Background: Hemolysis is an important preanalytical factor that influences laboratory test results. Because arterial blood gas analysis (ABGA) is performed using whole blood, it is difficult to visually check whether a specimen is hemolyzed, and even blood gas analyzers cannot detect hemolysis. However, there is insufficient consensus on the parameters that are influenced by hemolyzed specimens. This study aimed to determine the effect of hemolysis on ABGA results. Methods: One hundred residual arterial blood specimens were collected from Severance Hospital between March and April 2022. Samples were aliquoted into three groups for mechanical hemolysis. Hemolysis was induced using 16-, 22-, and 26-gauge needles and measured using the Profile pHOx Ultra Blood Gas Analyzer (Nova Biomedical, USA). The remaining blood was centrifuged, and the hemolysis index was determined using the plasma. Results: Among the parameters, pH and K increased, whereas pCO 2 , Na,Ca 2+ , and HCO 3− decreased. The values of Hb, Mg2+ , and Hct did not change with the degree of hemolysis, although there was a difference between the two groups. The values of pCO 2 , Hb, K, and Ca 2+ increased as the degree of hemolysis increased, with % biases exceeding the desirable bias. Conclusions This study confirmed that hemolysis significantly influences pH, pCO 2 , and K. Therefore, when clinical findings and blood gas analysis results are inconsistent, clinicians should be cautious of spurious hemolysis when interpreting the results.

Background: Hemolysis is an important preanalytical factor that influences laboratory test results. Because arterial blood gas analysis (ABGA) is performed using whole blood, it is difficult to visually check whether a specimen is hemolyzed, and even blood gas analyzers cannot detect hemolysis. However, there is insufficient consensus on the parameters that are influenced by hemolyzed specimens. This study aimed to determine the effect of hemolysis on ABGA results. Methods: One hundred residual arterial blood specimens were collected from Severance Hospital between March and April 2022. Samples were aliquoted into three groups for mechanical hemolysis. Hemolysis was induced using 16-, 22-, and 26-gauge needles and measured using the Profile pHOx Ultra Blood Gas Analyzer (Nova Biomedical, USA). The remaining blood was centrifuged, and the hemolysis index was determined using the plasma. Results: Among the parameters, pH and K increased, whereas pCO 2 , Na,Ca 2+ , and HCO 3− decreased. The values of Hb, Mg2+ , and Hct did not change with the degree of hemolysis, although there was a difference between the two groups. The values of pCO 2 , Hb, K, and Ca 2+ increased as the degree of hemolysis increased, with % biases exceeding the desirable bias. Conclusions This study confirmed that hemolysis significantly influences pH, pCO 2 , and K. Therefore, when clinical findings and blood gas analysis results are inconsistent, clinicians should be cautious of spurious hemolysis when interpreting the results.

Background: Hemolysis is an important preanalytical factor that influences laboratory test results. Because arterial blood gas analysis (ABGA) is performed using whole blood, it is difficult to visually check whether a specimen is hemolyzed, and even blood gas analyzers cannot detect hemolysis. However, there is insufficient consensus on the parameters that are influenced by hemolyzed specimens. This study aimed to determine the effect of hemolysis on ABGA results. Methods: One hundred residual arterial blood specimens were collected from Severance Hospital between March and April 2022. Samples were aliquoted into three groups for mechanical hemolysis. Hemolysis was induced using 16-, 22-, and 26-gauge needles and measured using the Profile pHOx Ultra Blood Gas Analyzer (Nova Biomedical, USA). The remaining blood was centrifuged, and the hemolysis index was determined using the plasma. Results: Among the parameters, pH and K increased, whereas pCO 2 , Na,Ca 2+ , and HCO 3− decreased. The values of Hb, Mg2+ , and Hct did not change with the degree of hemolysis, although there was a difference between the two groups. The values of pCO 2 , Hb, K, and Ca 2+ increased as the degree of hemolysis increased, with % biases exceeding the desirable bias. Conclusions This study confirmed that hemolysis significantly influences pH, pCO 2 , and K. Therefore, when clinical findings and blood gas analysis results are inconsistent, clinicians should be cautious of spurious hemolysis when interpreting the results.

Breast cancer is the most common cancer in women worldwide, and its early detection is critical for improving survival outcomes. As a diagnostic and screening tool, mammography can be less effective owing to the masking effect of fibroglandular tissue, but breast US has good sensitivity even in dense breasts. However, breast US is highly operator dependent, highlighting the need for artificial intelligence (AI)-driven solutions. Unlike other modalities, US is performed using a handheld device that produces a continuous real-time video stream, yielding 12000–48000 frames per examination. This can be significantly challenging for AI development and requires real-time AI inference capabilities. In this review, we classified AI solutions as computer-aided diagnosis and computer-aided detection to facilitate a functional understanding and review commercial software supported by clinical evidence.In addition, to bridge healthcare gaps and enhance patient outcomes in geographically under resourced areas, we propose a novel framework by reviewing the existing AI-based triage workflows including mobile ultrasound.

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.

Breast cancer is the most common cancer in women worldwide, and its early detection is critical for improving survival outcomes. As a diagnostic and screening tool, mammography can be less effective owing to the masking effect of fibroglandular tissue, but breast US has good sensitivity even in dense breasts. However, breast US is highly operator dependent, highlighting the need for artificial intelligence (AI)-driven solutions. Unlike other modalities, US is performed using a handheld device that produces a continuous real-time video stream, yielding 12000–48000 frames per examination. This can be significantly challenging for AI development and requires real-time AI inference capabilities. In this review, we classified AI solutions as computer-aided diagnosis and computer-aided detection to facilitate a functional understanding and review commercial software supported by clinical evidence.In addition, to bridge healthcare gaps and enhance patient outcomes in geographically under resourced areas, we propose a novel framework by reviewing the existing AI-based triage workflows including mobile ultrasound.

Background: Hemolysis is an important preanalytical factor that influences laboratory test results. Because arterial blood gas analysis (ABGA) is performed using whole blood, it is difficult to visually check whether a specimen is hemolyzed, and even blood gas analyzers cannot detect hemolysis. However, there is insufficient consensus on the parameters that are influenced by hemolyzed specimens. This study aimed to determine the effect of hemolysis on ABGA results. Methods: One hundred residual arterial blood specimens were collected from Severance Hospital between March and April 2022. Samples were aliquoted into three groups for mechanical hemolysis. Hemolysis was induced using 16-, 22-, and 26-gauge needles and measured using the Profile pHOx Ultra Blood Gas Analyzer (Nova Biomedical, USA). The remaining blood was centrifuged, and the hemolysis index was determined using the plasma. Results: Among the parameters, pH and K increased, whereas pCO 2 , Na,Ca 2+ , and HCO 3− decreased. The values of Hb, Mg2+ , and Hct did not change with the degree of hemolysis, although there was a difference between the two groups. The values of pCO 2 , Hb, K, and Ca 2+ increased as the degree of hemolysis increased, with % biases exceeding the desirable bias. Conclusions This study confirmed that hemolysis significantly influences pH, pCO 2 , and K. Therefore, when clinical findings and blood gas analysis results are inconsistent, clinicians should be cautious of spurious hemolysis when interpreting the results.

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.