Use of discrete event simulation in hospital capacity planning

In recent years, the healthcare industry is undergoing a rapid expansion in the United States. For healthcare facilities, resource planning at early design stage is a critical step before architectural design. The ‘resources' here refer to both long term resources (pods, rooms, beds, configuration of one pod) in terms of capacity and configuration, and short term resources(staffs, equipments) in terms of capacity and allocation. To achieve performance targets defined by the clients, such as staff/equipment/bed utilization efficiency, average waiting time of all patients, turn away rate, an assessment and verification at the preliminary planning stage is necessary. There are at least two methods to solve this problem. The first is analytical in nature, relying on queuing theory, and falls under the industrial engineering field. The other is computational in nature, relying on process simulation, and specifically discrete event simulation. While queuing theory is easier to conduct, usually requiring less data, and providing more generic rules than simulation, simulation methods result in detailed information about patient flow modeling and deliver more accurate results. This paper is divided into three parts. The first part introduces queuing theory and discrete event simulation in terms of their principles, features and applications in healthcare planning. This is followed by a case study in the ED using discrete event simulation to plan pod configuration and number of pods for an emergency department. During this process, the simulation tool is introduced as an example instrument for advanced DES simulation. The paper ends with a discussion of outcomes. (1) DES is capable to differentiate between alternatives with small changes, and can be widely used to do capacity planning for healthcare facilities. (2) the chosen simulation tool supports the modelling and analysis steps well

Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks

Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis. However, high spatial resolution typically comes at the expense of longer scan time, less spatial coverage, and lower signal to noise ratio (SNR). Single Image Super-Resolution (SISR), a technique aimed to restore high-resolution (HR) details from one single low-resolution (LR) input image, has been improved dramatically by recent breakthroughs in deep learning. In this paper, we introduce a new neural network architecture, 3D Densely Connected Super-Resolution Networks (DCSRN) to restore HR features of structural brain MR images. Through experiments on a dataset with 1,113 subjects, we demonstrate that our network outperforms bicubic interpolation as well as other deep learning methods in restoring 4x resolution-reduced images.Comment: Accepted by ISBI'1

Multiple-Level Power Allocation Strategy for Secondary Users in Cognitive Radio Networks

In this paper, we propose a multiple-level power allocation strategy for the secondary user (SU) in cognitive radio (CR) networks. Different from the conventional strategies, where SU either stays silent or transmit with a constant/binary power depending on the busy/idle status of the primary user (PU), the proposed strategy allows SU to choose different power levels according to a carefully designed function of the receiving energy. The way of the power level selection is optimized to maximize the achievable rate of SU under the constraints of average transmit power at SU and average interference power at PU. Simulation results demonstrate that the proposed strategy can significantly improve the performance of SU compared to the conventional strategies.Comment: 12 page