Improving patient safety by decreasing restraint use

Restraints are among the top 15 most frequently reported sentinel events (Cosper et al., 2013). An improvement project was undertaken to reduce restraint use on a medical/surgical acute care unit within a 200 bed not-for-profit teaching hospital. The project aimed to reduce physical restraint use prevalence by 10%. After conducting a literature review and a microsystem assessment, Multidisciplinary Rounding (MDR) was identified as an evidence-based intervention to address physical restraint use. A reduction in restraint prevalence was noted from 2.4% to 1.6%. This equates to a 33% reduction in restraint prevalence. Other interventions implemented during the same time frame may have also contributed to the reduction in restraint use including increased availability of restraint alternatives, staff awareness of project, and restraint communication board. The author recommends use of MDR as a means to reduce physical restraint use, and recommends utilizing a team approach to reducing restraint use in combination with ensuring availability of restraint alternatives and bringing awareness to unit staff regarding misconceptions about the use of physical restraints

Alien Registration- Goldberg, Pearl R. (Auburn, Androscoggin County)

https://digitalmaine.com/alien_docs/22697/thumbnail.jp

Collecting Sufficient Evidence When Conducting a Case Study

Case study is a popular research design within the social sciences despite concerns of its credibility. Case studies provide an in-depth exploration of the unit of analysis (case). Hence, data triangulation is a key characteristic of the design whose purpose is to provide a thick, rich, and contextual description. Data for varied sources enhances credibility of the study. However, studies involving only one source of evidence exist in peer reviewed publications. This paper reviews the nature of case studies and discusses the importance of data triangulation. Further, three published case studies involving a single source of data are reviewed and suggestions of more appropriate designs are provided

Loop Calculus in Statistical Physics and Information Science

Considering a discrete and finite statistical model of a general position we introduce an exact expression for the partition function in terms of a finite series. The leading term in the series is the Bethe-Peierls (Belief Propagation)-BP contribution, the rest are expressed as loop-contributions on the factor graph and calculated directly using the BP solution. The series unveils a small parameter that often makes the BP approximation so successful. Applications of the loop calculus in statistical physics and information science are discussed.Comment: 4 pages, submitted to Phys.Rev.Lett. Changes: More general model, Simpler derivatio

FRANKLIN COUNTY, IOWA'S PROGRAM IN PUBLIC AFFAIRS

Public Economics,

Gaussian Belief with dynamic data and in dynamic network

In this paper we analyse Belief Propagation over a Gaussian model in a dynamic environment. Recently, this has been proposed as a method to average local measurement values by a distributed protocol ("Consensus Propagation", Moallemi & Van Roy, 2006), where the average is available for read-out at every single node. In the case that the underlying network is constant but the values to be averaged fluctuate ("dynamic data"), convergence and accuracy are determined by the spectral properties of an associated Ruelle-Perron-Frobenius operator. For Gaussian models on Erdos-Renyi graphs, numerical computation points to a spectral gap remaining in the large-size limit, implying exceptionally good scalability. In a model where the underlying network also fluctuates ("dynamic network"), averaging is more effective than in the dynamic data case. Altogether, this implies very good performance of these methods in very large systems, and opens a new field of statistical physics of large (and dynamic) information systems.Comment: 5 pages, 7 figure