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: This study developed an integrated database for 15 regional biobanks that provides large quantities of high-quality bio-data to researchers to be used for the prevention of disease, for the development of personalized medicines, and in genetics studies. METHODS: We collected raw data, managed independently by 15 regional biobanks, for database modeling and analyzed and defined the metadata of the items. We also built a three-step (high, middle, and low) classification system for classifying the item concepts based on the metadata. To generate clear meanings of the items, clinical items were defined using the Systematized Nomenclature of Medicine Clinical Terms, and specimen items were defined using the Logical Observation Identifiers Names and Codes. To optimize database performance, we set up a multi-column index based on the classification system and the international standard code. RESULTS: As a result of subdividing 7,197,252 raw data items collected, we refined the metadata into 1,796 clinical items and 1,792 specimen items. The classification system consists of 15 high, 163 middle, and 3,588 low class items. International standard codes were linked to 69.9% of the clinical items and 71.7% of the specimen items. The database consists of 18 tables based on a table from MySQL Server 5.6. As a result of the performance evaluation, the multi-column index shortened query time by as much as nine times. CONCLUSIONS: The database developed was based on an international standard terminology system, providing an infrastructure that can integrate the 7,197,252 raw data items managed by the 15 regional biobanks. In particular, it resolved the inevitable interoperability issues in the exchange of information among the biobanks, and provided a solution to the synonym problem, which arises when the same concept is expressed in a variety of ways.
Biological Specimen Banks ; Classification ; Data Collection ; Genetics ; Korea* ; Logical Observation Identifiers Names and Codes ; Precision Medicine ; Systematized Nomenclature of Medicine

OBJECTIVES: This study developed an integrated database for 15 regional biobanks that provides large quantities of high-quality bio-data to researchers to be used for the prevention of disease, for the development of personalized medicines, and in genetics studies. METHODS: We collected raw data, managed independently by 15 regional biobanks, for database modeling and analyzed and defined the metadata of the items. We also built a three-step (high, middle, and low) classification system for classifying the item concepts based on the metadata. To generate clear meanings of the items, clinical items were defined using the Systematized Nomenclature of Medicine Clinical Terms, and specimen items were defined using the Logical Observation Identifiers Names and Codes. To optimize database performance, we set up a multi-column index based on the classification system and the international standard code. RESULTS: As a result of subdividing 7,197,252 raw data items collected, we refined the metadata into 1,796 clinical items and 1,792 specimen items. The classification system consists of 15 high, 163 middle, and 3,588 low class items. International standard codes were linked to 69.9% of the clinical items and 71.7% of the specimen items. The database consists of 18 tables based on a table from MySQL Server 5.6. As a result of the performance evaluation, the multi-column index shortened query time by as much as nine times. CONCLUSIONS: The database developed was based on an international standard terminology system, providing an infrastructure that can integrate the 7,197,252 raw data items managed by the 15 regional biobanks. In particular, it resolved the inevitable interoperability issues in the exchange of information among the biobanks, and provided a solution to the synonym problem, which arises when the same concept is expressed in a variety of ways.
Biological Specimen Banks ; Classification ; Data Collection ; Genetics ; Korea* ; Logical Observation Identifiers Names and Codes ; Precision Medicine ; Systematized Nomenclature of Medicine

BACKGROUND: Standardization of medical terminology is essential for data transmission between health-care institutions or clinical laboratories and for maximizing the benefits of information technology. Purpose of our study was to standardize the medical terms used in the clinical laboratory, such as test names, units, terms used in result descriptions, etc. During the first year of the study, we developed a standard database of concept names for laboratory terms, which covered the terms used in government health care centers, their branch offices, and primary health care units. METHODS: Laboratory terms were collected from the electronic data interchange (EDI) codes from National Health Insurance Corporation (NHIC), Logical Observation Identifier Names and Codes (LOINC) database, community health centers and their branch offices, and clinical laboratories of representative university medical centers. For standard expression, we referred to the English-Korean/ Korean-English medical dictionary of Korean Medical Association and the rules for foreign language translation. Programs for mapping between LOINC DB and EDI code and for translating English to Korean were developed. RESULTS: A Korean standard laboratory terminology database containing six axial concept names such as components, property, time aspect, system (specimen), scale type, and method type was established for 7,508 test observations. Short names and a mapping table for EDI codes and Unified Medical Language System (UMLS) were added. Synonym tables for concept names, words used in the database, and six axial terms were prepared to make it easier to find the standard terminology with common terms used in the field of laboratory medicine. CONCLUSIONS: Here we report for the first time a Korean standard laboratory terminology database for test names, result description terms, result units covering most laboratory tests in primary healthcare centers.
Clinical Laboratory Techniques/*classification/standards ; Databases, Factual ; Korea ; Language ; *Logical Observation Identifiers Names and Codes ; Terminology as Topic ; *Unified Medical Language System

