The health professionals need access to all the clinical information of their patients to develop their work, it which is called clinic history, if this information is supported in a computer system, then we can talk of Electronic Health Record (EHR). An efficient and high quality health system requires an HCE as an integrator of clinical data and source of information for health professionals, we can say today that is an essential element in a medical environment. The Access to all patient data centrally and updated with the last information, it is a strategic objective in the field of health from the point of view of care, management and research
A EHR must have a structure that they can be processed by a computer system. The structure should therefore be suitable for an environment of health care and other uses such as: research, training, statistics, etc... That is why, the aspect most important to consider when developing an EHR systems is the organization of clinical information.
An electronic health record architecture must be capable to model any clinical history existing and future, today some standards intended to fulfill this task are presented. The standardization issues have become more important and are becoming more complex with the globalization of the economy and liberalization of markets. The products must be designed to be acceptable to users from multiple countries with different languages, different value systems and different working conditions.
OpenEHR is an open specification intended for health records management, describes the storage and management of clinical information by electronic health records EHR.
OpenEHR define a reference model for the representation of information clinic as well as a archetypes model responsible for representing clinical concepts of higher semantic level. The reference model are compose by all necessary classes to represent any kind of clinical information, including background information, such as the attending physician, date of completion of the tests, etc... It is a simple and flexible model adaptable to any structure of health information.
In the Archetypes model are defined clinical concepts formally and a higher semantic level, for example: A discharge summary, a laboratory test, administration of substances and this is based on the reference model classes restricting them to certain values ??or structures of precise data. All nodes represented the archetype can be linked to clinical terminologies, thats give to the definition of the archetype a precise meaning to ensure semantic interoperability. OpenEHR offers to the community a tool for the management of clinical knowledge, with this tool you can share knowledge (archetypes) and control the lifecycle, also can open new projects where several people to collaborate in the creation and management of clinical knowledge . The knowledge represented by the archetypes can be translated to any language, for it has a multilanguage own ontology which in turn can be aligned with any outside medical ontology.
The origins of openEHR begin in January 1992 when it starts in London a project called GEHR (Good European Health Record) who led a working group composed of people from universities, business and health with the aim of creating an EHR standard and financed EU, several countries England, Begic, Portugal and France participate. In 1994 the funding is over and all remaining incomplete work not be successful. Reading the autobiography of Thomas Beale an Australian who was working on the project and could be considered the father of openEHR, when in 1994 the project Thomas Beale returns to Australia is over, but he is still continued thinking in how to solve the problems in GEHR and in 1997 he created together with his colleague Sam Heard an information model and a set of constraints that gave rise to what is known as archetype openEHR, in 1998 he created the openEHR foundation and a company called "Oceans Informatics" and beyond the devoted to defining with other partners openEHR specifications.
OpenEHR having some years of research behind, but it can still be considered a young technology, there are few tools that address the information produced by the standard. In the other side we have OWL that has available many tools, plus there are specialists treat such information and the production of new knowledge through data mining tools and OWL reasoners, therefore, the conversion of OWL archetypes produce new knowledge from the information recorded by the openEHR.
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We have already reviewed some of the advantages of using a standard clinical system, if that standard is openEHR have additional advantages and if we aligned with OWL then the result is that we can use tools that are compatible with technology. One of these tools are the OWL reasoner, ontologies can defined an archetype to model this ontology, create relationships between classes, these relationships we add properties and finally on this ontology which can be adapted to the changing needs of the study in question, we create instances of the data. At this point we have all ingredients to put to work an OWL reasoner with openEHR data and infer knowledge.
A strength of RDF is the management that makes the information distributed RDF have of naturally way elements that allow process data that are scattered throughout the vast world of the web, you are able to access data distributed in many servers and bring them together in a single query. The hospital databases are distributed throughout a country. All this clinical information is distributed, even with a standard that makes interoperable the institutions and assuming that standard was openEHR, CEN13606, HL7v3 not have the ability to launch consultations to collect the distributed information and treats in a unified manner, for this reason it is important to harmonize and align work in the world of archetypes to OWL world.
A feature of openEHR is the separation of the demographics of the clinical data, it is for this feature, which makes it feasible to open a public channel for investigation of both the university and the company, also is for this that we can extract information for studies of disease evolution thereof, effectiveness of treatments, etc ... the collaboration between the health system and potential consumers of information would make it easier, also would be more transparent, it could perform comparative analysis in terms of efficiency and quality between different centers and this would not be possible if the privacy of patients in a hospital undertakes sometime. Therefore OWL and search mechanisms remain valid to extract information from databases openEHR without compromising the privacy of individuals.
An archetype define formally clinical concepts such as a discharge summary, laboratory testing, administration of substances, this is based on the reference model classes, restricting them to certain values or precise data structures . All nodes represented the archetype can be linked to clinical terminologies for give to the definition of the archetype of a precise meaning to ensure semantic interoperability.
Archetype Definition Language (ADL) is a formal language designed to express archetypes, is based on a model of constraints on entities / elements in a domain. Archetype Object Model is an object model that defines the semantics of an archetype.
An important aspect of openEHR is to manage the clinical knowledge through archetypes and that is why the foundation has developed a tool to manage it, is called CKM (Clinical Knowledge Manager). This online tool has several functions: The most important of all, is to have a centralized repository of archetypes where a country or community of countries have a standard way of register the information. It also controls the lifecycle of an archetype, from the draft stage to commissioning production.
The team is open to all people that want to involved in the project, by the moment is integrated by Fernando Sanchez Duran. In software development has involved members of the openEHR seminar organized by the research group khaos of Malaga University and led by Dr. Josť Francisco Aldana and Dr. Ismael Navas.
The idea of working in this field is of Dr. Josť Francisco Aldana. Fernando Sanchez as a student of master in software engineering and artificial intelligence raises the possibility of Master Thesis work a system to interconnect clinical databases, then Dr. Josť Aldana redirects all work to openEHR standard and subsequently raises a number of tools for relate the world of openEHR with OWL ontologies.
Fernando Sanchez Duran