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Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients’ health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED. Because predictive modeling represents a substantial change from current approaches, in this study we explored the acceptability of HRSN predictive modeling in the ED.Methods: Emergency clinicians, ED staff, and patient perspectives on the acceptability and usage of predictive modeling for HRSNs in the ED were obtained through in-depth semi-structured interviews (eight per group, total 24). All participants practiced at or had received care from an urban, Midwest, safety-net hospital system. We analyzed interview transcripts using a modified thematic analysis approach with consensus coding.Results: Emergency clinicians, ED staff, and patients agreed that HRSN predictive modeling must lead to actionable responses and positive patient outcomes. Opinions about using predictive modeling results to initiate automatic referrals to HRSN services were mixed. Emergency clinicians and staff wanted transparency on data inputs and usage, demanded high performance, and expressed concern for unforeseen consequences. While accepting, patients were concerned that prediction models can miss individuals who required services and might perpetuate biases.Conclusion: Emergency clinicians, ED staff, and patients expressed mostly positive views about using predictive modeling for HRSNs. Yet, clinicians, staff, and patients listed several contingent factors impacting the acceptance and implementation of HRSN prediction models in the ED.

Abstract Background Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system. Methods We used user-centered design methods to integrate the ML algorithm into the EHR system. The backend and UI design sub-teams collaborated closely, both informed by user feedback sessions. We conducted seven user feedback sessions with five UF Health primary care physicians (PCPs) to explore aspects of CDS tools, including workflow, risk display, and risk mitigation strategies. After customizing the tool based on PCPs’ feedback, we held two rounds of one-on-one usability testing sessions with 8 additional PCPs to gather feedback on prototype alerts. These sessions informed iterative UI design and backend processes, including alert frequency and reappearance circumstances. Results The backend process development identified needs and requirements from our team, information technology, UFHealth, and PCPs. Thirteen PCPs (male = 62%, White = 85%) participated across 7 user feedback sessions and 8 usability testing sessions. During the user feedback sessions, PCPs (n = 5) identified flaws such as the term “high risk” of overdose potentially leading to unintended consequences (e.g., immediate addiction services referrals), offered suggestions, and expressed trust in the tool. In the first usability testing session, PCPs (n = 4) emphasized the need for natural risk presentation (e.g., 1 in 200) and suggested displaying the alert multiple times yearly for at-risk patients. Another 4 PCPs in the second usability testing session valued the UFHealth-specific alert for managing new or unfamiliar patients, expressed concerns about PCPs’ workload when prescribing to high-risk patients, and recommended incorporating the details page into training sessions to enhance usability. Conclusions The final backend process for our CDS alert aligns with PCP needs and UFHealth standards. Integrating feedback from PCPs in the early development phase of our ML-driven CDS tool helped identify barriers and facilitators in the CDS integration process. This collaborative approach yielded a refined prototype aimed at minimizing unintended consequences and enhancing usability.

Abstract Background The EORTC Quality of Life Group has developed a questionnaire to evaluate cancer patients’ perception of their communication with healthcare professionals (HCPs): the EORTC QLQ-COMU26. In this study we test the validity and reliability of this novel measure in an international and culturally diverse sample of cancer patients. Methods Cancer patients completed the following EORTC questionnaires at two time points (before and during treatment): the QLQ-COMU26 (including a debriefing questionnaire), the QLQ-C30, and specific IN-PATSAT32 scales. These data were used to assess: the cross-cultural applicability, acceptability, scale structure, reliability, convergent/divergent validity, known-groups validity, and responsiveness to change of the QLQ-COMU26. Results Data were collected from 498 patients with various cancer diagnoses in 10 European countries, Japan, Jordan and India (overall 5 cultural regions). At most, only 3% of patients identified an item as confusing and 0.6% as upsetting, which indicates that the questionnaire was clear and did not trigger negative emotional responses. Confirmatory factor analysis and multi-trait scaling confirmed the hypothesised QLQ-COMU26 scale structure comprising six multi-item scales and four single items (RMSEA = 0.025). Reliability was good for all scales (internal consistency > 0.70; test–retest reliability > 0.85). Convergent validity was supported by correlations of ≥ 0.50 with related scales of the IN-PATSAT32 and correlations

