1,721,251 research outputs found
Better hospital context increases success of care pathway implementation on achieving greater teamwork: a multicenter study on STEMI care
No full textOBJECTIVE:
To evaluate whether hospital context influences the effect of care pathway implementation on teamwork processes and output in STEMI care.
DESIGN:
A multicenter pre-post intervention study.
SETTING:
Eleven acute hospitals.
PARTICIPANTS:
Cardiologists-in-chief, nurse managers, quality staff, quality managers and program managers reported on hospital context. Teamwork was rated by professional groups (medical doctors, nurses, allied health professionals, other) in the following departments: emergency room, catheterization lab, coronary care unit, cardiology ward and rehabilitation.
INTERVENTION:
Care pathway covering in-hospital care from emergency services to rehabilitation.
MAIN OUTCOME MEASURES:
Hospital context was measured by the five dimensions of the Model for Understanding Success in Quality: microsystem, quality improvement team, quality improvement support, high-level organization, external environment. Teamwork process measures reflected teamwork between professional groups within departments and teamwork between departments. Teamwork output was measured through the level of organized care. Two-level regression analysis accounted for clustering of respondents within hospitals and assessed the influence of hospital context on the impact of care pathway implementation on teamwork.
RESULTS:
Care pathway implementation significantly improved teamwork processes both between professional groups (P < 0.001) and between departments (P < 0.001). Teamwork output also improved (P < 0.001). The effect of care pathway implementation on teamwork was more pronounced when the quality improvement team and quality improvement support and capacity were more positively reported on.
CONCLUSIONS:
Hospitals can leverage the effect of quality improvement interventions such as care pathways by evaluating and improving aspects of hospital context
Time series prediction using random weights fuzzy neural networks
No full textIn this paper, we introduce Random Weights Fuzzy Neural Networks as a suitable tool for solving prediction problems. The generalization capability of these randomized fuzzy neural networks is exploited in order to estimate accurately the sample be predicted from a multidimensional input. The latter is obtained by applying an embedding technique to the time series, which selects only the meaningful past samples to be used for prediction. We tested the proposed approach on real-world time series pertaining to the application context of power delivery. We proved the efficacy of the proposed approach by comparing its forecasting accuracy with respect to other prediction systems based on well-known data-driven regression models
Inverse classification for military decision support systems
No full textWe propose in this paper a military application, which can be used in civil contexts as well, for solving inverse classification problems. Pattern recognition and decision support systems are typical tools through which inverse classification problems can be solved in order to achieve the desired goals. As standard classifiers do not work properly for inverse classification, which is an inherent ill-posed problem and therefore difficult to be inverted, we propose a new approach that exploits all the information associated with the decisions observed in the past. The experimental results prove the feasibility of the proposed algorithm, with errors lower than 10% with respect to standard classification models
Fuzzy membership functions based on point-to-polygon distance evaluation
No full textIn this paper, a new approach is presented for the evaluation of membership functions in fuzzy clustering algorithms. Starting from the geometrical representation of clusters by polygons, the fuzzy membership is evaluated through a suited point-to-polygon distance estimation. Three different methods are proposed, either by using the geometrical properties of clusters in the data space or by using Gaussian or cone-shaped kernel functions. They differ from the basic trade-off between computational complexity and approximation accuracy. By the proposed approach, fuzzy clusters of any geometrical complexity can be used, since there is no longer required to impose constraints on the shape of clusters resulting from the choice of computationally affordable membership functions. The methods illustrated in the paper are validated in terms of speed and accuracy by using several numerical simulations. © 2013 IEEE
A higher-order fuzzy neural network for modeling financial time series
No full textThis work investigates on the widespread use of fuzzy neural networks in time series forecasting, concerning in particular the energy commodity markets. We propose a new learning strategy suited to any neural model. The proposed approach is further assessed in the case of higher-order Sugeno-type fuzzy rules, which are able to replicate the daily data and to reproduce the same statistical features for various Commodity time series. The data used are obtained from the daily return series of specific energy commodities, such as coal, natural gas, crude oil and electricity, over the period 2001-2010 for both the European and US markets. We will prove that our approach can obtain interesting results in terms of prediction accuracy and volatility estimation, compared to well-known neural and fuzzy neural models and to the ARMA-GARCH statistical paradigm
A decentralized algorithm for distributed ensemble clustering
