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The development of Electronic Health Record (EHR) systems containing valuable clinical information is an opportunity not only for health care but also for clinical research. Clinical Trial (CT) management systems would improve their processes by accessing this EHR data in a straightforward way.Nevertheless, there are still many problems to be solved in order to facilitate the reuse of information, including the lack of common formats for the representation of data, or a limited definition of the meaning of that data. The use of standards and clinical terminologies, together with a clear definition of clinical information models becomes essential in order to enable the semantic interoperability of EHR and clinical trials by means of a standardized definition of the data to be exchanged.
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© CDISC 2012
David Moner, Juan Bru, José A. Maldonado, Montserrat Robles
Technical University of Valencia, Spain
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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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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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© CDISC 2012
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
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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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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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© CDISC 2012
Diabetes Mellitus: a use case• We use LinkEHR® Studio, a model-independent editor of archetypes.
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HL7 CDAPatient 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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LinkEHR
Diabetes Mellitus: a use case
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Legacy data model
Legacydata
Archetype Standard model
Transformscript
Standarddata
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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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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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
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Thank you for your attention
Questions?