The creation of undesirable (soggy) sauerkraut resulted in the loss of $1,000,000 worth of organic sauerkraut in 2022, which prompted a multistep investigation of the cause and potential solution. The cause of this condition has been previously reported as unique fermentation conditions and the lack of key trace nutrients essential for cabbage (Brassica oleracea var. capitata) cell wall integrity. Because the condition was limited to organic sauerkraut in 2022, this investigation initially focused on differences in fermentation conditions between organic and conventional sauerkraut. No differences in fermentation conditions accounted for the condition; therefore, attention was focused on analyzing the mineral content of cabbage grown for sauerkraut production that pinpointed a deficiency in critical micronutrients such as iron, copper, manganese, boron, and zinc. This deficiency was traced to the use of poultry manure that was contaminated with glyphosate residue from conventionally fed turkeys and chickens that consumed genetically engineered (GE) feed and used as the fertilizer for organic cabbage production. The presence of glyphosate, a potent mineral chelator and antibiotic, was identified as a significant factor that impairs the absorption and physiological function of essential minerals in the shikimate metabolic pathway whereby cell walls and lignin are produced, thus compromising the structural quality of the sauerkraut. After this discovery, the study progressed to evaluate various remediation strategies aimed at eliminating glyphosate from the soil and restoring nutrient uptake. Corn grain and silage were selected as the test crops for this phase. Among the tested remediation solutions were raw sauerkraut juice containing Lactobacillus plantarum, which is reported to degrade glyphosate in the rumen of dairy cows and two patented proprietary microbial mixtures, PB027 and PB027SK, that degrade glyphosate by all three of the known metabolic pathways. These treatments were specifically formulated to degrade residual glyphosate in the soil. The results showed that these interventions could reduce soil glyphosate levels by 80% to 90% within 6 to 7 months to significantly enhance both the yield and quality of corn grain and silage. The increase in corn grain yield from glyphosate degradation on the Shiocton silt loam soil was 907.89 kg·ha−1 (13.5 bushels/acre). The increase in yield on the irrigated Kidder sandy loam soil was quantified at 726.31 kg·ha−1 (10.8 bushels/acre) for corn grain and 6.62 t·ha−1 (2.68 t/acre) for silage, with an additional improvement in silage feed quality beneficial for milk production. The findings underscore the importance of addressing both micronutrient sufficiency and glyphosate residue in soil to ensure the optimal growth of cabbage and the quality of sauerkraut produced. By successfully identifying manure as a subtle source of nutrient immobilization and implementing effective soil remediation techniques, this research highlights a clear path forward for improving crop yield and quality to ultimately enhance the structural integrity and consumer acceptance of sauerkraut. This study has broader applications for the nutritional content and crop yields of many organic crops that use conventional poultry and animal manures that may contain glyphosate in desiccated plant tissues or GE feeding operations.

AbstractSulfur nanoparticles were synthesized from hazardous H2S gas using novel biodegradable iron chelates in w/o microemulsion system. Fe3+–malic acid chelate (0.05 M aqueous solution) was studied in w/o microemulsion containing cyclohexane, Triton X-100 andn-hexanol as oil phase, surfactant, co-surfactant, respectively, for catalytic oxidation of H2S gas at ambient conditions of temperature, pressure, and neutral pH. The structural features of sulfur nanoparticles have been characterized by X-ray diffraction (XRD), transmission electron microscope (TEM), energy dispersive spectroscopy (EDS), diffused reflectance infra-red Fourier transform technique, and BET surface area measurements. XRD analysis indicates the presence of α-sulfur. TEM analysis shows that the morphology of sulfur nanoparticles synthesized in w/o microemulsion system is nearly uniform in size (average particle size 10 nm) and narrow particle size distribution (in range of 5–15 nm) as compared to that in aqueous surfactant systems. The EDS analysis indicated high purity of sulfur (>99%). Moreover, sulfur nanoparticles synthesized in w/o microemulsion system exhibit higher antimicrobial activity (against bacteria, yeast, and fungi) than that of colloidal sulfur.

Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients’ health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED. Because predictive modeling represents a substantial change from current approaches, in this study we explored the acceptability of HRSN predictive modeling in the ED. Methods: Emergency clinicians, ED staff, and patient perspectives on the acceptability and usage of predictive modeling for HRSNs in the ED were obtained through in-depth semi-structured interviews (eight per group, total 24). All participants practiced at or had received care from an urban, Midwest, safety-net hospital system. We analyzed interview transcripts using a modified thematic analysis approach with consensus coding. Results: Emergency clinicians, ED staff, and patients agreed that HRSN predictive modeling must lead to actionable responses and positive patient outcomes. Opinions about using predictive modeling results to initiate automatic referrals to HRSN services were mixed. Emergency clinicians and staff wanted transparency on data inputs and usage, demanded high performance, and expressed concern for unforeseen consequences. While accepting, patients were concerned that prediction models can miss individuals who required services and might perpetuate biases. Conclusion: Emergency clinicians, ED staff, and patients expressed mostly positive views about using predictive modeling for HRSNs. Yet, clinicians, staff, and patients listed several contingent factors impacting the acceptance and implementation of HRSN prediction models in the ED.

We present an analysis of the evolution of near-bed oxygen in the next century in the northwest European continental shelf in a three-member ensemble of coupled physics–biogeochemistry models. The comparison between model results helps highlight the biogeochemical mechanisms responsible for the observed deoxygenation trends and their response to climate drivers. While all models predict a decrease in near-bed oxygen proportional to climate change intensity, the response is spatially heterogeneous, with hotspots of oxygen decline (up to −1 mg L−1) developing along the Norwegian trench in the members with the most intense change, as well as areas where compensating mechanisms mitigate change. We separate the components of oxygen change associated with the warming effect on oxygen solubility from those due to the effects of changes in transport and biological processes. We find that while warming is responsible for a mostly uniform decline throughout the shelf (−0.30 mg L−1 averaged across ensemble members), changes in transport and biological processes account for the detected heterogeneity. Hotspots of deoxygenation are associated with enhanced stratification that greatly reduces vertical transport. A major change in circulation in the North Sea is responsible for the onset of one such hotspot that develops along the Norwegian trench and adjacent areas in the members characterised by intense climate change. Conversely, relatively shallow and well-mixed coastal areas like the southern North Sea, Irish Sea and English Channel experience an increase in net primary production that partially mitigates oxygen decline in all members. This work represents the first multi-model comparison addressing deoxygenation in the northwest European shelf and contributes to characterising the possible trajectories of near-bed oxygen and the processes that drive deoxygenation in this region. As our downscaled members factor in riverine inputs and small- and medium-scale circulation, which are not usually well represented in earth system models, results are relevant for the understanding of deoxygenation in coastal and shelf systems.

Abstract The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status. We trained 6 machine learning models to classify infant feeding status: logistic regression, random forest, XGBoost gradient descent, k-nearest neighbors, and support-vector classifier. Model comparison was evaluated based on overall accuracy, precision, recall, and F1 score. Our modeling corpus included an even number of clinical notes that was a balanced sample across each class. We manually reviewed 999 notes that represented 746 mother-infant dyads with a mean gestational age of 38.9 weeks and a mean maternal age of 26.6 years. The most frequent feeding status classification present for this study was exclusive breastfeeding [n = 183 (18.3%)], followed by exclusive formula bottle feeding [n = 146 (14.6%)], and exclusive feeding of expressed mother’s milk [n = 102 (10.2%)], with mixed feeding being the least frequent [n = 23 (2.3%)]. Our final analysis evaluated the classification of clinical notes as breast, formula/bottle, and missing. The machine learning models were trained on these three classes after performing balancing and down sampling. The XGBoost model outperformed all others by achieving an accuracy of 90.1%, a macro-averaged precision of 90.3%, a macro-averaged recall of 90.1%, and a macro-averaged F1 score of 90.1%. Our results demonstrate that natural language processing can be applied to clinical notes stored in the electronic health records to classify infant feeding status. Early identification of breastfeeding status using NLP on unstructured electronic health records data can be used to inform precision public health interventions focused on improving lactation support for postpartum patients.

