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: LOINC(R)(Logical Observations Identifiers, Names, Codes) is being used as the global standard for sharing laboratory test information and standardization. However, difficulties have been encountered in transferring local code to LOINC. Use in existing laboratory information systems(LIS) is possible with maximized local codes and LOINC mapping. Since the existing mapping tool has parts that do not match domestic medical environments, it is difficult to use without modification or supplementation. To this end, we have developed algorithms for LOINC mapping and have evaluated their usefulness. METHODS: We used 2,376 M-codes transformed from Pusan National University Hospital's 1,150 local codes, and codes from various laboratory test domains(Diagnostic Hematology, Clinical Chemistry, Seroimmunology, Molecular and Cytogenetics, Microbiology, Transfusion Medicine). In materializing the automatic mapping algorithms, spread sheet programs(Excel, Microsoft) and existing mapping tools(RELMA, Regenstrief) were used. The accuracy of the mapped codes was verified by a specialist of the Laboratory Medicine Department. RESULTS: Of the 2,376 M-codes, mapping on LOINC was found to be possible for 78.7%(1,871) while LOINC corresponding with the local codes could not be found for 21.3%(505). Of the mapped codes, 90.8%(1,699) were mapped accurately automatically, while the rest were mapped manually. CONCLUSIONS: The LOINC mapping algorithm that was developed in this study was useful for mapping various forms of local code with LOINC.
Adoption ; Chemistry, Clinical ; Cytogenetics ; Hematology ; Logical Observation Identifiers Names and Codes ; Specialization

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

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

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