The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study

Objectives: To assess the impact of a closed-loop electronic prescribing, automated dispensing, barcode patient identification and electronic medication administration record (EMAR) system on prescribing and administration errors, confirmation of patient identity before administration, and staff time. Design, setting and participants: Before-and-after study in a surgical ward of a teaching hospital, involving patients and staff of that ward. Intervention: Closed-loop electronic prescribing, automated dispensing, barcode patient identification and EMAR system. Main outcome measures: Percentage of new medication orders with a prescribing error, percentage of doses with medication administration errors (MAEs) and percentage given without checking patient identity. Time spent prescribing and providing a ward pharmacy service. Nursing time on medication tasks. Results: Prescribing errors were identified in 3.8% of 2450 medication orders pre-intervention and 2.0% of 2353 orders afterwards (p<0.001; χ2 test). MAEs occurred in 7.0% of 1473 non-intravenous doses pre-intervention and 4.3% of 1139 afterwards (p = 0.005; χ2 test). Patient identity was not checked for 82.6% of 1344 doses pre-intervention and 18.9% of 1291 afterwards (p<0.001; χ2 test). Medical staff required 15 s to prescribe a regular inpatient drug pre-intervention and 39 s afterwards (p = 0.03; t test). Time spent providing a ward pharmacy service increased from 68 min to 98 min each weekday (p = 0.001; t test); 22% of drug charts were unavailable pre-intervention. Time per drug administration round decreased from 50 min to 40 min (p = 0.006; t test); nursing time on medication tasks outside of drug rounds increased from 21.1% to 28.7% (p = 0.006; χ2 test). Conclusions: A closed-loop electronic prescribing, dispensing and barcode patient identification system reduced prescribing errors and MAEs, and increased confirmation of patient identity before administration. Time spent on medication-related tasks increased.

Identification of a novel mammalian post-translational modification, phosphocholine, on placental secretory polypeptides

Placental neurokinin B appears to be post-translationally modified by phosphocholine (PC) attached to the aspartyl side chain at residue 4 of the mature peptide. Corticotrophin releasing factor (CRF) was found to be expressed by the rat placenta with the main secreted forms being phosphocholinated proCRF+/- one or two polysaccharide moieties. A combination of high-pressure liquid chromatography (HPLC) and two-site immunometric analysis suggested that PC was also attached to the placental precursors of adrenocorticotrophin, hemokinin, activin and follistatin. However, the fully processed forms of rat placental activin and CRF were free of PC. Formerly, the parasitic filarial nematodes have used PC as a post-translational modification, attached via the polysaccharicle moiety of certain secretory glycoproteins to attenuate the host immune system allowing parasite survival, but it is the PC group itself which endows the carrier with the biological activity. The fact that treatment of proCRF peptides with phospholipase C but not endoglycosidase destroyed PC immunoreactivity suggested a simpler mode of attachment of PC to placental peptides than that used by nematodes. Thus, it is possible that by analogy the placenta uses its secreted phosphocholinated hormones to modulate the mother's immune system and help protect the placenta from rejection.

Identification of a serine protease involved with the regulation of adrenal growth

The adrenal cortex is a dynamic organ in which the cells of the outer cortex continually divide. It is well known that this cellular proliferation is dependent on constant stimulation from peptides derived from the ACTH precursor pro-opiomelanocortin (POMC) because disruption of pituitary corticotroph function results in rapid atrophy of the gland. Previous results from our laboratory have suggested that the adrenal mitogen is a fragment derived from the N-terminal of POMC not containing the gamma-MSH sequence. Because such a peptide is not generated during processing of POMC in the pituitary, we proposed that the mitogen is generated from circulating pro-gamma-MSH by an adrenal protease. Using degenerate oligonucleotides, we identified a secreted serine protease expressed by the adrenal gland that we named adrenal secretory protease (ASP). In the adrenal cortex, expression of ASP is limited to the outer zona glomerulosa/fasciculata, the region where cortical cells are believed to be derived, and is significantly up-regulated during compensatory growth. Y1 adrenocortical cells transfected with a vector expressing an antisense RNA (and thus having reduced levels of endogenous ASP) were found to grow slower than sense controls while also losing their ability to utilize exogenous pro-gamma-MSH in the media supporting a role for ASP in adrenal growth. Digestion of an N-POMC peptide substrate encompassing the residues around the dibasic cleavage site at positions 49/50 with affinity-purified ASP showed cleavage not to occur at the dibasic site but two residues downstream leading us to propose the identity of the adrenal mitogen to be N-POMC (1-52).

Identification of novel expressed sequences, up-regulated in the leucocytes of chronic fatigue syndrome patients