Introduction Reduced thrombin generation is an important component of post cardiopulmonary bypass (CPB) coagulopathy. To replenish coagulation factors and enhance thrombin generation in bleeding surgical patients, frozen plasma (FP) and four-factor prothrombin complex concentrate (4F-PCC) are used. However, the efficacy–safety balance of 4F-PCC relative to FP in cardiac surgery is unconfirmed.Methods and analysis LEX-211 (FARES-II) is an active-control, randomised, phase 3 study comparing two coagulation factor replacement therapies in bleeding adult cardiac surgical patients at 12 hospitals in Canada and the USA. The primary objective is to determine whether 4F-PCC (Octaplex/Balfaxar, Octapharma) is clinically non-inferior to FP for haemostatic effectiveness. Inclusion criteria are any index (elective or non-elective) cardiac surgery employing CPB and coagulation factor replacement with 4F-PCC or FP ordered in the operating room for bleeding management. Patients will be randomised to receive 1500 or 2000 international units of 4F-PCC or 3 or 4 units of FP, depending on body weight. The primary endpoint of haemostatic treatment response is ‘effective’ if no additional haemostatic intervention is required from 60 min to 24 hours after the first initiation of 4F-PCC or FP; or ‘ineffective’ if any other haemostatic intervention (including a second dose of study drug) is required. An estimated 410 evaluable patients will be required to demonstrate non-inferiority (one-sided α of 0.025, power ≥90%, non-inferiority margin 0.10). Secondary outcomes include transfusions, bleeding-related clinical endpoints, coagulation parameters and safety.Ethics and dissemination The trial has been approved by the institutional review boards of all participating centres. Trial completion is anticipated at the end of 2024, and results will be disseminated via publications in peer-reviewed journals and conference presentations in 2025. The results will advance our understanding of coagulation management in bleeding surgical patients, potentially reducing the need for allogeneic blood products and improving outcomes in surgical patients.Trial registration number NCT05523297.

ObjectiveThis study aimed to examine changes in child emergency department (ED) discharges and hospitalizations for primary general medical (GM) and primary psychiatric disorders; prevalence of psychiatric disorders among acute care encounters; and change in acute mental health (MH) care encounters by disorder type and, within these categories, by child sociodemographic characteristics before and after statewide COVID-19–related school closure orders.MethodsThis retrospective, cross-sectional cohort study used the Pediatric Health Information System database to assess percent changes in ED discharges and hospitalizations (N=2,658,474 total encounters) among children ages 3–17 years in 44 U.S. children’s hospitals in 2020 compared with 2019, by using matched data for 36- and 12-calendar-week intervals.ResultsDecline in MH ED discharges accounted for about half of the decline in ED discharges and hospitalizations for primary GM disorders (−24.8% vs. −49.1%), and MH hospitalizations declined 3.4 times less (−8.0% vs. −26.8%) in 2020. Suicide attempt or self-injury and depressive disorders accounted for >50% of acute MH care encounters before and after the statewide school closures. The increase in both ED discharges and hospitalizations for suicide attempt or self-injury was 5.1 percentage points (p

Abstract Background Patient health-related social needs (HRSN) complicate care and drive poor outcomes in emergency department (ED) settings. This study sought to understand what HRSN information is available to ED physicians and staff, and how HRSN-related clinical actions may or may not align with patient expectations. Methods We conducted a qualitative study using in-depth semi-structured interviews guided by HRSN literature, the 5 Rights of Clinical Decision Support (CDS) framework, and the Contextual Information Model. We asked ED providers, ED staff, and ED patients from one health system in the mid-Western United Stated about HRSN information availability during an ED encounter, HRSN data collection, and HRSN data use. Interviews were recorded, transcribed, and analyzed using modified thematic approach. Results We conducted 24 interviews (8 per group: ED providers, ED staff, and ED patients) from December 2022 to May 2023. We identified three themes: (1) Availability: ED providers and staff reported that HRSNs information is inconsistently available. The availability of HRSN data is influenced by patient willingness to disclose it during an encounter. (2) Collection: ED providers and staff preferred and predominantly utilized direct conversation with patients to collect HRSNs, despite other methods being available to them (e.g., chart review, screening questionnaires). Patients’ disclosure preferences were based on modality and team member. (3) Use: Patients wanted to be connected to relevant resources to address their HRSNs. Providers and staff altered clinical care to account for or accommodate HRSNs. System-level challenges (e.g., limited resources) limited provider and staff ability to address patients HRSNs. Conclusions In the ED, HRSNs information was inconsistently available, collected, or disclosed. Patients and ED providers and staff differed in their perspectives on how HSRNs should be collected and acted upon. Accounting for such difference in clinical and administrative decisions will be critical for patient acceptance and effective usage of HSRN information.