Context:Traditionally, intensive care unit (ICU) discharge has followed a tiered approach of managing cases, i.e., patients are transferred to a general or parent surgical ward or high-dependency unit before being discharged to home. This practice assumes that some additional time is mandatory to observe patients who are recovering from the illness. With latest improvisations in postdischarge services for direct discharge to home (DDH), the balance is shifting in favor of DDH. Aims:To compare the clinical outcomes and safety of ICU patients discharged directly to home with those transferred to hospital wards before being discharged. Settings and Design:Prospective cohort study done at a tertiary care hospital. Methods:According to patient's discharge or transfer to ward (TTW), they were grouped in either of the two: Group D: Direct discharge to home (DDH), Group W: TTW (minimum stay in ward 24 h). All patients were followed up to 3 months of discharge/transfer. Statistical Analysis Used:Continuous data of two independent groups were compared by independent Student's t-test, whereas categorical data by Chi-square (χ2) test were used. A two-tailed (α =2) P < 0.05 was considered statistically significant. Results:The results showed that ICU patient discharged directly to home had better post-ICU recovery for 90 days' follow-up. Conclusions:ICU patients discharged directly to home have significantly better clinical outcomes and safety, when applied to carefully selected ICU patients compared to those transferred to hospital wards before being discharged.
Background:Upper gastrointestinal bleeding (UGIB) remains a common medical emergency associated with significant morbidity and mortality. Early risk stratification is essential to identify patients who require urgent intervention. The Glasgow-Blatchford Score (GBS) is widely used to assess UGIB severity at presentation; however, data regarding its performance in the Indian population are limited. This study aimed to evaluate the ability of the GBS to predict the need for blood transfusion and early endoscopy among patients presenting with UGIB. Methods:A retrospective observational study was conducted at a tertiary care hospital in South India over a period of 2.5 years. Medical records of adult patients (18-80 years) presenting to the emergency department with clinical features of UGIB were reviewed. Demographic, clinical, and laboratory parameters required for calculating the GBS were collected. Statistical analysis included independent t-tests and Chi-square tests to evaluate associations between GBS and clinical outcomes. Receiver operating characteristic (ROC) curve analysis was performed to determine optimal cutoff values for predicting blood transfusion and early endoscopy. Results:A total of 100 patients were included, with a mean age of 59 ± 12 years, and 78% were male. Chronic liver disease was present in 61% of the cohort. Patients requiring blood transfusion had significantly higher GBS values compared with those who did not (P = 0.006). ROC analysis showed moderate predictive ability for blood transfusion (area under the curve [AUC] 67.8%) with an optimal cutoff score of 8 (sensitivity 86% and specificity 46%). For early endoscopy, the AUC was 71.8%, with a cutoff score of 9 (sensitivity: 80.6% and specificity: 54%). Conclusion:The GBS demonstrated significant predictive value for identifying patients with UGIB who are likely to require blood transfusion and early endoscopic evaluation. Incorporation of GBS into emergency department triage protocols may facilitate timely risk stratification and support evidence-based management of UGIB.
Background:Generative artificial intelligence (AI) tools are increasingly used by healthcare professionals, yet empirical data on adoption among Latin American emergency medicine clinicians remain limited. Methods:A voluntary, anonymous, cross-sectional online survey was administered to emergency medicine professionals across Latin America (April-October 2025). The Spanish-language instrument, pilot-tested before deployment, assessed AI familiarity, use patterns, perceived value, risks, governance, and training needs. Descriptive statistics and Chi-square tests were used for analysis. Results:A total of 123 respondents from 16 countries were analyzed. Most practiced in public or mixed healthcare settings (71.3%) and had over 10 years of experience (61.3%). Familiarity with generative AI was moderate to high (76.9%), and professional use was common (78.3%), with ChatGPT the dominant platform (81.0%). Adoption patterns were homogeneous across countries, with no significant associations between country of origin and familiarity, frequency of use, or platform preference. Primary applications included research and literature review (66.1%), medical education (59.1%), and clinical documentation (40.9%). Nearly half reported high perceived utility (49.6%), and 51.7% noted improvements in productivity. Major concerns included misinformation, erosion of clinical judgment (56.2% each), algorithmic bias (53.7%), and legal liability (46.3%). Institutional AI governance was nearly absent, with only 7.4% reporting existing policies. Despite this, 88.4% expressed interest in formal AI training and 65.6% supported integration into clinical guidelines. Conclusions:Latin American emergency clinicians are rapidly adopting generative AI with perceived benefits across multiple domains, but almost entirely without governance frameworks or structured training, highlighting an urgent need for regional policy and education infrastructure.
