In the era of precision medicine, pharmacogenetics has substantial potential for addressing inter-individual variability in drug responses. Although pharmacogenetics has been a research focus for many years, resulting in the establishment of several formal guidelines, its clinical implementation remains limited to several gene–drug combinations in most countries, including Korea. The main causes of delayed implementation are technical challenges in genotyping and knowledge gaps among healthcare providers; therefore, clinical laboratories play a critical role in the timely implementation of pharmacogenetics. This paper presents an update of the Clinical Pharmacogenetic Testing and Application guidelines issued by the Korean Society for Laboratory Medicine and aims to provide the necessary information for clinical laboratories planning to implement or expand their pharmacogenetic testing. Current knowledge regarding nomenclature, gene–drug relationships, genotyping technologies, testing strategies, methods for clinically relevant information delivery, QC, and reimbursements has been curated and described in this guideline.

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: 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.

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.

Objective: To evaluate the current body of literature pertaining to the use of ocular point-of-care ultrasound (POCUS) in the emergency department (ED). Methods: A comprehensive literature search was conducted on Scopus, Web of Science, MEDLINE, and Cochrane Central Register of Controlled Trials (CENTRAL) databases. Inclusion criteria were studies written in English and primary clinical studies involving ocular POCUS scans in an ED setting. Exclusion criteria were nonprimary studies (e.g., reviews or case reports), studies written in a non-English language, nonhuman studies, studies performed in a nonemergency setting, studies involving non-POCUS ocular ultrasound modalities, or studies published more than 10 years prior. Data extraction was guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. Results: The initial search yielded 391 results with 153 duplicates. Of the remaining 238 studies selected for retrieval and screening, 24 met the inclusion criteria. These 24 included studies encompassed 2,448 patients across prospective, retrospective, cross-sectional, and case series study designs. The majority of included studies focused on the use of POCUS in the ED to measure optic nerve sheath diameter as a proxy for papilledema and metabolic aberrations, while a minority of studies used ocular POCUS to assist in the diagnosis of orbital fractures or posterior segment pathology. Conclusion The vast majority of studies investigating the use of ocular POCUS in recent years emphasize its utility in measuring optic nerve sheath diameter and fluctuations in intracranial pressure, though additional outcomes of interest include pathology of the posterior segment, orbit, and globe.

BACKGROUND

Students’ perceptions of their educational environment have been determined to influence their educational performance, behavior, and well-being. Assessing the students’ educational environment enables stakeholders to recognize limitations and opportunities for ensuring optimal learning experiences. However, no studies have yet explored the perceived educational environment of occupational therapy (OT) students in the Philippines.

Using the Dundee Ready Education Environment Measure (DREEM) instrument, this study assesses how occupational therapy students perceive their educational environment. Additionally, this study aims to identify strengths and weaknesses in the current educational environment.

This descriptive cross-sectional study utilized a records review of anonymized secondary responses from the Student Life Survey 2021. The DREEM questionnaire was employed to evaluate the perspectives of occupational therapy students in five domains, namely: students’ perception of learning, students’ perceptions of academic self-perceptions, students’ perceptions of atmosphere, and students’ social self-perceptions. Individual item and subscale scores were summarized as mean and standard deviation (SD).

With a total mean score of 124.12 ± 26.79 in the five domains, the OT students positively perceive their educational environment. Data analysis revealed the scores for the DREEM domains of Students' Perception of Teachers (30.50 ± 6.08), Students' Perception of Learning (31.11 ± 5.94), Students' Academic Self-Perceptions (20.37 ± 4.26), Students’ Social Self-Perceptions (15.58 ± 4.00), and Students’ Perceptions of Atmosphere (26.56 ± 6.51). Interpretation for each domain indicates that the educational environment is moving in the right direction, has a more positive approach, is feeling more on the positive side, is not too bad, and has a more positive atmosphere, respectively.

The study revealed a positive perception of the educational environment among OT students. However, there needed improvement in the areas of authoritarian teachers, the demanding program environment, and the overemphasis on factual learning. To guarantee that OT education in the country is of high quality, these findings can be utilized to inform the institution’s stakeholders regarding policy development, curriculum review, and future OT program development and implementation.