Cost effectiveness of total knee arthroplasty from a health care providers' perspective before and after introduction of an interdisciplinary clinical pathway - is investment always improvement?

<p>Abstract</p> <p>Background</p> <p>Total knee arthroplasty (TKA) is an effective, but also cost-intensive health care intervention for end stage osteoarthritis. This investigation was designed to evaluate the cost-effectiveness of TKA before versus after introduction of an interdisciplinary clinical pathway from a University Orthopedic Surgery Department's cost perspective as an interdisciplinary full service health care provider.</p> <p>Methods</p> <p>A prospective trial recruited two sequential cohorts of 132 and 128 consecutive patients, who were interviewed by means of the WOMAC questionnaire. Direct process costs from the health care providers' perspective were estimated according to the German DRG calculation framework. The health economic evaluation was based on margiual cost-effectveness ratios (MCERs); an individual marginal cost effectiveness relation ≤ 100 € per % WOMAC index increase was considered as primary endpoint of the confirmatory cohort comparison. The interdisciplinary clinical pathway under consideration primarily consisted of a voluntary preoperative personal briefing of patients concerning postoperatively expectable progess in health status and optimum use of walking aids after surgery. All patients were supplied with written information on these topics, attendance of the personal briefing also included preoperative training for postoperative mobilisation by the Department's physiotherapeutic staff.</p> <p>Results</p> <p>An individual marginal cost effectiveness relation ≤ 100 €/% WOMAC index increase was found in 38% of the patients in the pre pathway implementation cohort versus in 30% of the post pathway implementation cohort (Fisher p = 0.278). Both cohorts showed substantial improvement in WOMAC scores (39 versus 35% in median), whereas the cohort did not differ significantly in the median WOMAC score before surgery (41% for the pre pathway cohort versus 44% for the post pathway cohort). Despite a locally significant decrease in costs (4303 versus 4194 € in median), the individual cost/benefit relation became worse after introduction of the pathway: for the first cohort the MCER was estimated 108 € per gained % WOMAC index increase (86 - 150 €/%) versus 118 €/% WOMAC gain (93 - 173 €/%) in the second cohort after pathway implementation. In summary, the proposed critical pathway for TKA could be shown to be significantly cost efficient, but not cost effective concerning functional outcome, when the above individual marginal cost effectiveness criterion was concentrated on.</p> <p>Conclusions</p> <p>The introduction of an interdisciplinary clinical pathway does not necessarily improve patient related outcomes. On the contrary, cost effectiveness from the health care providers' perspective may even turn out remarkably reduced in the setting considered here (functional outcome assessment after treatment by a full service health care provider).</p

Application of natural computing algorithms to maximum likelihood estimation of direction of arrival

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)This work presents a study of the performance of populational meta-heuristics belonging to the field of natural computing when applied to the problem of direction of arrival (DOA) estimation, as well as an overview of the literature about the use of such techniques in this problem. These heuristics offer a promising alternative to the conventional approaches in DOA estimation, as they search for the global optima of the maximum likelihood (ML) function in a framework characterized by an elegant balance between global exploration and local improvement, which are interesting features in the context of multimodal optimization, to which the ML-DOA estimation problem belongs. Thus, we shall analyze whether these algorithms are capable of implementing the ML estimator, i.e., finding the global optima of the ML function. In this work, we selected three representative natural computing algorithms to perform DOA estimation: differential evolution, clonal selection algorithm, and the particle swarm. Simulation results involving different scenarios confirm that these methods can reach the performance of the ML estimator, regardless of the number of sources and/or their nature. Moreover, the number of points evaluated by such methods is quite inferior to that associated with a grid search, which gives support to their application. (C) 2011 Elsevier B.V. All rights reserved.92513381352Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)FAPESP [2008/56937-2

Sensivity analysis of the non hyperbolic common reflection surface, 15th Annual Report, Hamburg

Annual reportaucu

Improving the Efficiency of Natural Computing Algorithms in DOA Estimation Using a Noise Filtering Approach

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)We propose a novel strategy to generate initial candidate solutions for bio-inspired algorithms applied to the direction of arrival estimation problem. The idea, which aims to improve the efficiency of the estimator, consists in using the frequency response of a well-known optimum noise reduction filter as the probability density function of the set of candidate solutions. In accordance to this approach, we also employ a modified likelihood function to reduce the estimation error. Simulation results considering an immune-inspired algorithm confirm a significant improvement of its performance and efficiency, and the new estimator reaches the conditional Cram,r-Rao lower bound.32419912001Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPESP [2008/56937-2, 2010/51027-8

Pre-stack Data Recovery through Common Offset CRS Stack with Differential Evolution

International audienc

Parameter estimation from non-hyperbolic reflection traveltimes for large aperture common midpoint gathers

International audienceAs far as superficial seismic reflection events are concerned, the classical normal moveout (NMO) process, which leads to the construction of seismic zero-offset (or stacked) sections, encounters several difficulties due to the large data aperture. One of these difficulties is that the hyperbolic approximation of the reflection traveltime in common midpoint (CMP) gathers is no longer valid when offset largely exceeds the target depth. Recently, Fomel and Kazinnik proposed a novel, multi-parameter, non-hyperbolic formula for the traveltime of such reflection events. This formula, which will be referred to here as the FK traveltime after the authors, is exact for reflectors whose shape can be described by a hyperbola, and shows promising accuracy for long offsets and/or curved reflectors. It also depends on the same set of parameters as the common reflection surface (CRS) traveltime. In this paper, we propose strategies for estimating these CRS parameters based on the FK traveltime using large aperture data. However, in contrast to traditional CRS processing, and due to their widespread use, only common midpoint (CMP) gathers will be considered for the parameter searches. We will begin with a sensitivity analysis, showing the impact of each parameter on the traveltime. Based on this analysis, we will propose a two-step estimation strategy, that could lead to improved seismic images, especially for very shallow, high aperture events. We will then highlight, through synthetic examples and discussions, the strengths and limitations of this strategy

A Study On The Application Of Bio-inspired Algorithms To The Problem Of Direction Of Arrival Estimation [um Estudo Da Aplicação De Algoritmos Bio-inspirados Ao Problema De Estimação De Direção De Chegada]

The classical solution to the problem of estimating the direction of arrival (DOA) of plane waves impinging on a sensor array is based on the application of the maximum likelihood method. This approach leads to the problem of optimizing a cost function which is non-linear, non-quadratic, multimodal and variant with respect to the signal-noise ratio (SNR). The methods proposed in the literature to solve this problem fail for a wide set of SNR values. This work presents the results obtained from a study on the application of natural computing algorithms to the DOA estimation problem. 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