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: 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

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.

Objective: To review the implementation of essential reproductive health services in Eastern Visayas, Philippines.Materials and Methods: We reviewed four national policies through a qualitative research design using a series of key informant interviews conducted with service providers and focus group discussions with service beneficiaries.Results: There was a gap between the policies and the implementation of reproductive health services in the Eastern Visayas region. This gap is mainly due to the refusal of service providers to cater to teenagers’ needs regarding reproductive health services. This has resulted in teenagers hesitating to seek reproductive health services and related support from primary healthcare facilities. Service beneficiaries have also reported on the unavailability of several reproductive health services in primary healthcare facilities.Conclusion: The gap between national policies and program implementation must be bridged. This can be achieved by creating culturally-specific policies that can improve the implementation of reproductive health programs in the study areas.

Objective: SHAPE (Simple Hysterectomy And PElvic node assessment) was an international phase III trial demonstrating that simple hysterectomy was non-inferior to radical hysterectomy for pelvic recurrence risk, but superior for quality of life and sexual health.The objective was to conduct a cost-effectiveness analysis comparing simple vs. radical hysterectomy for low-risk early-stage cervical cancer. Methods: Markov model compared the costs and benefits of simple vs. radical hysterectomy for early cervical cancer over a 5-year time horizon. Quality-adjusted life years (QALYs) were estimated from health utilities derived from EQ-5D-3L surveys. Sensitivity analyses accounted for uncertainty around key parameters. Monte Carlo simulation estimated complication numbers according to surgical procedure. Results: Simple hysterectomy was more effective and less costly than radical hysterectomy. Average overall costs were $11,022 and $12,533, and average gains were 3.56 and 3.54 QALYs for simple and radical hysterectomy, respectively. Baseline health utility scores were 0.81 and 0.83 for simple and radical hysterectomy, respectively. By year 3, these scores improved for simple hysterectomy (0.82) but not for radical hysterectomy (0.82). Assuming 800 early cervical cancer patients annually in Canada, the model estimated 3 vs. 82 patients with urinary retention, and 49 vs. 86 patients with urinary incontinence persisting 4 weeks after simple vs.radical hysterectomy, respectively. Results were most sensitive to variability in health utilities after surgery, but stable through wide ranges of costs and recurrence estimates. Conclusion Simple hysterectomy is less costly and more effective in terms of quality-adjusted life expectancy compared to radical hysterectomy for early cervical cancer.