Implementation and evaluation of a nurse-administered dysphagia screening tool to identify patient’s at high risk for post-extubation dysphagia

Purpose: Post-extubation dysphagia (PED) occurs in 3% to 62% of intensive care unit patients. Patients with moderate or severe PED are more likely to experience pneumonia, reintubation, or death. Early identification of post-extubation dysphagia is crucial so diet modifications, temporary feeding measures, and/or advanced swallow evaluations and therapies can be implemented. The purpose of this quality improvement project was to implement a nurse-administered dysphagia screening tool (NADST) for post-extubated patients in a 21-bed mixed medical intensive care unit (MICU) at a large academic medical center. Methods: Utilizing quality improvement methods, a modified dysphagia screening tool was trialed in a MICU for two months. Eight Super Users (RNs) were recruited and attended one of three train the trainer sessions taught by a Speech Language Pathologist. The Super Users trained the remaining unit nurses (RNs). A 5-minute video for the unit nurses was created to supplement the trainings. Pre- and post-intervention surveys were administered to measure changes in knowledge, beliefs, and practices around PED screening. Patient electronic health records were reviewed to identify all patients eligible for PED screening and screening dispositions. Results: Of the 59 eligible patients, 34 patients were screened utilizing the NADST. Nurses had a high level of knowledge but varying practices and comfort with dysphagia screening prior to the intervention. The intervention increased the comfort level and screening frequencies for PED. The NADST was found to be useful for improving nursing practice. Conclusions: Through the utilization of a Super User training model, this quality improvement project demonstrated that implementing a standardized PED screening tool does improve PED screening frequencies

Book Review

Review of Laura Miller, The Magician’s Book: A Skeptic’s Adventures in Narnia (New York: Little, Brown and Company, 2008). 311 pages. $25.99. ISBN 9780316017633

Developing a Vermont Nurse Triage Line: A Systems Improvement Project

Nurse Triage Lines (NTL) have been utilized since the 1970s as a healthcare service delivery model. The efficacy of their utilization has been proven in non-acute, mainly primary care settings. During the 2009 H1N1 pandemic in the United States, NTLs proved their efficacy in an acute emergency event. The Minnesota FluLine, the exemplar case study, showed a significant reduction in unnecessary healthcare resource utilization as well as a significant economic cost savings. This project performed an organizational assessment for the Vermont Department of Health (VDH) focused on implementing an NTL. Through qualitative semi-structured interviews with key informants, key themes surround the implementation of an NTL were identified utilizing a modified Strengths, Weaknesses, Opportunities, and Threats model. Through quantitative use of economic modeling, a cost savings analysis was preformed to explore potential cost savings for Vermont if an NTL had been established during the 2009 H1N1 Pandemic. Results of this project suggest that there is a need for an NTL. Furthermore, VDH is capable of implementing an NTL. Future projects should focus on operationalizing an NTL and evaluating the process and outcomes

Risk perception and emergency experience: comparing a representative German sample with German emergency survivors

People’s perception of risk and its influencing factors has become an important element of research in past decades. The present paper investigated the influence of emergency experiences on risk perception and the impact of experience and gender on the accuracy of risk perception. A representative sample of the German population was subdivided into a general survivor group who had experienced at least one emergency previously (N = 165) and a general public group with no prior emergency experiences (N = 2248), which were compared to a German sample of survivors from the EU-funded Behavior, Security, and Culture (BeSeCu) international study of human behavior in emergency situations and evacuations (N = 201). The perceived risk of different emergencies – including larger-scale events like floods and other important but often overlooked events like domestic fires – was assessed with a questionnaire. Objective risk was also calculated for different emergencies and compared to the risk perceptions of each group to provide a measure of accuracy. The results of this study showed that emergency experiences increase perceived risk, for the experienced event in particular, and this outcome was evident regardless of whether the event was a large-scale one like a natural disaster or a smaller-scale one like a fire in one’s home. Additional data from the BeSeCu survivors identified several pre-, peri-, and post-event factors that might have influenced this outcome. Further results included the finding that gender is an important factor that moderates the accuracy of risk estimations but researchers should be mindful that the presence and pattern of any gender difference in perceived risk accuracy may vary across different types of event. Possible reasons and implications of the results are discussed

iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects

We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded. Recent RGB-D-based methods are robust to moderate degrees of occlusion. For RGB inputs, no previous method works well for partly occluded objects. Our main contribution is to present the first deep learning-based system that estimates accurate poses for partly occluded objects from RGB-D and RGB input. We achieve this with a new instance-aware pipeline that decomposes 6D object pose estimation into a sequence of simpler steps, where each step removes specific aspects of the problem. The first step localizes all known objects in the image using an instance segmentation network, and hence eliminates surrounding clutter and occluders. The second step densely maps pixels to 3D object surface positions, so called object coordinates, using an encoder-decoder network, and hence eliminates object appearance. The third, and final, step predicts the 6D pose using geometric optimization. We demonstrate that we significantly outperform the state-of-the-art for pose estimation of partly occluded objects for both RGB and RGB-D input

Recovering 6D Object Pose: A Review and Multi-modal Analysis

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem

Suris tetrons: possible spectroscopic evidence for four-particle optical excitations of the 2D electron gas

The excitations of a two-dimensional electron gas in quantum wells with intermediate carrier density (~10^{11} cm^{-2}), i.e., between the exciton-trion- and the Fermi-Sea range, are so far poorly understood. We report on an approach to bridge this gap by a magneto-photoluminescence study of modulation-doped (Cd,Mn)Te quantum well structures. Employing their enhanced spin splitting, we analyzed the characteristic magnetic-field behavior of the individual photoluminescence features. Based on these results and earlier findings by other authors, we present a new approach for understanding the optical transitions at intermediate densities in terms of four-particle excitations, the Suris tetrons, which were up to now only predicted theoretically. All characteristic photoluminescence features are attributed to emission from these quasi-particles when attaining different final states.Comment: 12 pages, 3 figure