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

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

OBJECTIVES: Many countries try to efficiently deliver high quality healthcare services at lower and manageable costs where healthcare information and communication technologies (ICT) standardisation may play an important role. New Zealand provides a good model of healthcare ICT standardisation. The purpose of this study was to review the current healthcare ICT standardisation and progress in New Zealand. METHODS: This study reviewed the reports regarding the healthcare ICT standardisation in New Zealand. We also investigated relevant websites related with the healthcare ICT standards, most of which were run by the government. Then, we summarised the governance structure, standardisation processes, and their output regarding the current healthcare ICT standards status of New Zealand. RESULTS: New Zealand government bodies have established a set of healthcare ICT standards and clear guidelines and procedures for healthcare ICT standardisation. Government has actively participated in various enactments of healthcare ICT standards from the inception of ideas to their eventual retirement. Great achievements in eHealth have already been realized, and various standards are currently utilised at all levels of healthcare regionally and nationally. Standard clinical terminologies, such as International Classification of Diseases (ICD) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) have been adopted and Health Level Seven (HL7) standards are actively used in health information exchanges. CONCLUSIONS: The government to New Zealand has well organised ICT institutions, guidelines, and regulations, as well as various programs, such as e-Medications and integrated care services. Local district health boards directly running hospitals have effectively adopted various new ICT standards. They might already be benefiting from improved efficiency resulting from healthcare ICT standardisation.
Delivery of Health Care* ; Health Level Seven ; Informatics ; Information Science ; International Classification of Diseases ; Medical Informatics ; New Zealand* ; Retirement ; Running ; Social Control, Formal ; Systematized Nomenclature of Medicine ; Telemedicine

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*

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

No abstract available.
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 objective is to introduce 'clinical archetype' which is a formal and agreed way of representing clinical information to ensure interoperability across and within Electronic Health Records (EHRs). The paper also aims at presenting the challenges building quality labeled clinical archetypes and the challenges towards achieving semantic interoperability between EHRs. METHODS: Twenty years of international research, various European healthcare informatics projects and the pioneering work of the openEHR Foundation have led to the following results. RESULTS: The requirements for EHR information architectures have been consolidated within ISO 18308 and adopted within the ISO 13606 EHR interoperability standard. However, a generic EHR architecture cannot ensure that the clinical meaning of information from heterogeneous sources can be reliably interpreted by receiving systems and services. Therefore, clinical models called 'clinical archetypes' are required to formalize the representation of clinical information within the EHR. Part 2 of ISO 13606 defines how archetypes should be formally represented. The current challenge is to grow clinical communities to build a library of clinical archetypes and to identify how evidence of best practice and multi-professional clinical consensus should best be combined to define archetypes at the optimal level of granularity and specificity and quality label them for wide adoption. Standardizing clinical terms within EHRs using clinical terminology like Systematized Nomenclature of Medicine Clinical Terms is also a challenge. CONCLUSIONS: Clinical archetypes would play an important role in achieving semantic interoperability within EHRs. Attempts are being made in exploring the design and adoption challenges for clinical archetypes.
Consensus ; Delivery of Health Care ; Electronic Health Records ; Health Information Management ; Informatics ; Practice Guidelines as Topic ; Semantics* ; Sensitivity and Specificity ; Systematized Nomenclature of Medicine

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