Abstract
Data entry is an obstacle for the usability of electronic health records (EHR) applications and the acceptance of physicians, who prefer to document using “free text”. Natural language is huge and very rich in details but at the same time is ambiguous; it has great dependence on context and uses jargon and acronyms. Healthcare Information Systems should capture clinical data in a structured and preferably coded format. This is crucial for data exchange between health information systems, epidemiological analysis, quality and research, clinical decision support systems, administrative functions, etc. In order to address this point, numerous terminological systems for the systematic recording of clinical data have been developed. These systems interrelate concepts of a particular domain and provide reference to related terms and possible definitions and codes. The purpose of terminology services consists of representing facts that happen in the real world through database management. This process is named Semantic Interoperability. It implies that different systems understand the information they are processing through the use of codes of clinical terminologies. Standard terminologies allow controlling medical vocabulary. But how do we do this? What do we need? Terminology services are a fundamental piece for health data management in health environment.
Highlights
Major healthcare stakeholders around the world have emphasized on the importance of establishing electronic health records (EHR) for all health care institutions
Free text has the advantage of allowing health care providers to express themselves freely, but as disadvantage it has the need for an arduous codification process to allow further analysis
Some systems may support the assignment of multiple terms, or synonyms, to a given concept...” [26] Systematized Nomenclature of Medicine (SNOMED) CT was developed to serve as a standard data infrastructure for clinical application, which requires a greater degree of specificity
Summary
Major healthcare stakeholders around the world have emphasized on the importance of establishing electronic health records (EHR) for all health care institutions. Their goals for doing so include increasing patient safety, reducing medical errors, improving efficiency and reducing costs [1, 2]. Presentation and document retrieval for clinical tasks must be taken into account, so that the differences between the needs of users and the needs of available software’s are addressed. Data entry is an obstacle for the adoption of EHR with structured data method and the acceptance of healthcare providers, who prefer to document healthcare findings, processes and outcomes using unfettered “free text” or narrative text in natural language [3]. Natural language is huge and very rich in details but at the same time ambiguous, having great dependence on context, it uses jargon and acronyms and it lacks of rigorous definitions
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