Purpose: Immunochemical fecal occult blood tests (iFOBT) have been utilized as the primary method for colorectal cancer screening within Korea's National Cancer Screening Program. This study aimed to evaluate the impact of the accreditation program for clinical laboratories and external quality assessment (EQA) programs on colorectal cancer screening. Methods: We analyzed the false-positive rates of iFOBT in colorectal cancer screening from 2016 to 2020 according to participation and performance in the Outstanding Laboratory Accreditation Program (OLAP) conducted by the Laboratory Medicine Foundation, and the External Quality Assessment programs run by the Korean Association of External Quality Assessment Service. Results: False-positive rates of iFOBT were lower among institutions accredited by OLAP (2.35%) compared with non-accredited (3.04%) and non-participating institutions (5.60%). Similarly, institutions participating in the EQA program exhibited lower false-positive rates (3.79%) compared to non-participants (7.04%). Within the iFOBT-specific EQA program, institutions that passed demonstrated the lowest false-positive rate (3.37%), while failing institutions showed the highest rate (9.07%), surpassing even non-participating institutions (6.44%). Conclusion Participation in quality management programs such as OLAP and EQA was associated with lower false-positive rates in iFOBT for colorectal cancer screening. These findings suggest that quality management initiatives can increase the accuracy of iFOBT, potentially improving the effectiveness of colorectal cancer screening programs, and reducing unnecessary follow-up procedures and associated healthcare costs.

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.

Purpose: Immunochemical fecal occult blood tests (iFOBT) have been utilized as the primary method for colorectal cancer screening within Korea's National Cancer Screening Program. This study aimed to evaluate the impact of the accreditation program for clinical laboratories and external quality assessment (EQA) programs on colorectal cancer screening. Methods: We analyzed the false-positive rates of iFOBT in colorectal cancer screening from 2016 to 2020 according to participation and performance in the Outstanding Laboratory Accreditation Program (OLAP) conducted by the Laboratory Medicine Foundation, and the External Quality Assessment programs run by the Korean Association of External Quality Assessment Service. Results: False-positive rates of iFOBT were lower among institutions accredited by OLAP (2.35%) compared with non-accredited (3.04%) and non-participating institutions (5.60%). Similarly, institutions participating in the EQA program exhibited lower false-positive rates (3.79%) compared to non-participants (7.04%). Within the iFOBT-specific EQA program, institutions that passed demonstrated the lowest false-positive rate (3.37%), while failing institutions showed the highest rate (9.07%), surpassing even non-participating institutions (6.44%). Conclusion Participation in quality management programs such as OLAP and EQA was associated with lower false-positive rates in iFOBT for colorectal cancer screening. These findings suggest that quality management initiatives can increase the accuracy of iFOBT, potentially improving the effectiveness of colorectal cancer screening programs, and reducing unnecessary follow-up procedures and associated healthcare costs.

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.

Purpose: Immunochemical fecal occult blood tests (iFOBT) have been utilized as the primary method for colorectal cancer screening within Korea's National Cancer Screening Program. This study aimed to evaluate the impact of the accreditation program for clinical laboratories and external quality assessment (EQA) programs on colorectal cancer screening. Methods: We analyzed the false-positive rates of iFOBT in colorectal cancer screening from 2016 to 2020 according to participation and performance in the Outstanding Laboratory Accreditation Program (OLAP) conducted by the Laboratory Medicine Foundation, and the External Quality Assessment programs run by the Korean Association of External Quality Assessment Service. Results: False-positive rates of iFOBT were lower among institutions accredited by OLAP (2.35%) compared with non-accredited (3.04%) and non-participating institutions (5.60%). Similarly, institutions participating in the EQA program exhibited lower false-positive rates (3.79%) compared to non-participants (7.04%). Within the iFOBT-specific EQA program, institutions that passed demonstrated the lowest false-positive rate (3.37%), while failing institutions showed the highest rate (9.07%), surpassing even non-participating institutions (6.44%). Conclusion Participation in quality management programs such as OLAP and EQA was associated with lower false-positive rates in iFOBT for colorectal cancer screening. These findings suggest that quality management initiatives can increase the accuracy of iFOBT, potentially improving the effectiveness of colorectal cancer screening programs, and reducing unnecessary follow-up procedures and associated healthcare costs.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.