Purpose: To evaluate the compatibility and usability of test results obtained from the IDRA and Keratograph 5M in clinical settings by comparing their performance in patients with dry eye disease. Methods: From December 27 to 30, 2022, a study was conducted on 30 patients diagnosed with dry eye utilizing both the Keratograph 5M and IDRA devices. The parameters compared and analyzed included lipid layer thickness, tear meniscus height, tear film break-up time, and meibography. A paired t-test was used for statistical comparison. The lipid layer thickness in the Keratograph 5M was graded on a scale from 0 to 4 based on thickness. Results: No significant differences were found between the two devices in tear film break-up time, tear meniscus height, and meibography (p = 0.148, 0.072, 0.124, respectively). However, the tear lipid layer thickness measured by IDRA showed a proportional relationship with the grade assigned by the Keratograph 5M (Kendall R = 0.217, p = 0.037; Spearman R = 0.260, p = 0.045). Conclusions The IDRA device offers the advantage of performing multiple dry eye tests; simultaneously, thereby saving time compared to the Keratograph 5M. Both devices can be used compatibly with IDRA particularly advantageous for providing a numerical value for tear lipid layer thickness which enhances the convenience of dry eye diagnosis and treatment.

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: To evaluate the compatibility and usability of test results obtained from the IDRA and Keratograph 5M in clinical settings by comparing their performance in patients with dry eye disease. Methods: From December 27 to 30, 2022, a study was conducted on 30 patients diagnosed with dry eye utilizing both the Keratograph 5M and IDRA devices. The parameters compared and analyzed included lipid layer thickness, tear meniscus height, tear film break-up time, and meibography. A paired t-test was used for statistical comparison. The lipid layer thickness in the Keratograph 5M was graded on a scale from 0 to 4 based on thickness. Results: No significant differences were found between the two devices in tear film break-up time, tear meniscus height, and meibography (p = 0.148, 0.072, 0.124, respectively). However, the tear lipid layer thickness measured by IDRA showed a proportional relationship with the grade assigned by the Keratograph 5M (Kendall R = 0.217, p = 0.037; Spearman R = 0.260, p = 0.045). Conclusions The IDRA device offers the advantage of performing multiple dry eye tests; simultaneously, thereby saving time compared to the Keratograph 5M. Both devices can be used compatibly with IDRA particularly advantageous for providing a numerical value for tear lipid layer thickness which enhances the convenience of dry eye diagnosis and treatment.

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: To evaluate the compatibility and usability of test results obtained from the IDRA and Keratograph 5M in clinical settings by comparing their performance in patients with dry eye disease. Methods: From December 27 to 30, 2022, a study was conducted on 30 patients diagnosed with dry eye utilizing both the Keratograph 5M and IDRA devices. The parameters compared and analyzed included lipid layer thickness, tear meniscus height, tear film break-up time, and meibography. A paired t-test was used for statistical comparison. The lipid layer thickness in the Keratograph 5M was graded on a scale from 0 to 4 based on thickness. Results: No significant differences were found between the two devices in tear film break-up time, tear meniscus height, and meibography (p = 0.148, 0.072, 0.124, respectively). However, the tear lipid layer thickness measured by IDRA showed a proportional relationship with the grade assigned by the Keratograph 5M (Kendall R = 0.217, p = 0.037; Spearman R = 0.260, p = 0.045). Conclusions The IDRA device offers the advantage of performing multiple dry eye tests; simultaneously, thereby saving time compared to the Keratograph 5M. Both devices can be used compatibly with IDRA particularly advantageous for providing a numerical value for tear lipid layer thickness which enhances the convenience of dry eye diagnosis and treatment.