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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25. © CDISC 2012
David Moner
damoca@upv.es
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Thank you for your attention
Questions?