No abstract available.
Systematized Nomenclature of Medicine*

OBJECTIVE: CDA (Clinical Document Architecture) is a markup standard for clinical document exchange. In order to increase the semantic interoperability of documents exchange, the clinical statements in the narrative blocks should be encoded with code values. Natural language processing (NLP) is required in order to transform the narrative blocks into the coded elements in the level 3 CDA documents. In this paper, we evaluate the accuracy of text mapping methods which are based on NLP. METHODS: We analyzed about one thousand discharge summaries to know their characteristics and focused the syntactic patterns of the diagnostic sections in the discharge summaries. According to the patterns, different rules were applied for matching code values of Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). RESULTS: The accuracy of matching was evaluated using five-hundred discharge summaries. The precision was as follows: 86.5% for diagnosis, 61.8% for chief complaint, 62.7%, for problem list, and 64.8% for discharge medication. CONCLUSION: The text processing method based on the pattern analysis of a clinical statement can be effectively used for generating CDA entries.
Diagnosis ; Natural Language Processing ; Semantics ; Systematized Nomenclature of Medicine

OBJECTIVE: To obtain sharable and reusable knowledge among various hospital information systems, it is essential to represent each term with standard terminology. To support knowledge representation for interoperable clinical decision support system for hypertension management, the feasibility of SNOMED CT was evaluated. METHODS: Concept matching was conducted using the method of direct matching, post-coordinated matching and general matching. For semantic matching, the SNOMED CT hierarchy was considered, and for raising the mapping rate, preferred terms and synonyms were used. RESULTS: Excluding the recommendation concepts that were not used in clinical data, finally 182 concepts were evaluated in terms of concept matching. Seventy two percent of the concepts was directly matched to pre-coordinated concepts in SNOMED CT. For the post-coordinated matching and the general matching to broader meaning, 9.3% and 18.7% were covered respectively. CONCLUSION: The direct coverage of SNOMED CT was moderate to high level for representing guideline knowledge concepts without loss of semantics. To supplement the coverage, it is inevitable to consider defining local concepts for implementing hypertension management systems.
Hospital Information Systems ; Hypertension ; Logic ; Semantics ; Systematized Nomenclature of Medicine

PURPOSE: This study was performed to propose an ontology methodology based on standardized nursing process as framework in obstetric and gynecologic nursing practice. METHODS: The instrument used in this study was based on the nursing diagnosis classification established by North American Nursing Diagnosis Association (NANDA) (2009-2011), fifth edition of the Nursing Interventions Classification (NIC) (2008), forth edition of the Nursing Outcomes Classification (NOC) (2008) developed by Iowa State University and systematized nomenclature of medicine clinical terms (SNOMED CT). The nursing records data were collected from electronic medical records of one hospital from August to October 2010. RESULTS: One hundred and forty-one nursing diagnosis statements used in obstetric and gynecologic nursing unit were linked standardized nursing classifications and constructed nursing diagnosis ontology including interoperability. CONCLUSION: Not only will this result be helpful to complete nurse's lack of knowledge and experience, it will also help to determine nursing diagnosis logically by using standardized nursing process. It will be utilized as the method to construct ontology including interoperability in other nursing units. It will be presented nursing interventions according to nursing diagnosis and thus will be easier to establish nursing planning. This can provide immediate feedback of the nursing process application.
Electronic Health Records ; Iowa ; Logic ; Nursing Diagnosis ; Nursing Process ; Nursing Records ; Systematized Nomenclature of Medicine

