ABSTRACT
The study aimed to explore patients' experiences and perceptions throughout the various stages of endoscopic procedures and examine the association between patient-centered communication and the patient's experience. A total of 191 patients responded to pre- and post-procedure surveys that inquired about fear and pain, patients' satisfaction regarding the information provided to them, perceptions and experience. Pain was associated with post-procedure fear (r = 0.63, p < 0.01) and negatively associated with reported patient experience at the end of the visit (r = -0.17, p < 0.01). Significant positive associations were found between patient experience and satisfaction from the information provided before (r = 0.47, p < 0.01) and the information provided after the procedure (r = 0.51, p < 0.001). A predictive model found that perceptions toward the physicians, satisfaction from information provided before discharge, and feelings of trust are predictors of the patient experience (F = 44.9, R2 = 0.61, p < 0.001). Patients' satisfaction with information provided before and after the procedure can positively affect the patients' experience, leading to a decrease in fear and anxiety and increasing compliance with medical recommendations. Strategies for PCC with endoscopic patients should be developed and designed in a participatory manner, taking into account the various aspects associated with the patient experience.
ABSTRACT
We introduce the HueNet - a novel deep learning framework for a differentiable construction of intensity (1D) and joint (2D) histograms and present its applicability to paired and unpaired image-to-image translation problems. The key idea is an innovative technique for augmenting a generative neural network by histogram layers appended to the image generator. These histogram layers allow us to define two new histogram-based loss functions for constraining the structural appearance of the synthesized output image and its color distribution. Specifically, the color similarity loss is defined by the Earth Mover's Distance between the intensity histograms of the network output and a color reference image. The structural similarity loss is determined by the mutual information between the output and a content reference image based on their joint histogram. Although the HueNet can be applied to a variety of image-to-image translation problems, we chose to demonstrate its strength on the tasks of color transfer, exemplar-based image colorization, and edges â photo, where the colors of the output image are predefined. The code is available at https://github.com/mor-avi-aharon-bgu/HueNet.git.
