Clinical Terminology in Patient Health Record System - SNOMED CT Overview

Background of study: Patient Health Record System (PHRS) is used by physicians for capturing patient medical records in electronic media. Standardization in PHRS arises a major challenge due to its complexities. The used of clinical terminology is needed in order to facilitate more expressive clinical data input, provide unambiguous encoding and support the exchange of clinical information. One of highly specialized clinical terminology is SNOMED CT(Systematized Nomenclature of Medicine Clinical Terms) that able to encode clinical data, and contains concepts that linked to clinical knowledge to enable accurate recording of data without ambiguity. The aims of this paper is to discuss the use of clinical terminology in PHRS and identifying importance factors for applying clinical terminology in healthcare services. Method: This study used review of literature in order to find the use of clinical terminology in patient health record system by reviewing current used of clinical terminology. Result: The result of the study found that clinical terminology supports information exchange between healthcare provider

Clinical Terminology in Patient Health Record System - SNOMED CT Overview

Background of study: Patient Health Record System (PHRS) is used byphysicians for capturing patient medical records in electronic media.Standardization in PHRS arises a major challenge due to its complexities. Theused of clinical terminology is needed in order to facilitate more expressiveclinical data input, provide unambiguous encoding and support the exchange ofclinical information. One of highly specialized clinical terminology is SNOMEDCT(Systematized Nomenclature of Medicine Clinical Terms) that able to encodeclinical data, and contains concepts that linked to clinical knowledge to enableaccurate recording of data without ambiguity. The aims of this paper is to discussthe use of clinical terminology in PHRS and identifying importance factors forapplying clinical terminology in healthcare services.Method: This study used review of literature in order to find the use of clinicalterminology in patient health record system by reviewing current used of clinicalterminology.Result: The result of the study found that clinical terminology supportsinformation exchange between healthcare providers

Measuring diversity in medical reports based on categorized attributes and international classification systems

<p>Abstract</p> <p>Background</p> <p>Narrative medical reports do not use standardized terminology and often bring insufficient information for statistical processing and medical decision making. Objectives of the paper are to propose a method for measuring diversity in medical reports written in any language, to compare diversities in narrative and structured medical reports and to map attributes and terms to selected classification systems.</p> <p>Methods</p> <p>A new method based on a general concept of f-diversity is proposed for measuring diversity of medical reports in any language. The method is based on categorized attributes recorded in narrative or structured medical reports and on international classification systems. Values of categories are expressed by terms. Using SNOMED CT and ICD 10 we are mapping attributes and terms to predefined codes. We use f-diversities of Gini-Simpson and Number of Categories types to compare diversities of narrative and structured medical reports. The comparison is based on attributes selected from the Minimal Data Model for Cardiology (MDMC).</p> <p>Results</p> <p>We compared diversities of 110 Czech narrative medical reports and 1119 Czech structured medical reports. Selected categorized attributes of MDMC had mostly different numbers of categories and used different terms in narrative and structured reports. We found more than 60% of MDMC attributes in SNOMED CT. We showed that attributes in narrative medical reports had greater diversity than the same attributes in structured medical reports. Further, we replaced each value of category (term) used for attributes in narrative medical reports by the closest term and the category used in MDMC for structured medical reports. We found that relative Gini-Simpson diversities in structured medical reports were significantly smaller than those in narrative medical reports except the "Allergy" attribute.</p> <p>Conclusions</p> <p>Terminology in narrative medical reports is not standardized. Therefore it is nearly impossible to map values of attributes (terms) to codes of known classification systems. A high diversity in narrative medical reports terminology leads to more difficult computer processing than in structured medical reports and some information may be lost during this process. Setting a standardized terminology would help healthcare providers to have complete and easily accessible information about patients that would result in better healthcare.</p

Standards in medical informatics : fundamentals and applications

La aplicación de la informática a la práctica médica ha permitido el desarrollo de novedosas formas de comunicación en la atención de la salud. La optimización de los procesos comunicativos se alcanza con el uso de estándares que armonicen el intercambio de información y provean un lenguaje común para todos los agentes involucrados. En el presente artículo, se describe el concepto de estándar aplicado a la informática médica y su importancia en el desarrollo de diversas aplicaciones tales como la representación computacional del conocimiento médico, la codificación diagnóstica, la búsqueda de literatura médica y la integración de las ciencias biológicas a las aplicaciones clínicas.Q4295-302The use of computers in medical practice has enabled novel forms of communication to be developed in health care. The optimization of communication processes is achieved through the use of standards to harmonize the exchange of information and provide a common language for all those involved. This article describes the concept of a standard applied to medical informatics and its importance in the development of various applications, such as computational representation of medical knowledge, disease classification and coding systems, medical literature searches and integration of biological and clinical sciences

Representing SNOMED CT Concept Evolutions using Process Profiles

Abstract. SNOMED CT is a very large biomedical terminology supported by a concept-based ontology. In recent years it has been distributed under the new release format &apos;RF2&apos;. RF2 provides a more consistent and coherent mechanism for keeping track of changes over versions, even to the extent that -in theory at leastany release will contain enough information to allow reconstruction of all previous versions. In this paper, using the January 2016 release of SNOMED CT, we explore various ways to transform change-assertions in RF2 into a more uniform representation with the goal of assessing how faithful these changes are with respect to biomedical reality. Key elements in our approach are (1) recent proposals for the Information Artifact Ontology that provide a realism-based perspective on what it means for a representation to be about something, and (2) the expectation that the theory of what we call &apos;process profiles&apos; can be applied not merely to quantitative information artifacts but also to other sorts of symbolic representations of processes

Automatic medical term generation for a low-resource language: translation of SNOMED CT into Basque

