Mapping Korean National Health Insurance Pharmaceutical Claim Codes to SNOMED CT

Jung, Hwa Young; Park, Hyeoun-Ae; Kang, Hannah; Lee, Min-Sun; Sung, Sumi; Park, SeulKi

doi:10.3233/shti220462

Cited by 3 publications

(3 citation statements)

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“…Despite many useful applications offered by concept normalizations, such as aligning data sources between different institutions, these opportunities are limited when such concept ontologies in non-English are limited or the local institution's text EHR is especially noisy. For example, despite many Korean medical communities' huge interest in mapping their local EHR to standardized concepts [28,[67][68][69][70], the Korean-translated version of LOINC was only recently released in 2022 [71]. Therefore, these preliminary key concepts generated from XLM-RoBERTa suggest an exciting potential role of transformerbased language models in building or expanding non-English text resources at the individual medical institution level.…”

Section: Lif T(w) =mentioning

confidence: 99%

Identifying Alcohol-Related Information From Unstructured Bilingual Clinical Notes With Multilingual Transformers

Kim

Park

et al. 2023

IEEE Access

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As a key modifiable risk factor, alcohol consumption is clinically crucial information that allows medical professionals to further understand their patients' medical conditions and suggest appropriate lifestyle modifying interventions. However, identifying alcohol-related information from unstructured freetext clinical notes is often challenging. Not only are the formats of the notes inconsistent, but they also include a massive amount of non-alcohol-related information. Furthermore, for medical institutions outside of English-speaking countries, these clinical notes contain both a mixture of English and local languages, inducing additional difficulty in the extraction. Thanks to the increasing availability of electronic medical record (EMR), several previous works explored the idea of using natural language processing (NLP) to train machine learning models that automatically identify alcohol-related information from unstructured clinical notes. However, all these previous works are limited to English clinical notes, thereby able to leverage various large-scale external ontologies during the text preprocessing. Furthermore, they rely on simple NLP techniques such as the bag-of-words models that suffer from high dimensionality and out-ofvocabulary issues. Addressing these issues, we adopt fine-tuning multilingual transformers. By leveraging their linguistically rich contextual information learned during their pre-training, we are able to extract alcohol-related information from unstructured clinical notes without preprocessing the clinical notes on any external ontologies. Furthermore, our work is the first to explore the use of transformers in bilingual clinical notes to extract alcohol-related information. Even with minimal text preprocessing, we achieve extraction accuracy of 84.70% in terms of macro F-1 score. INDEX TERMS clinical informatics, alcohol information extraction, natural language processing, information extraction from clinical notes, multilingual transformers I. INTRODUCTIONAs medical institutions worldwide are widely adopting electronic medical record (EMR), vast amounts of healthcare data are produced and stored electronically [1-5]. As a significant component of EMR, clinical notes, which record patients' conditions in free text, provide essential information such as patients' medical history, social history, or lifestyle patterns. Despite being a vital data source, its practical use in medical decision support systems is hampered by challenges in extracting key information from its unstructured text format [6][7][8][9]. For medical institutions in non-English speaking countries, these challenges are compounded by the use of both English and local languages in their clinical notes. Besides standardizing the notes to resolve any inconsistent formats or structures, they need to handle the multilingual aspect of their notes simultaneously. Due to this additional

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Section: Lif T(w) =mentioning

confidence: 99%

Identifying Alcohol-Related Information From Unstructured Bilingual Clinical Notes With Multilingual Transformers

Kim

Park

et al. 2023

IEEE Access

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“…In 2020, to achieve semantic interoperability in healthcare, South Korea joined the SNOMED International as the 39 th member country. As a way to promote the use of SNOMED CT in Korea, there have been various national initiatives such as mapping Korean Classification of Disease-7 (KCD-7), national health insurance claims codes which is also called electronic data interchange (EDI) codes for procedures, pharmaceutical products, narrative medical records of gastrectomy patients, and Korean national health checkup questionnaire to SNOMED CT [4][5][6][7][8]. However, the national health insurance claims codes for laboratory tests were not mapped to SNOMED CT, limiting the use of laboratory tests information for patient care or clinical research.…”

Section: Introductionmentioning

confidence: 99%

Mapping Korean National Health Insurance Claim Codes for Laboratory Test to SNOMED CT

Sung

Park

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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The aim of this study was to map Korean national health insurance claims codes for laboratory tests to SNOMED CT. The mapping source codes were 4,111 claims codes for laboratory test and mapping target codes were the International Edition of SNOMED CT released on July 31, 2020. We used rule-based automated and manual mapping methods. The mapping results were validated by two experts. Out of 4,111 codes, 90.5% were mapped to the concepts of procedure hierarchy in SNOMED CT. Of them, 51.4% of the codes were exactly mapped to SNOMED CT concepts, and 34.8% of the codes were mapped to SNOMED CT concepts as one-to-one mapping.

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“…To date, there have been few studies related to CDSSs for nursing activity, and most of them were conducted to develop and evaluate a prototype based on EMRs, 22 not CDSS development for clinical performance. 23 Furthermore, there is limited information on oral mucosa PU prevention.…”

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confidence: 99%

Development and Effectiveness of a Clinical Decision Support System for Pressure Ulcer Prevention Care Using Machine Learning

Kim

Ryu²,

Choi³

2022

CIN: Computers, Informatics, Nursing

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This study was conducted to develop and evaluate the effectiveness of a clinical decision support system for pressure ulcer prevention on clinical (performance, visual discrimination ability, and decision-making ability) and cognitive (knowledge and attitude) workflow. After developing a clinical decision support system using machine learning, a quasiexperimental study was used. Data were collected between January and April 2020. Forty-nine RNs who met the inclusion criteria and worked at seven tertiary and five secondary hospitals participated. A clinical decision support system was provided to the intervention group during the same period. Differences in outcome variables between the two groups were analyzed using t tests. The level of pressure ulcer prevention nursing performance and visual differentiation ability of skin pressure and oral mucosa pressure ulcer showed significantly greater improvement in the experimental group compared with the control group, whereas clinical decision making did not differ significantly. A clinical decision support system using machine learning was partially successful in performance of skin pressure ulcer prevention, attitude, and visual differentiation ability for skin and oral mucosa pressure ulcer prevention. These findings indicated that a clinical decision support system using machine learning needs to be implemented for pressure ulcer prevention.

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Mapping Korean National Health Insurance Pharmaceutical Claim Codes to SNOMED CT

Cited by 3 publications

References 0 publications

Identifying Alcohol-Related Information From Unstructured Bilingual Clinical Notes With Multilingual Transformers

Identifying Alcohol-Related Information From Unstructured Bilingual Clinical Notes With Multilingual Transformers

Mapping Korean National Health Insurance Claim Codes for Laboratory Test to SNOMED CT

Development and Effectiveness of a Clinical Decision Support System for Pressure Ulcer Prevention Care Using Machine Learning

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