Flexible nurse staffing based on hourly bed census predictions

Workload on nursing wards depends highly on patient arrivals and patient lengths of stay, which are both inherently variable. Predicting this workload and staffing nurses accordingly is essential for guaranteeing quality of care in a cost effective manner. This paper introduces a stochastic method that uses hourly census predictions to derive efficient nurse staffing policies. The generic analytic approach minimizes staffing levels while satisfying so-called nurse-to-patient ratios. In particular, we explore the potential of flexible staffing policies which allow hospitals to dynamically respond to their fluctuating patient population by employing float nurses. The method is applied to a case study of the surgical inpatient clinic of the Academic Medical Center (AMC) Amsterdam. This case study demonstrates the method's potential to study the complex interaction between staffing requirements and several interrelated planning issues such as case mix, care unit partitioning and size, and surgical block planning. Inspired by the numerical results, the AMC decided that this flexible nurse staffing methodology will be incorporated in the redesign of the inpatient care operations during the upcoming years

Integral resource capacity planning for inpatient care services based on hourly bed census predictions

The design and operations of inpatient care facilities are typically largely historically shaped. A better match with the changing environment is often possible, and even inevitable due to the pressure on hospital budgets. Effectively organizing inpatient care requires simultaneous consideration of several interrelated planning issues. Also, coordination with upstream departments like the operating theater and the emergency department is much-needed. We present a generic analytical approach to predict bed census on nursing wards by hour, as a function of the Master Surgical Schedule (MSS) and arrival patterns of emergency patients. Along these predictions, insight is gained on the impact of strategic (i.e., case mix, care unit size, care unit partitioning), tactical (i.e., allocation of operating room time, misplacement rules), and operational decisions (i.e., time of admission/discharge). The method is used in the Academic Medical Center Amsterdam as a decision support tool in a complete redesign of the inpatient care operations

Packaging signals in single-stranded RNA viruses: nature’s alternative to a purely electrostatic assembly mechanism

The formation of a protective protein container is an essential step in the life-cycle of most viruses. In the case of single-stranded (ss)RNA viruses, this step occurs in parallel with genome packaging in a co-assembly process. Previously, it had been thought that this process can be explained entirely by electrostatics. Inspired by recent single-molecule fluorescence experiments that recapitulate the RNA packaging specificity seen in vivo for two model viruses, we present an alternative theory, which recognizes the important cooperative roles played by RNA–coat protein interactions, at sites we have termed packaging signals. The hypothesis is that multiple copies of packaging signals, repeated according to capsid symmetry, aid formation of the required capsid protein conformers at defined positions, resulting in significantly enhanced assembly efficiency. The precise mechanistic roles of packaging signal interactions may vary between viruses, as we have demonstrated for MS2 and STNV. We quantify the impact of packaging signals on capsid assembly efficiency using a dodecahedral model system, showing that heterogeneous affinity distributions of packaging signals for capsid protein out-compete those of homogeneous affinities. These insights pave the way to a new anti-viral therapy, reducing capsid assembly efficiency by targeting of the vital roles of the packaging signals, and opens up new avenues for the efficient construction of protein nanocontainers in bionanotechnology

Reaming debris as a novel source of autologous bone to enhance healing of bone defects

Item does not contain fulltextReaming debris is formed when bone defects are stabilized with an intramedullary nail, and contains viable osteoblast-like cells and growth factors, and might thus act as a natural osteoinductive scaffold. The advantage of using reaming debris over stem cells or autologous bone for healing bone defects is that no extra surgery is needed to obtain the material. To assess the clinical feasibility of using reaming debris to enhance bone healing, we investigated whether reaming debris enhances the healing rate of a bone defect in sheep tibia, compared to an empty gap. As golden standard the defect was filled with iliac crest bone. Bones treated with iliac crest bone and reaming debris showed larger callus volume, increased bone volume, and decreased cartilage volume in the fracture gap, and increased torsional toughness compared to the empty gap group at 3 weeks postoperative. In addition, bones treated with reaming debris showed increased torsional stiffness at 6 weeks postoperatively compared to the empty defect group, while bending stiffness was marginally increased. These results indicate that reaming debris could serve as an excellent alternative to iliac crest bone for speeding up the healing process in bone defects that are treated with an intramedullary nail

Development and validation of Raman spectroscopic classification models to discriminate tongue squamous cell carcinoma from non-tumorous tissue

Background Currently, up to 85% of the oral resection specimens have inadequate resection margins, of which the majority is located in the deeper soft tissue layers. The prognosis of patients with oral cavity squamous cell carcinoma (OCSCC) of the tongue is negatively affected by these inadequate surgical resections. Raman spectroscopy, an optical technique, can potentially be used for intra-operative evaluation of resection margins. Objective To develop in vitro Raman spectroscopy-based tissue classification models that discriminate OCSCC of the tongue from (subepithelial) non-tumorous tissue. Materials and methods Tissue classification models were developed using Principal Components Analysis (PCA) followed by (hierarchical) Linear Discriminant Analysis ((h)LDA). The models were based on a training set of 720 histopathologically annotated Raman spectra, obtained from 25 tongue samples (11 OCSCC and 14 normal) of 10 patients, and were validated by means of an independent validation set of 367 spectra, obtained from 19 tongue samples (6 OCSCC and 13 normal) of 11 patients. Results A PCA-LDA tissue classification model ‘tumor’ versus ‘non-tumorous tissue’ (i.e. surface squamous epithelium, connective tissue, muscle, adipose tissue, gland and nerve) showed an accuracy of 86% (sensitivity: 100%, specificity: 66%). A two-step PCA-hLDA tissue classification model ‘tumor’ versus ‘non-tumorous tissue’ showed an accuracy of 91%

A lattice study of 3D compact QED at finite temperature

We study the deconfinement phase transition and monopole properties in the finite temperature 3D compact Abelian gauge model on the lattice. We predict the critical coupling as function of the lattice size in a simplified model to describe monopole binding. We demonstrate numerically that the monopoles are sensitive to the transition. In the deconfinement phase the monopoles appear in the form of a dilute gas of magnetic dipoles. In the confinement phase both monopole density and string tension differ from semiclassical estimates if monopole binding is neglected. However, the analysis of the monopole clusters shows that the relation between the string tension and the density of monopoles in charged clusters is in reasonable agreement with those predictions. We study the cluster structure of the vacuum in both phases of the model.Comment: 18 pages, 14 EPS figures, LaTeX uses epsfig.st