211 p. (eusk.) 148 p. (eng.)Tesi-lan honetan, terminoak automatikoki euskaratzeko sistemak garatu eta ebaluatu ditugu. Horretarako,SNOMED CT, terminologia kliniko zabala barnebiltzen duen ontologia hartu dugu abiapuntutzat, etaEuSnomed deritzon sistema garatu dugu horren euskaratzea kudeatzeko. EuSnomedek lau urratsekoalgoritmoa inplementatzen du terminoen euskarazko ordainak lortzeko: Lehenengo urratsak baliabidelexikalak erabiltzen ditu SNOMED CTren terminoei euskarazko ordainak zuzenean esleitzeko. Besteakbeste, Euskalterm banku terminologikoa, Zientzia eta Teknologiaren Hiztegi Entziklopedikoa, eta GizaAnatomiako Atlasa erabili ditugu. Bigarren urratserako, ingelesezko termino neoklasikoak euskaratzekoNeoTerm sistema garatu dugu. Sistema horrek, afixu neoklasikoen baliokidetzak eta transliterazio erregelakerabiltzen ditu euskarazko ordainak sortzeko. Hirugarrenerako, ingelesezko termino konplexuak euskaratzendituen KabiTerm sistema garatu dugu. KabiTermek termino konplexuetan agertzen diren habiaratutakoterminoen egiturak erabiltzen ditu euskarazko egiturak sortzeko, eta horrela termino konplexuakosatzeko. Azken urratsean, erregeletan oinarritzen den Matxin itzultzaile automatikoa osasun-zientziendomeinura egokitu dugu, MatxinMed sortuz. Horretarako Matxin domeinura egokitzeko prestatu dugu,eta besteak beste, hiztegia zabaldu diogu osasun-zientzietako testuak itzuli ahal izateko. Garatutako lauurratsak ebaluatuak izan dira metodo ezberdinak erabiliz. Alde batetik, aditu talde txiki batekin egin dugulehenengo bi urratsen ebaluazioa, eta bestetik, osasun-zientzietako euskal komunitateari esker egin dugunMedbaluatoia kanpainaren baitan azkeneko bi urratsetako sistemen ebaluazioa egin da

Análisis del uso de la inteligencia colaborativa como herramienta para la construcción de bases de conocimiento consensuadas en procesos de diagnóstico médico

En la presente tesis se presenta un estudio que gira entorno principalmente a la Inteligencia Colaborativa como principal característica de las aplicaciones de tipo Social Media y Web 2.0. Estos conceptos han sido combinados con áreas de estudio como la medicina, las tecnologías de Web semántica y los sistemas de soporte a la decisión médica (CDSS), con el objetivo de conocer la forma en cómo la Inteligencia Colaborativa logra afectar de manera positiva a la obtención de diagnósticos. Las redes sociales en esta investigación, han sido identificadas como estructuras sociales conformadas por un grupo de personas cuyo objetivo principal es la participación en actividades comunes, la mayoría de las veces en busca de la solución a problemas. Este fenómeno de participación, compartición de información y colaboración ha sido tomado como base para la creación de redes sociales y demás plataformas colaborativas en Internet, en donde lo que destaca nuevamente es la arquitectura de participación de la que hacen uso. Un caso especial y que ha sido objeto de estudio de esta investigación son aquellas plataformas colaborativas con contenido médico. La Web semántica ha jugado un papel fundamental en este estudio ya que permite la comunicación entre diferentes sistemas para compartir información, es decir, la interoperabilidad entre sistemas. También facilita la representación del conocimiento en diferentes áreas y finalmente también permite realizar procesos de inferencia cuando se aplica a sistemas expertos. Con base en los conceptos anteriores y respaldada en el concepto de Wisdom of the crowd (la sabiduría de las multitudes), esta investigación plantea la definición de tres métodos de consenso que han sido aplicados a bases de conocimiento con contenido médico. Para la evaluación de los resultados se han utilizado las métricas comunes a los CDSS siguiendo los criterios propuestos por Kaplan en las diferentes bases de conocimiento consensuadas, las cuales se han comparado con los valores en las mismas métricas generadas por un CDSS tradicional que ha sido tomado como estándar de oro. Finalmente, esta tesis presenta las mejoras que la Inteligencia Colaborativa aporta a la medicina en términos de exactitud de los diagnósticos y las ventajas que esta representa cuando se aplica a estos sistemas. -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------In this thesis a study that primarily revolves around the main characteristic Collaborative Intelligence of Social Media type applications and Web 2.0 is presented. These concepts have been combined with areas of study such as medicine, Semantic Web technologies and Clinical Decision Support Systems (CDSS) with the aim to know the way how the Collaborative Intelligence does positively affect the development of diagnostics. In this research, social networks have been identified as social structures formed by a group of people whose main objective is the participation in common activities, most of the time looking for the solution of problems. This participation, collaboration an information sharing phenomenon has been taken as the basis for social networking and other online collaborative platforms, where it is again highlighting the participation architecture that those systems use. A special case has been studied in this research are those with medical content collaborative platforms. The Semantic Web has played a key role in this study because it allows communication between different systems to share information, ie interoperability between systems. It also facilitates knowledge representation in different areas and finally also allows inference processes when it is applied to expert systems. Based on the above concepts and supported by the concept of Wisdom of the crowd, this research presents the definition of three consensus methods that has been applied to knowledge bases with medical content. For evaluating the results common metrics to CDSS were used following the criteria proposed by Kaplan in the different consensus knowledge bases. The results have been compared with the same metrics values generated by a traditional CDDS which is taken as the gold standard. Finally, this thesis presents the improvements that the Collaborative Intelligence brings to medicine in terms of diagnostic accuracy and the advantages that this represents when applied to this kind of systems