14,643 research outputs found
Efficiency evaluation for pooling resources in health care
Hospitals traditionally segregate resources into centralized functional departments such as diagnostic departments, ambulatory care centres, and nursing wards. In recent years this organizational model has been challenged by the idea that higher quality of care and efficiency in service delivery can be achieved when services are organized around patient groups. Examples include specialized clinics for breast cancer patients and clinical pathways for diabetes patients. Hospitals are struggling with the question of whether to become more centralized to achieve economies of scale or more decentralized to achieve economies of focus. Using quantitative Queueing Theory and Simulation models, we examine service and patient group characteristics to determine the conditions where a centralized model is more efficient and conversely where a decentralized model is more efficient. The results from the model measure the tradeoffs between economies of scale and economies of focus from which management guidelines are derived
Efficiency evaluation for pooling resources in health care
Hospitals traditionally segregate resources into centralized functional departments such as diagnostic departments, ambulatory care centers, and nursing wards. In recent years this organizational model has been challenged by the idea that higher quality of care and efficiency in service delivery can be achieved when services are organized around patient groups. Examples include specialized clinics for breast cancer patients and clinical pathways for diabetes patients. Hospitals are struggling with the question of whether to become more centralized to achieve economies of scale or more decentralized to achieve economies of focus. In this paper we examine service and patient group characteristics to study the conditions where a centralized model is more efficient, and conversely, where a decentralized model is more efficient. This relationship is examined analytically with a queuing model to determine themost influential factors and then with simulation to fine-tune the results. The tradeoffs between economies of scale and economies of focus measured by these models are used to derive general management guidelines
Hybrid Spectrum Sharing in mmWave Cellular Networks
While spectrum at millimeter wave (mmWave) frequencies is less scarce than at
traditional frequencies below 6 GHz, still it is not unlimited, in particular
if we consider the requirements from other services using the same band and the
need to license mmWave bands to multiple mobile operators. Therefore, an
efficient spectrum access scheme is critical to harvest the maximum benefit
from emerging mmWave technologies. In this paper, we introduce a new hybrid
spectrum access scheme for mmWave networks, where data is aggregated through
two mmWave carriers with different characteristics. In particular, we consider
the case of a hybrid spectrum scheme between a mmWave band with exclusive
access and a mmWave band where spectrum is pooled between multiple operators.
To the best of our knowledge, this is the first study proposing hybrid spectrum
access for mmWave networks and providing a quantitative assessment of its
benefits. Our results show that this approach provides major advantages with
respect to traditional fully licensed or fully unlicensed spectrum access
schemes, though further work is needed to achieve a more complete understanding
of both technical and non technical implications
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Neural networks rely on convolutions to aggregate spatial information.
However, spatial convolutions are expensive in terms of model size and
computation, both of which grow quadratically with respect to kernel size. In
this paper, we present a parameter-free, FLOP-free "shift" operation as an
alternative to spatial convolutions. We fuse shifts and point-wise convolutions
to construct end-to-end trainable shift-based modules, with a hyperparameter
characterizing the tradeoff between accuracy and efficiency. To demonstrate the
operation's efficacy, we replace ResNet's 3x3 convolutions with shift-based
modules for improved CIFAR10 and CIFAR100 accuracy using 60% fewer parameters;
we additionally demonstrate the operation's resilience to parameter reduction
on ImageNet, outperforming ResNet family members. We finally show the shift
operation's applicability across domains, achieving strong performance with
fewer parameters on classification, face verification and style transfer.Comment: Source code will be released afterward
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