Strategies for Adopting and Implementing SNOMED CT in Korea

A SNOMED CT Mapping Guideline for the Local Terms Used to Document Clinical Findings and Procedures in Electronic Medical Records in South Korea: Methodological Study

BACKGROUND

South Korea joined SNOMED International as the 39th member country. To ensure semantic interoperability, South Korea introduced SNOMED CT (Systemized Nomenclature of Medicine-Clinical Terms) in 2020. However, there is no methodology to map local Korean terms to SNOMED CT. Instead, this is performed sporadically and independently at each local medical institution. The quality of the mapping, therefore, cannot be guaranteed.

OBJECTIVE

This study aimed to develop and introduce a guideline to map local Korean terms to the SNOMED CT used to document clinical findings and procedures in electronic health records at health care institutions in South Korea.

METHODS

The guidelines were developed from December 2020 to December 2022. An extensive literature review was conducted. The overall structures and contents of the guidelines with diverse use cases were developed by referencing the existing SNOMED CT mapping guidelines, previous studies related to SNOMED CT mapping, and the experiences of the committee members. The developed guidelines were validated by a guideline review panel.

RESULTS

The SNOMED CT mapping guidelines developed in this study recommended the following 9 steps: define the purpose and scope of the map, extract terms, preprocess source terms, preprocess source terms using clinical context, select a search term, use search strategies to find SNOMED CT concepts using a browser, classify mapping correlations, validate the map, and build the final map format.

CONCLUSIONS

The guidelines developed in this study can support the standardized mapping of local Korean terms into SNOMED CT. Mapping specialists can use this guideline to improve the mapping quality performed at individual local medical institutions.

The importance of SNOMED CT concept specificity in healthcare analytics

BACKGROUND

Healthcare data frequently lack the specificity level needed to achieve clinical and operational objectives such as optimising bed management. Pneumonia is a disease of importance as it accounts for more bed days than any other lung disease and has a varied aetiology. The condition has a range of SNOMED CT concepts with different levels of specificity.

OBJECTIVE

This study aimed to quantify the importance of the specificity of an SNOMED CT concept, against well-established predictors, for forecasting length of stay for pneumonia patients.

METHOD

A retrospective data analysis was conducted of pneumonia admissions to a tertiary hospital between 2011 and 2021. For inclusion, the primary diagnosis was a subtype of bacterial or viral pneumonia, as identified by SNOMED CT concepts. Three linear mixed models were constructed. Model One included known predictors of length of stay. Model Two included the predictors in Model One and SNOMED CT concepts of lower specificity. Model Three included the Model Two predictors and the concepts with higher specificity. Model performances were compared.

RESULTS

Sex, ethnicity, deprivation rank and Charlson Comorbidity Index scores (age-adjusted) were meaningful predictors of length of stay in all models. Inclusion of lower specificity SNOMED CT concepts did not significantly improve performance (ΔR2 = 0.41%, p = .058). SNOMED CT concepts with higher specificity explained more variance than each of the individual predictors (ΔR2 = 4.31%, p < .001).

CONCLUSION

SNOMED CT concepts with higher specificity explained more variance in length of stay than a range of well-studied predictors.

IMPLICATIONS

Accurate and specific clinical documentation using SNOMED CT can improve predictive modelling and the generation of actionable insights. Resources should be dedicated to optimising and assuring clinical documentation quality at the point of recording.

Effectiveness of the Use of Standardized Vocabularies on Epilepsy Patient Cohort Generation

Objectives

This study investigated the effectiveness of using standardized vocabularies to generate epilepsy patient cohorts with local medical codes, SNOMED Clinical Terms (SNOMED CT), and International Classification of Diseases tenth revision (ICD-10)/Korean Classification of Diseases-7 (KCD-7).

Methods

We compared the granularity between SNOMED CT and ICD-10 for epilepsy by counting the number of SNOMED CT concepts mapped to one ICD-10 code. Next, we created epilepsy patient cohorts by selecting all patients who had at least one code included in the concept sets defined using each vocabulary. We set patient cohorts generated by local codes as the reference to evaluate the patient cohorts generated using SNOMED CT and ICD-10/KCD-7. We compared the number of patients, the prevalence of epilepsy, and the age distribution between patient cohorts by year.

Results

In terms of the cohort size, the match rate with the reference cohort was approximately 99.2% for SNOMED CT and 94.0% for ICD-10/KDC7. From 2010 to 2019, the mean prevalence of epilepsy defined using the local codes, SNOMED CT, and ICD-10/KCD-7 was 0.889%, 0.891% and 0.923%, respectively. The age distribution of epilepsy patients showed no significant difference between the cohorts defined using local codes or SNOMED CT, but the ICD-9/KCD-7-generated cohort showed a substantial gap in the age distribution of patients with epilepsy compared to the cohort generated using the local codes.

Conclusions

The number and age distribution of patients were substantially different from the reference when we used ICD-10/KCD-7 codes, but not when we used SNOMED CT concepts. Therefore, SNOMED CT is more suitable for representing clinical ideas and conducting clinical studies than ICD-10/KCD-7.

Angiotensin-converting enzyme inhibitors versus angiotensin receptor blockers: New-onset diabetes mellitus stratified by statin use

WHAT IS KNOWN AND OBJECTIVES

Regardless of statin use, which is known to induce hyperglycaemia, comparative studies on the risk of new-onset diabetes mellitus (NODM) with angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) are needed. This study evaluated the effects of ACEIs and ARBs on NODM in the clinical setting.

METHODS

This retrospective cohort study utilized electronic medical record data from Seoul St. Mary's Hospital and Seoul National University Hospital from 2009 to 2012. Patients who were prescribed ACEIs or ARBs for the first time (irrespective of concomitant statin use) were followed up for 5 years.

RESULTS AND DISCUSSIONS

A total of 11,703 patients were included, 24.9% (n = 2916) were taking ACEIs and 75.1% (n = 9189) were taking ARBs. Patients on ACEIs had a significantly lower incidence of NODM both with statin use (HR = 0.13, p < 0.001) and without (HR = 0.15, p = 0.009) than patients on ARBs. Age ≥60 years (HR = 1.49, p = 0.010), BMI ≥25 (HR = 1.96, p < 0.010), use of calcium channel blockers (HR = 1.47, p = 0.010), and diuretics (HR = 1.48, p = 0.010) were risk factors for NODM with statin use.

WHAT IS NEW AND CONCLUSION

Patients taking ACEIs are less likely to develop NODM than patients taking ARBs, irrespective of statin use. Patients' conditions, including the risk of NODM, should be considered before prescribing ACEIs or ARBs. Future randomized clinical trials are needed to clarify further the relationship between ACEIs and ARBs and their effect on NODM.