BACKGROUND: Chronic fatigue syndrome (CFS) is an increasing medical phenomenon of unknown aetiology leading to high levels of chronic morbidity. Of the many hypotheses that purport to explain this disease, immune system activation, as a central feature, has remained prominent but unsubstantiated. Supporting this, a number of important cytokines have previously been shown to be over-expressed in disease subjects. The diagnosis of CFS is highly problematic since no biological markers specific to this disease have been identified. The discovery of genes relating to this condition is an important goal in seeking to correctly categorize and understand this complex syndrome. OBJECTIVE: The aim of this study was to screen for changes in gene expression in the lymphocytes of CFS patients. METHODS: 'Differential Display' is a method for comparing mRNA populations for the induction or suppression of genes. In this technique, mRNA populations from control and test subjects can be 'displayed' by gel electrophoresis and screened for differing banding patterns. These differences are indicative of altered gene expression between samples, and the genes that correspond to these bands can be cloned and identified. Differential display has been used to compare expression levels between four control subjects and seven CFS patients. RESULTS: Twelve short expressed sequence tags have been identified that were over-expressed in lymphocytes from CFS patients. Two of these correspond to cathepsin C and MAIL1 - genes known to be upregulated in activated lymphocytes. The expression level of seven of the differentially displayed sequences have been verified by quantifying relative level of these transcripts using TAQman quantitative PCR. CONCLUSION: Taken as a whole, the identification of novel gene tags up-regulated in CFS patients adds weight to the idea that CFS is a disease characterized by subtle changes in the immune system.

Identification of prebiotic fructooligosaccharide metabolism in Lactobacillus plantarum WCFS1 through microarrays

Short-chain fructooligosaccharides (scFOS) and other prebiotics are used to selectively stimulate the growth and activity of lactobacilli and bifidobacteria in the colon. However, there is little information on the mechanisms whereby prebiotics exert their specific effects upon such microorganisms. To study the genomic basis of scFOS metabolism in Lactobacillus plantarum WCFS1, two-color microarrays were used to screen for differentially expressed genes when grown on scFOS compared to glucose (control). A significant up-regulation (8- to 60-fold) was observed with a set of only five genes located in a single locus and predicted to encode a sucrose phosphoenolpyruvate transport system (PTS), a beta-fructofuranosidase, a fructokinase, an alpha-glucosidase, and a sucrose operon repressor. Several other genes were slightly overexpressed, including pyruvate dehydrogenase. For the latter, no detectable activity in L. plantarum under various growth conditions has been previously reported. A mannose-PTS likely to encode glucose uptake was 50-fold down-regulated as well as, to a lower extent, other PTSs. Chemical analysis of the different moieties of scFOS that were depleted in the growth medium revealed that the trisaccharide 1-kestose present in scFOS was preferentially utilized, in comparison with the tetrasaccharide nystose and the pentasaccharide fructofuranosylnystose. The main end products of scFOS fermentation were lactate and acetate. This is the first example in lactobacilli of the association of a sucrose PTS and a beta-fructofuranosidase that could be used for scFOS degradation.

State-space model identification of a wireless THz network

We model the large scale fading of wireless THz communications links deployed in a metropolitan area taking into account reception through direct line of sight, ground or wall reflection and diffraction. The movement of the receiver in the three dimensions is modelled by an autonomous dynamic linear system in state-space whereas the geometric relations involved in the attenuation and multi-path propagation of the electric field are described by a static non-linear mapping. A subspace algorithm in conjunction with polynomial regression is used to identify a Wiener model from time-domain measurements of the field intensity.

An on-line algorithm of uncertain time delay estimation in a continuous system

In this paper, we present an on-line estimation algorithm for an uncertain time delay in a continuous system based on the observational input-output data, subject to observational noise. The first order Pade approximation is used to approximate the time delay. At each time step, the algorithm combines the well known Kalman filter algorithm and the recursive instrumental variable least squares (RIVLS) algorithm in cascade form. The instrumental variable least squares algorithm is used in order to achieve the consistency of the delay parameter estimate, since an error-in-the-variable model is involved. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Identification of nonlinear systems using generalized kernel models

Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach.

Information system evolvability, feedback and pattern languages

Information systems for business are frequently heavily reliant on software. Two important feedback-related effects of embedding software in a business process are identified. First, the system dynamics of the software maintenance process can become complex, particularly in the number and scope of the feedback loops. Secondly, responsiveness to feedback can have a big effect on the evolvability of the information system. Ways have been explored to provide an effective mechanism for improving the quality of feedback between stakeholders during software maintenance. Understanding can be improved by using representations of information systems that are both service-based and architectural in scope. The conflicting forces that encourage change or stability can be resolved using patterns and pattern languages. A morphology of information systems pattern languages has been described to facilitate the identification and reuse of patterns and pattern languages. The kind of planning process needed to achieve consensus on a system's evolution is also considered.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.

Robust nonlinear model identification methods using forward regression

In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.

Highly asymmetric phase diagram of a poly(1,2-octylene oxide)-poly(ethylene oxide) diblock copolymer system comprising a brush-like poly(1,2-octylene oxide) block

The phase diagram of a series of poly(1,2-octylene oxide)-poly(ethylene oxide) (POO-PEO) diblock copolymers is determined by small-angle X-ray scattering. The Flory-Huggins interaction parameter was measured by small-angle neutron scattering. The phase diagram is highly asymmetric due to large conformational asymmetry that results from the hexyl side chains in the POO block. Non-lamellar phases (hexagonal and gyroid) are observed near f(PEO) = 0.5, and the lamellar phase is observed for f(PEO) >= 0.5.

Neural networks for identification of nonlinear systems under random piecewise polynomial disturbances

The problem of identification of a nonlinear dynamic system is considered. A two-layer neural network is used for the solution of the problem. Systems disturbed with unmeasurable noise are considered, although it is known that the disturbance is a random piecewise polynomial process. Absorption polynomials and nonquadratic loss functions are used to reduce the effect of this disturbance on the estimates of the optimal memory of the neural-network model.