OBJECTIVES: The aim of the study was to develop a metadata and ontology-based health information search engine ensuring semantic interoperability to collect and provide health information using different application programs. METHODS: Health information metadata ontology was developed using a distributed semantic Web content publishing model based on vocabularies used to index the contents generated by the information producers as well as those used to search the contents by the users. Vocabulary for health information ontology was mapped to the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and a list of about 1,500 terms was proposed. The metadata schema used in this study was developed by adding an element describing the target audience to the Dublin Core Metadata Element Set. RESULTS: A metadata schema and an ontology ensuring interoperability of health information available on the internet were developed. The metadata and ontology-based health information search engine developed in this study produced a better search result compared to existing search engines. CONCLUSIONS: Health information search engine based on metadata and ontology will provide reliable health information to both information producer and information consumers.
Consumer Health Information ; Information Systems ; Internet ; Search Engine* ; Semantics ; Systematized Nomenclature of Medicine ; Vocabulary

OBJECTIVES: In this study, we proposed an algorithm for mapping standard terminologies for the automated generation of medical bills. As the Korean and American structures of health insurance claim codes for laboratory tests are similar, we used Current Procedural Terminology (CPT) instead of the Korean health insurance code set due to the advantages of mapping in the English language. METHODS: 1,149 CPT codes for laboratory tests were chosen for study. Each CPT code was divided into two parts, a Logical Observation Identifi ers Names and Codes (LOINC) matched part (matching part) and an unmatched part (unmatched part). The matching parts were assigned to LOINC axes. An ontology set was designed to express the unmatched parts, and a mapping strategy with Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) was also proposed. Through the proceeding analysis, an algorithm for mapping CPT with SNOMED CT arranged by LOINC was developed. RESULTS: 75% of the 1,149 CPT codes could be assigned to LOINC codes. Two hundred and twenty-five CPT codes had only one component part of LOINC, whereas others had more than two parts of LOINC. The system of LOINC axes was found in 309 CPT codes, scale 555, property 9, method 42, and time aspect 4. From the unmatched parts, three classes, 'types', 'objects', and 'subjects', were determined. By determining the relationship between the classes with several properties, all unmatched parts could be described. Since the 'subject to' class was strongly connected to the six axes of LOINC, links between the matching parts and unmatched parts were made. CONCLUSIONS: The proposed method may be useful for translating CPT into concept-oriented terminology, facilitating the automated generation of medical bills, and could be adapted for the Korean health insurance claim code set.
Current Procedural Terminology ; Insurance, Health ; Logic ; Logical Observation Identifiers Names and Codes ; Systematized Nomenclature of Medicine ; Translating

OBJECTIVES: Coding Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT) with complex and polysemy clinical terms may ask coder to have a high level of knowledge of clinical domains, but with simpler clinical terms, coding may require only simpler knowledge. However, there are few studies quantitatively showing the relation between domain knowledge and coding ability. So, we tried to show the relationship between those two areas. METHODS: We extracted diagnosis and operation names from electronic medical records of a university hospital for 500 ophthalmology and 500 neurosurgery patients. The coding process involved one ophthalmologist, one neurosurgeon, and one medical record technician who had no experience of SNOMED coding, without limitation to accessing of data for coding. The coding results and domain knowledge were compared. RESULTS: 705 and 576 diagnoses, and 500 and 629 operation names from ophthalmology and neurosurgery, were enrolled, respectively. The physicians showed higher performance in coding than in MRT for all domains; all specialist physicians showed the highest performance in domains of their own departments. All three coders showed statistically better coding rates in diagnosis than in operation names (p < 0.001). CONCLUSIONS: Performance of SNOMED coding with clinical terms is strongly related to the knowledge level of the domain and the complexity of the clinical terms. Physicians who generate clinical data can be the best potential candidates as excellent coders from the aspect of coding performance.
Clinical Coding ; Electronic Health Records ; Humans ; Medical Record Administrators ; Neurosurgery ; Ophthalmology ; Specialization ; Systematized Nomenclature of Medicine

