Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: Depression among elderly individuals, especially those living alone, is an important social issue. This study investigated the prevalence of depressive symptoms and related factors in elderly individuals living alone in Jeju. Methods: A survey was conducted among elderly individuals living alone and registered in the Basic Senior Care Services. Depressive symptoms were analyzed using the Short Form Geriatric Depression Scale, with scores ≥6 indicat-ing depression. Factors such as sex, age, education level, marital status, income, health status, underlying diseases, smoking, alcohol consumption, and suicide risk were also examined. Results: Of the 4,742 participants, 52.6% showed a high prevalence of depressive symptoms. Factors affecting prevalence included sex (p=0.020), age (p<0.001), education (p<0.001), subjective health (p<0.001), smoking history (p<0.001), cancer (p=0.001), dementia (p<0.001), and diagnostic history of depression (p<0.001), whose symptoms were correlated with suicidal thoughts, plans, or attempts (p<0.001). Multivariate logistic regression indicated that sex, age, education level, subjective health, dementia, and current smoking and alcohol consumption were significant factors. Conclusion Depression among elderly individuals living alone in Jeju is severe and influenced by sociocultural factors. Early detection and mental health policies are essential to reduce its prevalence and socioeconomic impact.

Background: Depression among elderly individuals, especially those living alone, is an important social issue. This study investigated the prevalence of depressive symptoms and related factors in elderly individuals living alone in Jeju. Methods: A survey was conducted among elderly individuals living alone and registered in the Basic Senior Care Services. Depressive symptoms were analyzed using the Short Form Geriatric Depression Scale, with scores ≥6 indicat-ing depression. Factors such as sex, age, education level, marital status, income, health status, underlying diseases, smoking, alcohol consumption, and suicide risk were also examined. Results: Of the 4,742 participants, 52.6% showed a high prevalence of depressive symptoms. Factors affecting prevalence included sex (p=0.020), age (p<0.001), education (p<0.001), subjective health (p<0.001), smoking history (p<0.001), cancer (p=0.001), dementia (p<0.001), and diagnostic history of depression (p<0.001), whose symptoms were correlated with suicidal thoughts, plans, or attempts (p<0.001). Multivariate logistic regression indicated that sex, age, education level, subjective health, dementia, and current smoking and alcohol consumption were significant factors. Conclusion Depression among elderly individuals living alone in Jeju is severe and influenced by sociocultural factors. Early detection and mental health policies are essential to reduce its prevalence and socioeconomic impact.

Background: Depression among elderly individuals, especially those living alone, is an important social issue. This study investigated the prevalence of depressive symptoms and related factors in elderly individuals living alone in Jeju. Methods: A survey was conducted among elderly individuals living alone and registered in the Basic Senior Care Services. Depressive symptoms were analyzed using the Short Form Geriatric Depression Scale, with scores ≥6 indicat-ing depression. Factors such as sex, age, education level, marital status, income, health status, underlying diseases, smoking, alcohol consumption, and suicide risk were also examined. Results: Of the 4,742 participants, 52.6% showed a high prevalence of depressive symptoms. Factors affecting prevalence included sex (p=0.020), age (p<0.001), education (p<0.001), subjective health (p<0.001), smoking history (p<0.001), cancer (p=0.001), dementia (p<0.001), and diagnostic history of depression (p<0.001), whose symptoms were correlated with suicidal thoughts, plans, or attempts (p<0.001). Multivariate logistic regression indicated that sex, age, education level, subjective health, dementia, and current smoking and alcohol consumption were significant factors. Conclusion Depression among elderly individuals living alone in Jeju is severe and influenced by sociocultural factors. Early detection and mental health policies are essential to reduce its prevalence and socioeconomic impact.