An anonymous survey of 1143 employees in 17 nursing facilities assessed knowledge of, attitudes about, self-perceived compliance with, and barriers to implementing the 2002 Centers for Disease Control and Prevention hand hygiene guidelines. Overall, employees reported positive attitudes toward the guidelines but differed with regard to knowledge, compliance, and perceived barriers. These findings provide guidance for practice improvement programs in long-term care settings.

To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients' behaviors in real-time, is proposed. In this study, we use an mmWave radar to track multiple patients and detect the scattering point cloud of each one. For each patient, the Doppler pattern of the point cloud over a time period is collected as the behavior signature. A three-layer CNN model is created to classify the behavior for each patient. The tracking and point clouds detection algorithm was also implemented on an mmWave radar hardware platform with an embedded graphics processing unit (GPU) board to collect Doppler pattern and run the CNN model. A training dataset of six types of behavior were collected, over a long duration, to train the model using Adam optimizer with an objective to minimize cross-entropy loss function. Lastly, the system was tested for real-time operation and obtained a very good inference accuracy when predicting each patient's behavior in a two-patient scenario.

For an organization to be customer centric and service oriented requires that it use each encounter with a customer to create value, leverage advanced technologies to design digital services to fulfill the value, and assess perceived value-in-use to continue to revise the value as customer expectations evolve. The adaptation of value cycles to address the rapid changes in customer expectations requires agile digital platforms with dynamic software ecosystems interacting with multiple actors. For public health agencies focused on population health, these agile digital platforms should provide tailored care to address the distinct needs of select population groups. Using prior research on aging and dynamic software ecosystems, this paper develops a template for the design of an agile digital platform to support value cycle activities among clinical and non-clinical actors, including population groups. It illustrates the design of an agile digital platform to support clients that suffer from delirium, using digital services that leverage Internet of Things, natural language processing, and AI that uses real-time data for learning and care adaption. We conclude the paper with directions for future research.

No abstract

No abstract

No abstract

No abstract

Objectives Heart failure impacts patients’ functional capabilities, ultimately leading to frailty. The use of a left ventricular assist device (LVAD) is acceptable as both destination therapy and bridge to transplant in heart failure management. We aim to evaluate the prognostic value of the Clinical Frailty Scale (CFS) on outcomes in older patients undergoing implantation of LVAD. Methods We conducted a retrospective chart review of patients ≥ 60 years old that underwent LVAD implantation at our medical center from May 1, 2018, to October 30, 2020. CFS was retrospectively assigned before LVAD placement and CFS scores > 4 was considered frail. Kaplan–Meier curves and Cox regression were used to analyze 1‐year survival estimates. Results Forty percent of the cohort was classified as frail according to CFS. Thirty‐day re‐admission rates were comparable between frail and non‐frail patients (46% vs 35%; P = 0.419). 1‐year survival was lower in the frail vs non‐frail group (log rank, P = 0.017). On Cox analysis, only frailty was associated with 1‐year post‐intervention mortality (hazard ratio [HR] = 5.64, 95% confidence interval [CI] = 1.131–28.212; P = 0.035). Conclusions CFS‐defined frailty was associated with increased risk of 1‐year mortality after LVAD implantation. CFS may be a valuable tool in the frailty assessment for risk stratification of patients undergoing LVAD implantation. Multicenter studies are required to validate these findings.