This study was conducted to compare English keywords of articles published in major Korean Nursing Journals provided by the authors with the MeSH to see how often MeSH concepts are being used and suggest ways to improve use of the MeSH by Korean nurse researchers. Each English keyword provided by the author was compared with the MeSH concepts usign the MeSH browser. One thousand seven hundred eighty four articles published in 7 major Korean Nursing journals were reviewed. Only 44.2% (788 articles) out of 1,784 articles had at least one English keyword provided by the authors. There were total 2,137 English keywords used with 1,672 uniquely different keywords. Each article had 2.7 English keywords on the average. Twenty one point twenty nine percent of the English keywords matched with MeSH concepts. Reasons for not matching with MeSH concepts were that English keywords provided by the authors were unique nursing terminologies, psycho-social terminologies, phonetics of Korean words or pure Korean words. In order to have high quality bibliographical database, it is necessary to use standard keyword for indexing and searching nursing related literatures. This study recommends that use of a standard classification such as MeSH be included in the authors guideline for manuscript submission, keywords provided by authors be added or modified by the editor, and efforts to include those terms not matching with MeSH concepts into the MeSH classification be exercised.
Abstracting and Indexing as Topic ; Classification ; Nursing* ; Phonetics ; Subject Headings*

Standardization of medical terminology is essential in data transmission between health care institutes and in maximizing the benefits of information technology. The purpose of this study was to standardize medical terms for laboratory observations. During the second year of the study, a standard database of concept names for laboratory terms that covered those used in tertiary health care institutes and reference laboratories was developed. The laboratory terms in the Logical Observation Identifier Names and Codes (LOINC) database were adopted and matched with the electronic data interchange (EDI) codes in Korea. A public hearing and a workshop for clinical pathologists were held to collect the opinions of experts. The Korean standard laboratory terminology database containing six axial concept names, components, property, time aspect, system (specimen), scale type, and method type, was established for 29,340 test observations. Short names and mapping tables for EDI codes and UMLS were added. Synonym tables were prepared to help match concept names to common terms used in the fields. We herein described the Korean standard laboratory terminology database for test names, result description terms, and result units encompassing most of the laboratory tests in Korea.
Clinical Laboratory Information Systems/*standards ; Humans ; Laboratory Techniques and Procedures/*standards ; *Logical Observation Identifiers Names and Codes ; Terminology as Topic ; *Unified Medical Language System

OBJECTIVE: Indexing medical documents is important to retrieve medical information efficiently, but it is labor intensive and an annoying task for indexers or authors. This paper presents that whether an automatic indexing program can help the human task for Korean medical keyword indexing. METHODS: We developed an automatic indexing program using Korean Medical Subject Heading(K-MeSH) and evaluated the performance as compared with technical indexers and authors. RESULTS: Experimental result was that the current program's performance was much lower than technical indexers', but it was same as the authors' performance. CONCLUSION: The result showed that it is very affirmative to develop the automatic indexing program to help authors at least, and to help technical indexers with improving the program by enriching K-MeSH and utilizing K-MeSH structure.
Abstracting and Indexing as Topic* ; Humans ; Information Storage and Retrieval ; Medical Subject Headings

No abstract available.
Abstracting and Indexing as Topic* ; Health Occupations* ; Medical Subject Headings*

OBJECTIVES: Efficient identification of subject experts or expert communities is vital for the growth of any organization. Most of the available expert finding systems are based on self-nomination, which can be biased, and are unable to rank experts. Thus, the objective of this work was to develop a robust and unbiased expert finding system which can quantitatively measure expertise. METHODS: Medical Subject Headings (MeSH) is a controlled vocabulary developed by the National Library of Medicine (NLM) for indexing research publications, articles and books. Using the MeSH terms associated with peer-reviewed articles published from India and indexed in PubMed, we developed a Web-based program which can be used to identify subject experts and subjects associated with an expert. RESULTS: We have extensively tested our system to identify experts from India in various subjects. The system provides a ranked list of experts where known experts rank at the top of the list. The system is general; since it uses information available with the PubMed, it can be implemented for any country. CONCLUSIONS: The expert finding system is able to successfully identify subject experts in India. Our system is unique because it allows the quantification of subject expertise, thus enabling the ranking of experts. Our system is based on peer-reviewed information. Use of MeSH terms as subjects has standardized the subject terminology. The system matches requirements of an ideal expert finding system.
Abstracting and Indexing as Topic ; Bias (Epidemiology) ; Data Mining ; Expert Systems ; India ; Medical Subject Headings* ; National Library of Medicine (U.S.) ; Online Systems ; Professional Competence ; Vocabulary, Controlled

OBJECTIVE: A reference terminology is essential to achieve semantic interoperability and enhance the quality of health care. Reference terminologies that have achieved common acceptance contain many concepts that clinicians would not want in healthcare, which preclude their practical use in documentation of patient information. To solve the problems, this document proposes a reference terminology model which contains concepts that physicians can use satisfactorily. METHODS: We analyzed the structures of the UMLS and SNOMED CT. We also analyzed health care terms which had been collected by the Korea National Health Information Standard Committee. Based on the results of the analysis, we developed an object-oriented reference terminology model. And, we designed database schema with the model. RESULTS: Eight components of the UMLS and six components of the SNOMED CT were analyzed. The collected terms had various properties and mapping vocabularies according to the characteristics of their respective domains. A reference terminology model was developed from a three-level view using UML. A database schema was developed using ERD. CONCLUSION: This study mainly focuses on reference terminology modeling. It is hoped that this reference terminology modeling helps the semantic interoperable exchange of clinical documents as the basis of common EMR.
Delivery of Health Care* ; Hope ; Humans ; Korea ; Quality of Health Care ; Semantics ; Systematized Nomenclature of Medicine ; Unified Medical Language System ; Vocabulary