Effectiveness of an electronic inpatient medication record in reducing medication errors in Singapore

This study examined the effectiveness of an inpatient electronic medication record system in reducing medication errors in Singaporean hospitals. This pre- and post-intervention study involving a control group was undertaken in two Singaporean acute care hospitals. In one hospital the inpatient electronic medication record system was implemented while in another hospital the paper-based medication record system was used. The mean incidence difference in medication errors of 0.06 between pre-intervention (0.72 per 1000 patient days) and post-intervention (0.78 per 1000 patient days) for the two hospitals was not statistically significant (95%, CI: [0.26, 0.20]). The mean incidence differences in medication errors relating to prescription, dispensing, and administration were also not statistically different. Common system failures involved a lack of medication knowledge by health professionals and a lack of a systematic approach in identifying correct dosages. There was no difference in the incidence of medication errors following the introduction of the electronic medication record system. More work is needed on how this system can reduce medication error rates and improve medication safety. © 2013 Wiley Publishing Asia Pty Ltd.

Enabling construction innovation : The role of a no-blame culture as a collaboration behavioural driver in project alliances

A no-blame culture is widely accepted as a collaboration driver yet we see surprisingly scant literature on the theoretical underpinnings for the construction and project management context. A no-blame culture in project alliances, as conducted in Australasia, promotes innovative thinking in action. Innovation is dependent upon collaboration and true collaboration is inextricably linked with behavioural drivers. Foremost of these is a culture of openness and willingness to share the pain and gain from experimentation, one that requires that collaborators be protected from the threat of being blamed and held accountable for experimental failure. The Australasian project alliance procurement form has a unique 'no-blame' behavioural contract clause that can result in the type of breakthrough thinking crucial in developing a collaborative culture where innovation can evolve through a process of trial and error. © 2014 Taylor & Francis.

Effects of Mitochondrial DNA Rate Variation on Reconstruction of Pleistocene Demographic History in a Social Avian Species, Pomatostomus superciliosus

Mitochondrial sequence data is often used to reconstruct the demographic history of Pleistocene populations in an effort to understand how species have responded to past climate change events. However, departures from neutral equilibrium conditions can confound evolutionary inference in species with structured populations or those that have experienced periods of population expansion or decline. Selection can affect patterns of mitochondrial DNA variation and variable mutation rates among mitochondrial genes can compromise inferences drawn from single markers. We investigated the contribution of these factors to patterns of mitochondrial variation and estimates of time to most recent common ancestor (TMRCA) for two clades in a co-operatively breeding avian species, the white-browed babbler Pomatostomus superciliosus. Both the protein-coding ND3 gene and hypervariable domain I control region sequences showed departures from neutral expectations within the superciliosus clade, and a two-fold difference in TMRCA estimates. Bayesian phylogenetic analysis provided evidence of departure from a strict clock model of molecular evolution in domain I, leading to an over-estimation of TMRCA for the superciliosus clade at this marker. Our results suggest mitochondrial studies that attempt to reconstruct Pleistocene demographic histories should rigorously evaluate data for departures from neutral equilibrium expectations, including variation in evolutionary rates across multiple markers. Failure to do so can lead to serious errors in the estimation of evolutionary parameters and subsequent demographic inferences concerning the role of climate as a driver of evolutionary change. These effects may be especially pronounced in species with complex social structures occupying heterogeneous environments. We propose that environmentally driven differences in social structure may explain observed differences in evolutionary rate of domain I sequences, resulting from longer than expected retention times for matriarchal lineages in the superciliosus clade.

On the choice of test for a unit root when the errors are conditionally heteroskedastic

It is well known that in the context of the classical regression model with heteroskedastic errors, while ordinary least squares (OLS) is not efficient, the weighted least squares (WLS) and quasi-maximum likelihood (QML) estimators that utilize the information contained in the heteroskedasticity are. In the context of unit root testing with conditional heteroskedasticity, while intuition suggests that a similar result should apply, the relative performance of the tests associated with the OLS, WLS and QML estimators is not well understood. In particular, while QML has been shown to be able to generate more powerful tests than OLS, not much is known regarding the relative performance of the WLS-based test. By providing an in-depth comparison of the tests, the current paper fills this gap in the literature.

The asymptotic distribution of the CADF unit root test in the presence of heterogeneous AR(p) errors

The CADF test of Pesaran (J Appl Econ 22:265–312, 2007) are among the most popular univariate tests for cross-section correlated panels around. Yet, the existing asymptotic analysis of this test statistic is limited to a model in which the errors are assumed to follow a simple AR(1) structure with homogenous autoregressive coefficients. One reason for this is that the model involves an intricate identification issue, as both the serial and cross-section correlation structures of the errors are unobserved. The purpose of the current paper is to tackle this issue and in so doing extend the existing analysis to the case of AR((Formula presented.)) errors with possibly heterogeneous coefficients.

Driver behaviour prediction for motion simulators using changepoint segmentation

Driving phenomenon is a repetitive process, that permits sequential learning under identifying the proper change periods. Sequential filtering is widely used for tracking and prediction of state dynamics. However, it suffers at abrupt changes, which cause sudden incremental prediction error. We provide a sequential filtering approach using online Bayesian detection of change points to decrease prediction error generally, and specifically at abrupt changes. The approach learns from optimally detected segments for identifying driving behaviour. Change points detection is done by the Pruned Exact Linear Time algorithm. Computational cost of our approach is bounded by the cost of the implemented sequential filter. This computational performance is suitable to the online nature of motion simulator's delay reduction. The approach was tested on a simulated driving scenario using Vortex by CM Labs. The state dimensions are simulated 2D space coordinates, and velocity. Particle filter was used for online sequential filtering. Prediction results show that change-point detection improves the quality of state estimation compared to traditional sequential filters, and is more suitable for predicting behavioural activities.