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Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
Clinical Trials powered by Electronic Health Records
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Clinical Trials powered by Electronic Health Records

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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 …

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

Published in: Health & Medicine, Business
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  • 1. © CDISC 2012 David Moner, Juan Bru, José A. Maldonado, Montserrat Robles Technical University of Valencia, Spain damoca@upv.es 1 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 2
  • 3. © 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 3
  • 4. © 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. 4
  • 5. © 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. 5
  • 6. © 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. 6
  • 7. © CDISC 2012 Standard information models • A standard information model will provide basic pieces and data structures for the persistence and exchange of data. 7
  • 8. © CDISC 2012 From data to knowledge • Archetypes are a definition of a clinical model built upon the pieces provided by a standard information model. 8 Data structure + Meaning Archetype
  • 9. © 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. 9 Archetype
  • 10. © 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. 10
  • 11. © 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, … 11
  • 12. © 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. 12
  • 13. © 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 13
  • 14. © 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? 14
  • 15. © 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. 15
  • 16. © CDISC 2012 Diabetes Mellitus: a use case • We use LinkEHR® Studio, a model-independent editor of archetypes. 16 HL7 CDA Patient summary archetype
  • 17. © 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. 17
  • 18. © CDISC 2012 LinkEHR Diabetes Mellitus: a use case 18 Legacy data model Legacy data Archetype Standard model Transform script Standard data Follows FollowsGenerates
  • 19. © 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. 19
  • 20. © CDISC 2012 Diabetes Mellitus: a use case • Example of a CDISC ODM archetype defining the data needed for a Diabetes study. 20
  • 21. © CDISC 2012 Diabetes Mellitus: a use case 21
  • 22. © CDISC 2012 Diabetes Mellitus: a use case 22
  • 23. © 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. 23
  • 24. © 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…). 24
  • 25. © CDISC 2012 David Moner damoca@upv.es 25 Thank you for your attention Questions?

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