Hospital production in a national health service: the physician's dilemma

There is a paucity of literature concerning the relation between the resource utilization decisions of the salaried hospital based physician and patient outcomes in a national health service. The purpose of our study is to model and test hospital production where the major decision makers are physicians. We view the output of the hospital as a distribution function over final health states of the patient. Our model contains a utility function for physicians whose arguments include the expected final health status of the patient and a pressure function which reflects the resource allocation and hospital financing policy of the Portuguese Health Ministry. Two sets of first order conditions derived from the theoretical model are estimated within a simultaneous equations framework using data consisting of inpatient discharges for the most frequent non-obstetric DRG during the 1992-1999 time period. We find evidence that budget setting methods and the possession of a third party payer outside of the NHS are important predictors for use of the resource in question. Moreover, we find that use of the resource is important in predicting the final health status of the patient.

Beyond the quantum formalism: consequences of a neural-oscillator model to quantum cognition

In this paper we present a neural oscillator model of stimulus response theory that exhibits quantum-like behavior. We then show that without adding any additional assumptions, a quantum model constructed to fit observable pairwise correlations has no predictive power over the unknown triple moment, obtainable through the activation of multiple oscillators. We compare this with the results obtained in de Barros (2013), where a criteria of rationality gives optimal ranges for the triple moment.Comment: 4 pages; to appear in the Advances in Cognitive Neurodynamics, Proceedings of the 4th International Conference on Cognitive Neurodynamics - 201

Hospital production in a national health service: the physician's dilemma

There is a paucity of literature concerning the relation between the resource utilization decisions of the salaried hospital based physician and patient outcomes in a national health service. The purpose of our study is to model and test hospital production where the major decision makers are physicians. We view the output of the hospital as a distribution function over final health states of the patient. Our model contains a utility function for physicians whose arguments include the expected final health status of the patient and a pressure function which reflects the resource allocation and hospital financing policy of the Portuguese Health Ministry. Two sets of first order conditions derived from the theoretical model are estimated within a simultaneous equations framework using data consisting of inpatient discharges for the most frequent non-obstetric DRG during the 1992-1999 time period. We find evidence that budget setting methods and the possession of a third party payer outside of the NHS are important predictors for use of the resource in question. Moreover, we find that use of the resource is important in predicting the final health status of the patient.Fundação para a Ciência e a Tecnologia (FCT

Decision Making for Inconsistent Expert Judgments Using Negative Probabilities

In this paper we provide a simple random-variable example of inconsistent information, and analyze it using three different approaches: Bayesian, quantum-like, and negative probabilities. We then show that, at least for this particular example, both the Bayesian and the quantum-like approaches have less normative power than the negative probabilities one.Comment: 14 pages, revised version to appear in the Proceedings of the QI2013 (Quantum Interactions) conferenc

Response adaptation in barrel cortical neurons facilitates stimulus detection during rhythmic whisker stimulation in anesthetized mice

Rodents use rhythmic whisker movements at frequencies between 4 and 12 Hz to sense the environment that will be disturbed when the animal touches an object. The aim of this work is to study the response adaptation to rhythmic whisker stimulation trains at 4 Hz in the barrel cortex and the sensitivity of cortical neurons to changes in the timing of the stimulation pattern. Longitudinal arrays of four iridium oxide electrodes were used to obtain single-unit recordings in supragranular, granular, and infragranular neurons in urethane anesthetized mice. The stimulation protocol consisted in a stimulation train of three air puffs (20 ms duration each) in which the time interval between the first and the third stimuli was fixed (500 ms) and the time interval between the first and the second stimuli changed (regular: 250 ms; “accelerando”: 375 ms; or “decelerando” stimulation train: 125 ms interval). Cortical neurons adapted strongly their response to regular stimulation trains. Response adaptation was reduced when accelerando or decelerando stimulation trains were applied. This facilitation of the shifted stimulus was mediated by activation of NMDA receptors because the effect was blocked by AP5. The facilitation was not observed in thalamic nuclei. Facilitation increased during periods of EEG activation induced by systemic application of IGF-I, probably by activation of NMDA receptors, as well. We suggest that response adaptation is the outcome of an intrinsic cortical information processing aimed at contributing to improve the detection of “unexpected” stimuli that disturbed the rhythmic behavior of exploratio

Machine learning approach for classification of REE/Fe-zeolite catalysts for fenton-like reaction

Various heterogeneous catalysts based on rare earth elements (REE) and iron supported on zeolites were selected and analyzed using machine learning approaches. REE were used in the preparation of multiple REE/Fe-zeolite catalysts with lanthanum, praseodymium or cerium obtained by ion exchange or impregnation methods, using FAU or MFI structures as supports. The efficiency of these REE/Fe-zeolite catalysts was examined in Fenton-like reaction, in the degradation of tartrazine (Tar) and indigo carmine (IC) as selected organic pollutants in the aqueous solution. The REE/Fe-zeolite catalysts demonstrated outstanding performance, with Tar being degraded by over 80% and IC 95%. Machine learning algorithms were employed for clustering and classification of the different catalysts, based on their performance. Unsupervised learning algorithms like Principal Component Analysis and K-Means were used for pattern recognition while supervised classifiers were employed to classify the heterogeneous catalysts, considering their ability to degrade dyes by Fenton reaction