clinical trials powered by electronic health records
DESCRIPTION
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.TRANSCRIPT
CDISC EU Interchange 2012
Clinical Trials powered by Electronic Health Records
David Moner, Juan Bru, José Alberto Maldonado, Montserrat Robles
Instituto ITACA, Universitat Politècnica de València, Spain (Contact: [email protected])
IntroductionThe 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.
Material / MethodsThe CEN/ISO 13606 standard for the communication of electronic health records [1] proposes an innovative approach for the representation of clinical information. It is based in the use of a Reference Model for representing data instances and an Archetype Model for representing clinical concepts. Archetypes are formal definitions of clinical models which provide a powerful, reusable and interoperable mechanism for managing the creation, description, validation and query of EHRs. Examples of archetypes include prescriptions, health problems, differential diagnosis, pregnancy reports or blood pressure observations. Archetypes are also the link between information structures and terminologies or ontologies that semantically describe that information.
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CDISC EU Interchange 2012
In the field of clinical trials, CDISC Operational Data Model (ODM) is a generic reference model for the representation of any information included in a clinical research study. ODM, together with CDISC CDASH, provides the basic components and structures of information needed and used in clinical trials.
From data to knowledge
Information structures contained in EHR systems are mainly focused to health care and could be represented by archetypes. But it is not common to find archetype-based systems, but to have only data which is not standardized.
A first use of archetypes is the normalization of legacy data. LinkEHR [2] is a tool that helps in this duty by providing two basic functionalities. On the one hand, we can define archetypes based on any reference model, for example CEN/ISO 13606, HL7 CDA or CDISC ODM. On the other hand, LinkEHR allows defining mappings between archetypes and existing data sources and it generates transformation programs to convert legacy data into standardized data, conformant to the selected standard and archetype.
From knowledge to clinical research
Clinical research requires very specific information structures reused from the EHR. Archetypes can be also used for this purpose. LinkEHR can help in linking existing archetypes to more abstract archetypes, enriching the existing information at the same time by combining clinical data with new data from terminology systems and other knowledge resources such as CDSS.
ResultsThe proposed solution has been used to transform diabetes information from EHR standard information (HL7 CDA and CEN/ISO 13606) to clinical research standards (CDISC ODM).
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. Thus, Diabetes Mellitus will require more innovative pharmaceutical products in the years coming. 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, it will become appropriate to find an easy and fast way to report new information and issues from EHR systems to the CT systems.
For example, our EHR system provides information about prescriptions of one patient in CEN/ISO 13606 format. These prescriptions include the patient demographics, prescription dates, medication brand name, dose, pharmaceutical form, and a national medicine code. It can also generate discharge reports of the patient in HL7 CDA format. These reports include the
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CDISC EU Interchange 2012
patient demographics, dates, history, procedures, diagnosis and recommended treatments. Finally, the laboratory information system generates HL7 v2 messages with the results of several laboratory tests, including blood glucose level, glycated hemoglobin level (HbA1c), and others.
In order to reuse these data for clinical research it is necessary to extract the useful information for the study and enrich it with additional data. To do this, we proceed in the following three steps, also summarized in Figure 1.
Figure 1. Archetype-based process for reusing EHR data in clinical research.
1. Describe clinical information with a formal, computable and reusable format. By defining archetypes for each information structure of the EHR we provide a formal and semantic description of the concepts used at that level of clinical care. In our example, this is represented by the prescription, discharge and laboratory archetypes.
2. Abstract and enrich the data to make it useful for a clinical study. We can create more abstract archetypes, suitable for clinical research uses, and in parallel, a data enrichment process can be executed. In our example, this means the creation of a new medication archetype. Data from the prescription can be reused and enriched by adding new
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CDISC EU Interchange 2012
information, such as the active ingredient, the ATC code or the side effects of the medication.
3. Combine and transform data into the format for the CT system. A final transformation can put together all needed information into a CDISC ODM data instance representing a diabetes study in order to feed the CT database.
Discussion / ConclusionThe use of archetypes provides a uniform approach for linking EHR systems and CT systems. The advantages of this model are: (1) It is independent of existing standards, software and architecture of EHR systems. (2) Allows making EHR and CT systems fully interoperable. (3) Allows fast solution development adaptable to fit different scenarios. (4) Assures the quality of data for clinical research.
A model like this can be keystone in the way to reach a full collaboration between health and clinical research domains.
References[1] European Committee for Standardization. Health informatics - Electronic health record communication. EN13606, 2008
[2] Maldonado JA, et al. LinkEHR-Ed: A multi-reference model archetype editor based on formal semantics, Int. J. Med. Inform. (2009), doi:10.1016/j.ijmedinf.2009.03.006
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