Multiple Criteria Decision Models for Nurse-Patient Assignment: Balancing Workload and Continuity of Care

Continuity of care is critical for delivering high-quality care, yet has seldom been considered in models supporting nurse-patient assignment decisions within inpatient units. Research in the nursing literature suggests that assigning nurses to patients they have cared for previously can help reduce care-related error rates and increase patient satisfaction. However, it is also essential to ensure that patient workloads are allocated to nursing staff in a balanced manner to avoid overwork and burnout. This study investigates the tradeoffs associated with the assignment of patients to nurses in inpatient settings under the dual objective of maximizing continuity of care and minimizing workload imbalance. We develop a hybrid method that balances the need for fair workload distribution and continuity of care, and demonstrate the extent of the tradeoff between the level of continuity achieved and the associated cost in workload balance. To reduce the impact of this tradeoff, we relax the goal of maximizing continuity by introducing an acuity threshold. Here, patients with acuity values above the threshold vi are targeted for continuity-based assignment, and remaining patients are assigned to minimize workload imbalance. We evaluate the utility of introducing the threshold under a variety of hospital environmental conditions using a simulation model of the inpatient environment. Our findings show that it is possible to provide a substantial continuity assignment with a marginal impact on workload imbalance under the hybrid policy using the acuity threshold. In virtually all cases studied, the results show that it is possible to use the acuity threshold and gain the benefits of continuity of care, even under conditions of a strong preference for minimizing acuity imbalance

Exploring the workload balance effects of including continuity-based factors in nurse-patient assignments

Workload balance in nurse-patient assignments is important for ensuring quality in patient care. Unbalanced workloads can lead to high levels of nursing stress, medical errors, lower-quality outcomes, and higher costs. Studies have pro-posed assignment strategies based on patient acuity, location, and characteristics of specialized units. These methods do not address the part of workload associated with continuity in care coordination, and the potential benefits associated with continuity-based assignments. We present the results of a pilot simulation study comparing an acuity-oriented method to a continuity-based approach, using acuity as a measure of workload. Our results suggest that a purely continuity-based approach can result in skewed workloads when measured by patient acuity. In future work, we plan to consider hybrid methods, which may be able to provide the benefits of both continuity and acuity based methods

Low-Light Image Enhancement with Wavelet-based Diffusion Models

Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. To address these issues, we propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL. Specifically, we present a wavelet-based conditional diffusion model (WCDM) that leverages the generative power of diffusion models to produce results with satisfactory perceptual fidelity. Additionally, it also takes advantage of the strengths of wavelet transformation to greatly accelerate inference and reduce computational resource usage without sacrificing information. To avoid chaotic content and diversity, we perform both forward diffusion and reverse denoising in the training phase of WCDM, enabling the model to achieve stable denoising and reduce randomness during inference. Moreover, we further design a high-frequency restoration module (HFRM) that utilizes the vertical and horizontal details of the image to complement the diagonal information for better fine-grained restoration. Extensive experiments on publicly available real-world benchmarks demonstrate that our method outperforms the existing state-of-the-art methods both quantitatively and visually, and it achieves remarkable improvements in efficiency compared to previous diffusion-based methods. In addition, we empirically show that the application for low-light face detection also reveals the latent practical values of our method

Supervised Homography Learning with Realistic Dataset Generation

In this paper, we propose an iterative framework, which consists of two phases: a generation phase and a training phase, to generate realistic training data and yield a supervised homography network. In the generation phase, given an unlabeled image pair, we utilize the pre-estimated dominant plane masks and homography of the pair, along with another sampled homography that serves as ground truth to generate a new labeled training pair with realistic motion. In the training phase, the generated data is used to train the supervised homography network, in which the training data is refined via a content consistency module and a quality assessment module. Once an iteration is finished, the trained network is used in the next data generation phase to update the pre-estimated homography. Through such an iterative strategy, the quality of the dataset and the performance of the network can be gradually and simultaneously improved. Experimental results show that our method achieves state-of-the-art performance and existing supervised methods can be also improved based on the generated dataset. Code and dataset are available at https://github.com/megvii-research/RealSH.Comment: Accepted by ICCV 202