PURPOSE: The aim of this study was to explore how nursing diagnoses are made by undergraduate students of psychiatric unit in Korea. METHODS: Data were collected from case reports and analyzed based on NANDA (North American Nursing Diagnosis Association) nursing diagnoses and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) as reference terminology. RESULTS: The 30 different nursing diagnoses from 135 distinct nursing diagnosis statements were assessed after removing repetition of case studies from a of total of 1,140 statements of nursing diagnoses. The most frequently used NANDA diagnosis was "ineffective coping" The thirty nursing diagnoses were grouped under 10 out of the 13 NANDA domains. In addition, 98 related factors were classified into SNOMED CT hierarchies of Clinical Finding, Procedure, and Observable Entity. The content validity index for the mapping of nursing diagnoses was 0.97, indicating a relatively strong agreement. CONCLUSION: These results can help students to improve their knowledge and better formulate appropriate diagnoses. Using standardized terminology would improve competency of education and help to ratify the steps of the nursing process, especially nursing planning. Educational strategies that enhance diagnostic accuracy are recommended.
Diagnosis ; Education ; Humans ; Korea ; Nursing ; Nursing Diagnosis* ; Nursing Process ; Psychiatric Nursing ; Systematized Nomenclature of Medicine*

The electronic medical record is gradually penetrating the world-wide healthcare environment, including Korea. Users of electronic medical record want to get full advantages of it, but benefits would not be realized by simple implementation. Wellorganized architecture and controlled medical vocabulary are needed for achieving effective electronic medical record system. Many terminologies are introduced in healthcare system but single noble terminology to cope with users' need is not present. To extract clinically useful and standardized set of chief complaints for electronic medical record in our institutional environment, we analyzed chief complaints in 235,426 discharge summaries in Seoul National University Hospital. We normalized the chief complaint by 27 medical experts. First of all, researchers parsed chief complaints as main concept, qualifier, and modifier. We normalized main concepts, keeping applicability and usability of extracted set to electronic medical record in mind. And then, mapping set of chief complaint to SNOMED CT was done by 4 physicians. Among 94,913 unique strings, we selected 6,317 terms as standard set of chief complaint. The potential users of electronic medical record were asked to evaluate usability of the set and accepted it as representative of chief complaint. The majority of terms in set were completely mapped to SNOMED CT. We discussed several principles in normalizing chief complaints. We also pointed pros and cons of SNOMED CT as reference terminology to chief complaint domain. Through this study, we developed conceptually standardized chief complaint domain with user-friendly terms in Seoul National University Hospital Environment. Furthermore, this result would be the starting point to evolve medical terms in Korea into ontology based terminology system.
Delivery of Health Care ; Electronic Health Records* ; Korea ; Seoul ; Systematized Nomenclature of Medicine* ; Vocabulary

BACKGROUND: It is not possible to measure how much activity is required to understand and code a medical data. We introduce an assessment method in clinical coding, and applied this method to neurosurgical terms.METHODS: Coding activity consists of two stages. At first, the coders need to understand a presented medical term (informational activity). The second coding stage is about a navigating terminology browser to find a code that matches the concept (code-matching activity). Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) was used for the coding system. A new computer application to record the trajectory of the computer mouse and record the usage time was programmed. Using this application, we measured the time that was spent. A senior neurosurgeon who has studied SNOMED CT has analyzed the accuracy of the input coding. This method was tested by five neurosurgical residents (NSRs) and five medical record administrators (MRAs), and 20 neurosurgical terms were used.RESULTS: The mean accuracy of the NSR group was 89.33%, and the mean accuracy of the MRA group was 80% (p=0.024). The mean duration for total coding of the NSR group was 158.47 seconds, and the mean duration for total coding of the MRA group was 271.75 seconds (p=0.003).CONCLUSION: We proposed a method to analyze the clinical coding process. Through this method, it was possible to accurately calculate the time required for the coding. In neurosurgical terms, NSRs had shorter time to complete the coding and higher accuracy than MRAs.
Animals ; Clinical Coding ; Humans ; Medical Informatics ; Medical Record Administrators ; Methods ; Mice ; Neurosurgeons ; Systematized Nomenclature of Medicine