Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT).

Weekend discharges occur less frequently than discharges on weekdays, contributing to hospital congestion. Artificial intelligence algorithms have previously been derived to predict which patients are nearing discharge based upon ward round notes. In this implementation study, such an artificial intelligence algorithm was coupled with a multidisciplinary discharge facilitation team on weekend shifts. This approach was implemented in a tertiary hospital, and then compared to a historical cohort from the same time the previous year. There were 3990 patients included in the study. There was a significant increase in the proportion of inpatients who received weekend discharges in the intervention group compared to the control group (median 18%, IQR 18-20%, vs median 14%, IQR 12% to 17%, P = 0.031). There was a corresponding higher absolute number of weekend discharges during the intervention period compared to the control period (P = 0.025). The studied intervention was associated with an increase in weekend discharges and economic analyses support this approach as being cost-effective. Further studies are required to examine the generalizability of this approach to other centers.

Formic Acid Decomposition Using Palladium-Zinc Preformed Colloidal Nanoparticles Supported on Carbon Nanofibre in Batch and Continuous Flow Reactors: Experimental and Computational Fluid Dynamics Modelling Studies.

The need to replace conventional fuels with renewable sources is a great challenge for the science community. H2 is a promising alternative due to its high energy density and availability. H2 generation from formic acid (FA) decomposition occurred in a batch and a packed-bed flow reactor, in mild conditions, using a 2% Pd6Zn4/HHT (high heated treated) catalyst synthesised via the sol-immobilisation method. Experimental and theoretical studies took place, and the results showed that in the batch system, the conversion was enhanced with increasing reaction temperature, while in the continuous flow system, the conversion was found to decrease due to the deactivation of the catalyst resulting from the generation of the poisoning CO. Computational fluid dynamics (CFD) studies were developed to predict the conversion profiles, which demonstrated great validation with the experimental results. The model can accurately predict the decomposition of FA as well as the deactivation that occurs in the continuous flow system. Of significance was the performance of the packed-bed flow reactor, which showed improved FA conversion in comparison to the batch reactor, potentially leading to the utilisation of continuous flow systems for future fuel cell applications for on-site H2 production.

Bed flow priorities and the spatial and temporal dimensions of rural older adult care.

Despite prior research that examines the spatial and temporal dimensions of older adult care, there is disparate research on the influence of patient flow priorities on older adult care over time and place. Drawing on a qualitative case study of rural older adult transitions in care in the Canadian context we examine how patient flow prioritization undervalues older patients' needs and the local contexts in which care is provided. Certainly, accounting for the spatial and temporal dimensions of older adult care has broader implications that will enhance future research, policy and practice. Policy makers, researchers and clinicians may then use these recommendations as a stepping stone to align the health care system with the older populations that they serve.

A dual grafted fluorinated hydrocarbon amine weak anion exchange resin polymer for adsorption of perfluorooctanoic acid from water.

Perfluorooctanoic acid (PFOA) is a persistent and recalcitrant organic contaminant of exceptional environmental concern, and its removal from water has increasingly attracted global attention due to its wide distribution and strong bioaccumulation. Adsorption is considered an effective technique for PFOA removal and more efficient PFOA sorbents are still of interest. This study developed a dual grafted fluorinated hydrocarbon amine weak anion exchange (WAX) polymeric resin (Sepra-WAX-KelF-PEI) for PFOA removal from water. This polymer was synthesized by a two-step amine grafting reaction procedure involving first the reaction of the Sepra-WAX hydrocarbon polymer with poly(vinylidinefluoride-chlorotrifluoroethylene) (Kel-F 800) and then a second reaction with polyethyleneimine (PEI). Characterization of the synthesized polymers was performed using scanning electron microscopy and elemental analysis (F and Cl) by energy dispersive X-ray spectroscopy. The PFOA adsorption performance evaluations were conducted by packed column flow analyses with on-line detection. The results show the breakthrough of the Sepra-WAX-KelF-PEI synthesized with optimum stoichiometry was two times better than the starting anion exchange polymer Sepra-WAX, and six times better than powdered activated carbon, when using the same column size. The adsorption mechanisms of this novel adsorbent including hydrophobic interaction and electrostatic interaction were also clarified in this study. The adsorption kinetic parameters of the two optimum synthesized sorbents were determined using the Thomas model, the Yoon-Nelson model, and batch isotherm studies, and compared with those found with activated carbon and the starting WAX resin. Good agreement of the batch isotherm and column studies with respect to adsorption capacities trends between all three polymers (Sepra-WAX, Sepra-WAX-KelF, and Sepra-WAX-KelF-PEI) were noted.

Evaluation of a commercial packed bed flow hydrogenator for reaction screening, optimization, and synthesis.

The performance of the ThalesNano H-Cube(®), a commercial packed bed flow hydrogenator, was evaluated in the context of small scale reaction screening and optimization. A model reaction, the reduction of styrene to ethylbenzene through a 10% Pd/C catalyst bed, was used to examine performance at various pressure settings, over sequential runs, and with commercial catalyst cartridges. In addition, the consistency of the hydrogen flow was indirectly measured by in-line UV spectroscopy. Finally, system contamination due to catalyst leaching, and the resolution of this issue, is described. The impact of these factors on the run-to-run reproducibility of the H-Cube(®) reactor for screening and reaction optimization is discussed.

Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study.

The accurate prediction of likely discharges and estimates of length of stay (LOS) aid in effective hospital administration and help to prevent access block. Machine learning (ML) may be able to help with these tasks. For consecutive patients admitted under General Medicine at the Royal Adelaide Hospital over an 8-month period, daily ward round notes and relevant discrete data fields were collected from the electronic medical record. These data were then split into training and testing sets (7-month/1-month train/test split) prior to use in ML analyses aiming to predict discharge within the next 2 days, discharge within the next 7 days and an estimated date of discharge (EDD). Artificial neural networks and logistic regression were effective at predicting discharge within 48 h of a given ward round note. These models achieved an area under the receiver operator curve (AUC) of 0.80 and 0.78, respectively. Prediction of discharge within 7 days of a given note was less accurate, with artificial neural network returning an AUC of 0.68 and logistic regression an AUC of 0.61. The generation of an exact EDD remains inaccurate. This study has shown that repeated estimates of LOS using daily ward round notes and mixed-data inputs are effective in the prediction of general medicine discharges in the next 48 h. Further research may seek to prospectively and externally validate models for prediction of upcoming discharge, as well as combination human-ML approaches for generating EDDs.