Clinical Decision Support Systems for Diabetes Care: Evidence and Development Between 2017 and Present

The clinical decision support systems (CDSs) for diabetes have improved significantly over the years. Multiple factors serve as driving forces for the uptake of CDSs. Newer technologies, initiatives, government mandates, and a competitive environment collectively facilitate advancement in diabetes care. This book chapter summarizes global CDSs development in recent years. Our review of the past few years’ publications on CDSs for diabetes shows that the United States is leading the world in technology development and clinical evidence generation. Developing countries worldwide are catching up in CDSs development and standards of patient care. Though most CDSs and published studies are on diabetes diagnosis, treatment, and management, a small portion of the research is devoted to prediabetes and type I diabetes. Increased efforts worldwide have been devoted to artificial intelligence and machine learning in diabetes care

Realistic Noise Synthesis with Diffusion Models

Deep learning-based approaches have achieved remarkable performance in single-image denoising. However, training denoising models typically requires a large amount of data, which can be difficult to obtain in real-world scenarios. Furthermore, synthetic noise used in the past has often produced significant differences compared to real-world noise due to the complexity of the latter and the poor modeling ability of noise distributions of Generative Adversarial Network (GAN) models, resulting in residual noise and artifacts within denoising models. To address these challenges, we propose a novel method for synthesizing realistic noise using diffusion models. This approach enables us to generate large amounts of high-quality data for training denoising models by controlling camera settings to simulate different environmental conditions and employing guided multi-scale content information to ensure that our method is more capable of generating real noise with multi-frequency spatial correlations. In particular, we design an inversion mechanism for the setting, which extends our method to more public datasets without setting information. Based on the noise dataset we synthesized, we have conducted sufficient experiments on multiple benchmarks, and experimental results demonstrate that our method outperforms state-of-the-art methods on multiple benchmarks and metrics, demonstrating its effectiveness in synthesizing realistic noise for training denoising models

Clonal dominance defines metastatic dissemination in pancreatic cancer

View the abstracthttps://openworks.mdanderson.org/leading-edge/1059/thumbnail.jp

Enhancer Reprogramming Confers Dependence on Glycolysis and IGF Signaling in KMT2D Mutant Melanoma.

Histone methyltransferase KMT2D harbors frequent loss-of-function somatic point mutations in several tumor types, including melanoma. Here, we identify KMT2D as a potent tumor suppressor in melanoma through an in vivo epigenome-focused pooled RNAi screen and confirm the finding by using a genetically engineered mouse model (GEMM) based on conditional and melanocyte-specific deletion of KMT2D. KMT2D-deficient tumors show substantial reprogramming of key metabolic pathways, including glycolysis. KMT2D deficiency aberrantly upregulates glycolysis enzymes, intermediate metabolites, and glucose consumption rates. Mechanistically, KMT2D loss causes genome-wide reduction of H3K4me1-marked active enhancer chromatin states. Enhancer loss and subsequent repression of IGFBP5 activates IGF1R-AKT to increase glycolysis in KMT2D-deficient cells. Pharmacological inhibition of glycolysis and insulin growth factor (IGF) signaling reduce proliferation and tumorigenesis preferentially in KMT2D-deficient cells. We conclude that KMT2D loss promotes tumorigenesis by facilitating an increased use of the glycolysis pathway for enhanced biomass needs via enhancer reprogramming, thus presenting an opportunity for therapeutic intervention through glycolysis or IGF pathway inhibitors

Balancing Workload and Care Communication Costs in Nurse Patient Assignment

Nurse-patient assignment is a complex task. Many proposed methods attempt to balance workload across nurses using work-descriptive factors, such as patient acuity, or patient location/distance. However, such methods ignore other factors, such as the cost of care communication. In this initial work, we propose a prototype hybrid method that attempts to blend the use of both types of factors for assignment. We evaluated this hybrid method along with three control methods in a simulated inpatient unit environment. The results showed our hybrid method could obtain benefits of less communication cost with some penalty in acuity imbalance. Future work will focus on refining the method to reduce or avoid this penalty