LogDoctor: an open and decentralized worker-centered solution for occupational management in healthcare

Occupational stress among health workers is a pervasive issue that affects individual well-being, patient care quality, and healthcare systems' sustainability. Current time-tracking solutions are mostly employer-driven, neglecting the unique requirements of health workers. In turn, we propose an open and decentralized worker-centered solution that leverages machine intelligence for occupational health and safety monitoring. Its robust technological stack, including blockchain technology and machine learning, ensures compliance with legal frameworks for data protection and working time regulations, while a decentralized autonomous organization bolsters distributed governance. To tackle implementation challenges, we employ a scalable, interoperable, and modular architecture while engaging diverse stakeholders through open beta testing and pilot programs. By bridging an unaddressed technological gap in healthcare, this approach offers a unique opportunity to incentivize user adoption and align stakeholders' interests. We aim to empower health workers to take control of their time, valorize their work, and safeguard their health while enhancing the care of their patients

Evaluation of point-of-care ultrasound use in the diagnostic approach for right upper quadrant abdominal pain management in the emergency department: a prospective study

Point-of-care ultrasound (PoCUS) is commonly used at the bedside in the emergency department (ED) as part of clinical examinations. Studies frequently investigate PoCUS diagnostic accuracy, although its contribution to the overall diagnostic approach is less often evaluated. The primary objective of this prospective, multicenter, cohort study was to assess the contribution of PoCUS to the overall diagnostic approach of patients with right upper quadrant abdominal pain. Two independent members of an adjudication committee, who were blind to the intervention, independently evaluated the diagnostic approaches before and after PoCUS for the same patient. The study included 62 patients admitted to the ED with non-traumatic right upper quadrant abdominal pain from September 1, 2022, to March 6, 2023. The contribution of PoCUS to the diagnostic approach was evaluated using a proportion test assuming that 75% of diagnostic approaches would be better or comparable with PoCUS. Wilcoxon signed-rank tests evaluated the impact of PoCUS on the mean number of differential diagnoses, planned treatments, and complementary diagnostic tests. Overall, 60 (97%) diagnostic approaches were comparable or better with PoCUS (χ 2 = 15.9, p < 0.01). With PoCUS, the mean number of differential diagnoses significantly decreased by 2.3 (95% CI – 2.7 to – 1.5) (p < 0.01), proposed treatments by 1.3 (95% CI – 1.8 to – 0.9) (p < 0.01), and complementary diagnostic tests by 1.3 (95% CI – 1.7 to – 1.0) (p < 0.01). These findings show that PoCUS positively impacts the diagnostic approach and significantly decreases the mean number of differential diagnoses, treatments, and complementary tests

Evaluation of point-of-care ultrasound use in the diagnostic approach for right upper quadrant abdominal pain management in the emergency department: a prospective study.

Point-of-care ultrasound (PoCUS) is commonly used at the bedside in the emergency department (ED) as part of clinical examinations. Studies frequently investigate PoCUS diagnostic accuracy, although its contribution to the overall diagnostic approach is less often evaluated. The primary objective of this prospective, multicenter, cohort study was to assess the contribution of PoCUS to the overall diagnostic approach of patients with right upper quadrant abdominal pain. Two independent members of an adjudication committee, who were blind to the intervention, independently evaluated the diagnostic approaches before and after PoCUS for the same patient. The study included 62 patients admitted to the ED with non-traumatic right upper quadrant abdominal pain from September 1, 2022, to March 6, 2023. The contribution of PoCUS to the diagnostic approach was evaluated using a proportion test assuming that 75% of diagnostic approaches would be better or comparable with PoCUS. Wilcoxon signed-rank tests evaluated the impact of PoCUS on the mean number of differential diagnoses, planned treatments, and complementary diagnostic tests. Overall, 60 (97%) diagnostic approaches were comparable or better with PoCUS (χ = 15.9, p < 0.01). With PoCUS, the mean number of differential diagnoses significantly decreased by 2.3 (95% CI - 2.7 to - 1.5) (p < 0.01), proposed treatments by 1.3 (95% CI - 1.8 to - 0.9) (p < 0.01), and complementary diagnostic tests by 1.3 (95% CI - 1.7 to - 1.0) (p < 0.01). These findings show that PoCUS positively impacts the diagnostic approach and significantly decreases the mean number of differential diagnoses, treatments, and complementary tests

Neura: a specialized large language model solution in neurology

Large language models’ (LLM) ability in natural language processing holds promise for diverse applications, yet their deployment in fields such as neurology faces domain-specific challenges. Hence, we introduce Neura: a scalable, explainable solution to specialize LLM. Blindly evaluated on a select set of five complex clinical cases compared to a cohort of 13 neurologists, Neura achieved normalized scores of 86.17% overall, 85% for differential diagnoses, and 88.24% for final diagnoses (55.11%, 46.15%, and 70.93% for neurologists) with rapid response times of 28.8 and 19 seconds (9 minutes and 37.2 seconds and 8 minutes and 51 seconds for neurologists) while consistently providing relevant, accurately cited information. These findings support the emerging role of LLM-driven applications to articulate human-acquired and integrated data with a vast corpus of knowledge, augmenting human experiential reasoning for clinical and research purposes