No full textIn this paper, we consider the problem of distributed unsupervised learning where data to be clustered are partitioned over a set of agents having limited connectivity. In order to solve this problem, we consider a novel and extended ensemble clustering procedure in order to make it suitable to a fully distributed scenario. The proposed algorithm can deal with the case where each agent has a local and different dataset. Additionally, to reduce the total amount of exchanged information, only the local prototypes of clusters are forwarded among the neighbors. Cluster similarity indexes are adopted to solve conflicts among agents and to achieve a common structure at the end of the communication process. The experimental results prove the feasibility of this approach, which is able to reach an optimal performance when compared to a fully centralized implementation, that is where data is collected beforehand on a single clustering agent
Explainable spatio-temporal Graph Neural Networks for multi-site photovoltaic energy production
Full text linkIn recent years, there has been a growing demand for renewable energy sources, which are inherently associated with a decentralized distribution and dependent on weather conditions. Their management and associated forecasting of produced energy are tasks of increasing complexity. Spatio-Temporal Graph Neural Networks have been applied in this context with excellent results, taking advantage of the correct integration of both topological data, defined by the distribution of the plants in the territory, and temporal data of the time series. A drawback of graph neural networks is the recurrent mechanism adopted to process the temporal part, which increases greatly the computational load of these models. Moreover, these models are formulated for real and sensitive contexts where, in addition to being accurate, the predictions must also be understandable by the human operator. For these reasons, in this paper we propose a novel explainable energy forecasting framework based on Spatio-Temporal Graph Neural Networks: the forecasting model generates predictions by processing temporal and spatial information using a spectral graph convolution and a 1D convolutional neural network respectively, then we apply a state-of-the-art explainer to them in order to produce explanations about the generation process. Our proposed method obtains predictions having better performance than previous approaches, both in terms of computational efficiency and prediction accuracy, with the possibility of interpreting them in order to understand the generation process. The novel approach based on fusion of forecasting and explainability in a single framework enables the creation of powerful and reliable systems suitable for real-world issues and challenges
Disentangling organizational and economic levers in transitional care programs: A systematic review and configurational analysis
No full textBackground Promoting safe and efficient transitions of care is critical to reducing readmission rates and associated costs and improving the quality of patient care. A growing body of literature suggests that transitional care (TC) programs are effective in improving quality of life and reducing unplanned readmissions for several patient groups. TC programs are highly complex and multidimensional, requiring evidence on how specific practices and system characteristics influence their effectiveness in patient care, readmission reduction and costs. Methods Using a systematic review and a configurational approach, the study examines the role played by system characteristics (size, ownership, professional skills, technology used), the organizational components implemented, analyzing their combinations, and the potential economic impact of TC programs. Results The more organizational components are implemented, the greater the likelihood that a TC program will be successful in reducing readmission rates. Not all components have the same effect. The results show that certain components, ‘post-discharge symptom monitoring and management’ and ‘discharge planning’, are necessary but not sufficient to achieve the outcome. The results indicate the existence of two different combinations of components that can be considered sufficient for the reduction of readmissions. Furthermore, while system characteristics are underexplored, the study shows different ways of incorporating the skill mix of professionals and their mode of coordination in TC programs. Four organizational models emerge: the health-based monocentric, the social-based monocentric, the multidisciplinary team and the mono-specialist team. The economic impact of the programs is generally positive. Despite an increase in patient management costs, there is an overall reduction in all postintervention costs, particularly those related to readmissions. Conclusions The results underline the importance of examining in depth the role of system characteristics and organizational factors in facilitating the creation of a successful TC program. The work gives preliminary insights into how to systematize organizational practices and different coordination modes for facilitating decision-makers’ choices in TC implementation. While there is evidence that TC programs also have economic benefits, the quality of economic evaluations is relatively low and needs further study
A cluster randomized trial to assess the impact of clinical pathways for patients with stroke: rationale and design of the Clinical Pathways for Effective and Appropriate Care Study
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