When given a sample of 100 emergency department discharge instructions, Claude Sonnet, a large language model, produced accurate Spanish translations as evaluated by Spanish-speaking physicians and medical interpreters.

Background: Background. Biomedical research studies are increasingly using digital to enroll, recruit and collect data from participants. However, variability in digital literacy and technological acceptance can be challenging for recruitment from groups traditionally underrepresented in research, including those served by Federally Qualified Health Centers. Objective: Objective. The objectives of this study were to (1) measure participant accessibility and comfort with digital platforms, and (2) examine the interrelation of technology access, digital literacy, and support preferences during enrollment and data submission. Methods: Methods. A cross-sectional analysis was conducted using enrollment data from a FQHC participating in the All of Us Research Program (AoURP). Participants had the option of High-Touch (staff-assisted) or Low-Touch (self-directed) support for enrollment and survey completion. Survey items assessed internet access and technology comfort, while support type was recorded by the research staff based on participants’ actual selection. Logistic regression models evaluated relationships between technology access, comfort, and enacted support type, while controlling for age, consent language, education, as well as race/ethnicity. Results: Results. The analytic sample included 605 participants. The majority reported access to the internet (89.1%) and felt comfortable with technology (74.1%). In the group requesting High-Touch support (n = 346), 14.5% reported no internet access, and 31.5% felt uncomfortable with technology. In the group requesting Low-Touch support (n = 259), 6.2% had no access to the internet, and 3.9% reported feeling uncomfortable (p < 0.001). In the adjusted models, much greater comfort with technology was significantly correlated with reduced odds requesting High-Touch support (comfortable: AOR = 0.118, 95% CI: 0.055–0.255; neutral: AOR = 0.212, 95% CI: 0.077–0.587), but internet access was not significantly correlated. Conclusions: Conclusions The strongest predictor for support preference for digital enrollment among the participants was their comfort with technology rather than access alone. These findings illustrate the significance of participant-centric design methods coupling adaptive support paths, mixed enrollment strategies, and individualized onboarding methods aligned with digital confidence to promote equitable engagement in precision health research.

Background: Lower- and middle-income countries bear a disproportionate share of the global burden of adverse events in health care. Despite this, patient safety research is predominantly conducted in high-income countries with well-developed health care systems, resulting in evidence and methodologies that have limited applicability in resource-constrained settings. Objective: This pilot study primarily aimed to identify the most suitable methodology for a full-scale study to detect inpatient adverse events at a tertiary care hospital in a lower- to middle-income country. Second, we aimed to use our experience with this study to further adapt the selected methodology to our setting. Methods: This external pilot study used a 2-step retrospective chart review methodology. Two separate screening tools, tools 1A and 1B, were compared to identify which performed better in our setting. We reviewed the medical records of patients who were discharged between January 1, 2019, and December 31, 2019, from a tertiary care hospital in a lower- to middle-income country in South Asia. The main outcome of interest was the rate of adverse events among hospitalized patients reported as the total number of adverse events experienced per 100 admissions. Results: A total of 100 medical records were screened using tool 1A, with the mean patient age being 39.2 (SD 27.7) years and the mean length of stay being 3.3 (SD 2.8) days. Only 1 adverse event was identified using tool 1A, resulting in an adverse event rate of 1 event per 100 hospital admissions. Tool 1B was also used to screen a total of 100 medical records. The mean patient age was found to be 39.8 (SD 28.4) years, with the mean length of stay being 3.5 (SD 3.4) days. A total of 30 adverse events were identified across 22 patient files, with 18 (60%) considered preventable, resulting in an adverse event rate of 30 events per 100 hospital admissions. Conclusions: This study demonstrates that tool 1B, adapted from the Global Trigger Tool for Measuring Adverse Events, represents an appropriate and sensitive methodology to identify adverse events among hospitalized patients in a lower- to middle-income country. Furthermore, the findings and experiences of this study were used to improve the design and procedures of our research methodology before implementation in a full-scale study.

Mildmay’s dietetics team and student interns shone at the 2025 BDA Research Symposium, presenting six abstracts on food waste, allergy safety, and nutrition in vulnerable groups. Their work shows how ...

Pancreatic Cancer Market projected to grow at 4.66% CAGR, reaching USD 8.7 Billion by 2035, driving growth global trends, competitive industry analysis and outlook 2025-2035.

Laboratory Equipment Market Trends and Insights By Product Type (Incubators, Centrifuges, Spectrometers, Scopes, Sonicators, Autoclaves, Others),...