Demand for hospital emergency departments: a conceptual understanding

BACKGROUND: Emergency departments (EDs) are critical to the management of acute illness and injury, and the provision of health system access. However, EDs have become increasingly congested due to increased demand, increased complexity of care and blocked access to ongoing care (access block). Congestion has clinical and organisational implications. This paper aims to describe the factors that appear to infl uence demand for ED services, and their interrelationships as the basis for further research into the role of private hospital EDs. DATA SOURCES: Multiple databases (PubMed, ProQuest, Academic Search Elite and Science Direct) and relevant journals were searched using terms related to EDs and emergency health needs. Literature pertaining to emergency department utilisation worldwide was identified, and articles selected for further examination on the basis of their relevance and significance to ED demand. RESULTS: Factors influencing ED demand can be categorized into those describing the health needs of the patients, those predisposing a patient to seeking help, and those relating to policy factors such as provision of services and insurance status. This paper describes the factors influencing ED presentations, and proposes a novel conceptual map of their interrelationship. CONCLUSION: This review has explored the factors contributing to the growing demand for ED care, the influence these factors have on ED demand, and their interrelationships depicted in the conceptual model

Data Curation Format Profile: netCDF

A primer for the netCDF (*.nc) file format for the purpose of data curation. It covers various tools for viewing netCDF files and questions to consider when curating a netCDF dataset or file.https://deepblue.lib.umich.edu/bitstream/2027.42/145724/1/Public-Data-CurationFormat Profile-netCDF.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145724/5/Public-Data-CurationFormat Profile-netCDFv2.pdfDescription of Public-Data-CurationFormat Profile-netCDF.pdf : Original versionDescription of Public-Data-CurationFormat Profile-netCDFv2.pdf : Version 2.0 with Integrated Data Viewer information from Sophie Ho

Learning Light Field Angular Super-Resolution via a Geometry-Aware Network

The acquisition of light field images with high angular resolution is costly. Although many methods have been proposed to improve the angular resolution of a sparsely-sampled light field, they always focus on the light field with a small baseline, which is captured by a consumer light field camera. By making full use of the intrinsic \textit{geometry} information of light fields, in this paper we propose an end-to-end learning-based approach aiming at angularly super-resolving a sparsely-sampled light field with a large baseline. Our model consists of two learnable modules and a physically-based module. Specifically, it includes a depth estimation module for explicitly modeling the scene geometry, a physically-based warping for novel views synthesis, and a light field blending module specifically designed for light field reconstruction. Moreover, we introduce a novel loss function to promote the preservation of the light field parallax structure. Experimental results over various light field datasets including large baseline light field images demonstrate the significant superiority of our method when compared with state-of-the-art ones, i.e., our method improves the PSNR of the second best method up to 2 dB in average, while saves the execution time 48$\times$. In addition, our method preserves the light field parallax structure better.Comment: This paper was accepted by AAAI 202