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

Ontology-Based Clinical Information Extraction Using SNOMED CT

Extracting and encoding clinical information captured in unstructured clinical documents with standard medical terminologies is vital to enable secondary use of clinical data from practice. SNOMED CT is the most comprehensive medical ontology with broad types of concepts and detailed relationships and it has been widely used for many clinical applications. However, few studies have investigated the use of SNOMED CT in clinical information extraction. In this dissertation research, we developed a fine-grained information model based on the SNOMED CT and built novel information extraction systems to recognize clinical entities and identify their relations, as well as to encode them to SNOMED CT concepts. Our evaluation shows that such ontology-based information extraction systems using SNOMED CT could achieve state-of-the-art performance, indicating its potential in clinical natural language processing

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

A novel framework for standardizing and digitizing clinical pathways in healthcare information systems

Most healthcare institutions are reorganizing their healthcare delivery systems based on Clinical Pathways (CPs). CPs are medical management plans designed to standardize medical activities, reduce cost, optimize resource usage, and improve quality of service. However, most CPs are still paper-based and not fully integrated with Health Information Systems (HISs). More CP automation research is therefore required to fully benefit from the practical potentials of CPs. The common theme of current research in this field is to connect CPs with Electronic Medical Record (EMR) systems. Such view positions EMRs at the centre of HISs. A major long-term objective of this research is the placement of CP systems at the centre of HISs, because within CPs lies the very heart of medical planning, treatment and impressions, including healthcare quality and cost factors. An important contribution to the realization of this objective is to develop an international CP-specific digital coding system, and to fully standardize and digitize CPs based on the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) medical terminology system. This makes CPs digitally visible and machine-readable. In addition, to achieve semantic interoperability of CPs, we propose a CP knowledge representation using ontology engineering and HL7 standard. Our proposed framework makes CP systems smoothly linkable across various HISs. To show the feasibility and potential of the proposed framework, we developed a prototype Clinical Pathway Management System (CPMS) based on CPs currently in use at hospitals. The results show that CPs can be fully standardized and digitized using SNOMED CT terms and codes, and the CPMS can work as an independent healthcare system, performing novel CP-related functions including useful decision-support tasks. Furthermore, CP data were captured without loss, which contributes to reducing missing patient data and improving the results of data mining algorithms in healthcare. Standardized CPs can also be easily compared for auditing and quality management. The proposed framework is promising, and contributes toward solving major challenges related to CP standardization, digitization, independence, and proper inclusion in today’s modern computerized hospitals

Toward a Knowledge-Driven Context-Aware System for Surgical Assistance

Complex surgeries complications are increasing, thus making an efficient surgical assistance is a real need. In this work, an ontology-based context-aware system was developed for surgical training/assistance during Thoracentesis by using image processing and semantic technologies. We evaluated the Thoracentesis ontology and implemented a paradigmatic test scenario to check the efficacy of the system by recognizing contextual information, e.g. the presence of surgical instruments on the table. The framework was able to retrieve contextual information about current surgical activity along with information on the need or presence of a surgical instrument

J Biomed Inform

We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.CC999999/ImCDC/Intramural CDC HHS/United States2019-11-20T00:00:00Z28729030PMC6864736694