Reducing clinical variations with clinical pathways: do pathways work?

OBJECTIVE: To test clinical pathways in a variety of Italian health care organizations in 2000-2002 to measure performance in decreasing process and outcome variations. DESIGN: Creation of indicators, specific for each clinical pathway, to measure variations in the care processes and outcomes. Pre- and post-analysis model to evaluate the possible effect of the clinical pathways on each indicator. SETTING: We tested the clinical pathways in six sites, each with different clinical pathways. RESULTS: Reductions in health care macro-variation phenomena (length of stay, patient pathways, etc.) and in performance micro-variation (variations in diagnostic and therapeutic prescriptions, protocol implementation, etc.) were shown in sites where pathways were implemented successfully. A significant improvement in outcome for patients who were treated according to the clinical pathway for heart failure was also demonstrated. CONCLUSIONS: The overall purpose of clinical pathways is to improve outcome by providing a mechanism to coordinate care and to reduce fragmentation, and ultimately cost. Our results demonstrated that it is possible to achieve this goal. Although controversial elements still exist, we think that clinical pathways can have a positive impact on quality in health care

Adenotonsillectomy in children with recurrent acute tonsillitis: review and implications for practice

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Teaching Mathematics to Non-Mathematics Majors through Problem Solving and New Technologies

The role of mathematics in several scientific disciplines is undisputed; work and everyday life take great advantage of its application. Nevertheless, students often tend to not particularly like it and to consider it of little interest. It is also believed that only people with a certain attitude are capable of mastering the subject. In consideration of this, we aimed to help science students develop mathematical competences by designing a course specifically oriented to applications and problem solving. We administered our course to students attending the first year of a program in biotechnology, asking them to work with technologies instilling curiosity and interest, thus achieving a better proficiency as a consequence. Two questionnaires, along with access and proficiency data, allowed us to collect information about students’ attitudes, beliefs, and activity, which we analyzed by means of descriptive statistics. The promotion of the interaction among learners made them active users of the contents, thus allowing for the adaptation of their learning paths according to their personal necessities, as well as the development of teamwork skills and flexibility. Finally, students recognized the usefulness of the problem-solving approach and the role played by software

NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks

Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently the Google Brain's team demonstrated the ability of Capsule Networks (CapsNets) to encode and learn spatial correlations between different input features, thereby obtaining superior learning capabilities compared to traditional (i.e., non-capsule based) DNNs. However, designing CapsNets using conventional methods is a tedious job and incurs significant training effort. Recent studies have shown that powerful methods to automatically select the best/optimal DNN model configuration for a given set of applications and a training dataset are based on the Neural Architecture Search (NAS) algorithms. Moreover, due to their extreme computational and memory requirements, DNNs are employed using the specialized hardware accelerators in IoT-Edge/CPS devices. In this paper, we propose NASCaps, an automated framework for the hardware-aware NAS of different types of DNNs, covering both traditional convolutional DNNs and CapsNets. We study the efficacy of deploying a multi-objective Genetic Algorithm (e.g., based on the NSGA-II algorithm). The proposed framework can jointly optimize the network accuracy and the corresponding hardware efficiency, expressed in terms of energy, memory, and latency of a given hardware accelerator executing the DNN inference. Besides supporting the traditional DNN layers, our framework is the first to model and supports the specialized capsule layers and dynamic routing in the NAS-flow. We evaluate our framework on different datasets, generating different network configurations, and demonstrate the tradeoffs between the different output metrics. We will open-source the complete framework and configurations of the Pareto-optimal architectures at https://github.com/ehw-fit/nascaps.Comment: To appear at the IEEE/ACM International Conference on Computer-Aided Design (ICCAD '20), November 2-5, 2020, Virtual Event, US

Polyvinyl butyral-based composites with carbon nanotubes: Efficient dispersion as a key to high mechanical properties

Even if the carbon nanotubes (CNTs) and their derivatives are commonly used as reinforcing phase in composite materials, also in commercial products, their tendency to agglomerate generally determines a scarce dispersion, thus not maximizing the effect due to the second phase. In this article, a perfect dispersion of highly entangled nanotubes was achieved by using a very simple approach: exploiting the dispersing effect of a low-cost polymer, polyvinyl butyral (PVB), coupled with standard ultrasound sonication. Several dispersion approaches were tested in order to develop a consistent and widely applicable dispersion protocol. The tape casting technology was subsequently used to produce 100 to 300 μm thick PVB-matrix composite tapes, reinforced by multiwall CNTs dispersed according to the optimized protocol. Their mechanical properties were evaluated, and a simple model was used to demonstrate that the effective dispersion of CNTs is the key to obtain significantly improved properties

The Link Among Neurological Diseases: Extracellular Vesicles as a Possible Brain Injury Footprint

Extracellular vesicles (EVs), referred as membranous vesicles released into body fluids from all cell types, represent a novel model to explain some aspects of the inter-cellular cross talk. It has been demonstrated that the EVs modify the phenotype of target cells, acting through a large spectrum of mechanisms. In the central nervous system, the EVs are responsible of the wide range of physiological processes required for normal brain function and neuronal support, such as immune signaling, cellular proliferation, differentiation, and senescence. Growing evidences link the EV functions to the pathogenic machinery of the neurological diseases, contributing to the disease progression and spreading. Extracellular vesicles are involved in the brain injury by multimodal ways; they propagate inflammation across the blood brain barrier (BBB), mediate neuroprotection and modulate regenerative processes. For these reasons, extracellular vesicles represent a promising biomarker in neurological disorders as well as an interesting starting point for the development of novel therapeutic strategies. Herein, we review the role of the EVs in the pathogenesis of neurological disease, discussing their potential clinical applications

Bacterial contamination of saline nasal irrigations in children: An original research

Microbiologic analysis of nasal saline irrigations (NSIs) used in hospitalized children was performed. Of 253 collected samples, 24.9% were positive, and the number of positive samples significantly increased over time (P < .001). Staphylococcus aureus was the most frequently detected bacterium (28.6%). None of the 118 patients who received NSIs developed a nasosinusal infection. Colonization by cutaneous and environmental germs is frequent and develops early. Hygienic measures should be advocated to reduce contamination