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.

With Industry 4.0, big data and artificial intelligence have become paramount in the field of medicine. Electronic health records, the primary source of medical data, are not collected for research purposes but represent real-world data; therefore, they have various constraints. Although structured, laboratory data often contain unstandardized terminology or missing information. The major challenge lies in the lack of standardization of test results in terms of metrology, which complicates comparisons across laboratories. In this review, we delve into the essential components necessary for integrating real-world laboratory data into high-quality big data, including the standardization of terminology, data formats, equations, and the harmonization and standardization of results. Moreover, we address the transference and adjustment of laboratory results, along with the certification for quality of laboratory data. By discussing these critical aspects, we seek to shed light on the challenges and opportunities inherent to utilizing real-world laboratory data within the framework of healthcare big data and artificial intelligence.

Machine learning (ML) is currently being widely studied and applied in data analysis and prediction in various fields, including laboratory medicine. To comprehensively evaluate the application of ML in laboratory medicine, we reviewed the literature on ML applications in laboratory medicine published between February 2014 and March 2024. A PubMed search using a search string yielded 779 articles on the topic, among which 144 articles were selected for this review. These articles were analyzed to extract and categorize related fields within laboratory medicine, research objectives, specimen types, data types, ML models, evaluation metrics, and sample sizes. Sankey diagrams and pie charts were used to illustrate the relationships between categories and the proportions within each category. We found that most studies involving the application of ML in laboratory medicine were designed to improve efficiency through automation or expand the roles of clinical laboratories. The most common ML models used are convolutional neural networks, multilayer perceptrons, and tree-based models, which are primarily selected based on the type of input data. Our findings suggest that, as the technology evolves, ML will rise in prominence in laboratory medicine as a tool for expanding research activities. Nonetheless, expertise in ML applications should be improved to effectively utilize this technology.

High-quality specimens are essential for accurate laboratory results. Preanalytical errors due to issues, such as hemolysis, microclotting, and insufficient specimen volume, account for 60%–70% of laboratory errors and frequently result from improper blood collection techniques or negligence during the collection process. Therefore, standardized blood collection guidelines and continuous education are required. In Korea, standardized venous blood collection procedures have not yet been fully established, highlighting the need for an evidence-based protocol tailored to local requirements. The venous blood collection guideline presented here was adapted from international standards to conform to globally recognized practices and address the Korean clinical context. The guideline, developed by the Korean Society for Laboratory Medicine, outlines the critical steps in venous blood collection, from patient identification and consent to post-collection handling. Practical recommendations are provided for medical students, doctors, nurses, and medical technologists. The guideline addresses specific considerations for pediatric and older patients, as well as individuals undergoing blood culture tests, with an emphasis on minimizing errors and promoting the safety of patients and medical staff. The guideline includes practical tools, such as checklists and detailed information on sampling devices, to facilitate implementation. This initiative would help standardize blood collection practices, improve specimen quality, and enhance patient care by ensuring accurate laboratory results in clinical settings.