Impact of automated dispensing cabinets on medication selection and preparation error rates in an emergency department: a prospective and direct observational before-and-after study

RATIONALE, AIMS AND OBJECTIVES: The implementation of automated dispensing cabinets (ADCs) in healthcare facilities appears to be increasing, in particular within Australian hospital emergency departments (EDs). While the investment in ADCs is on the increase, no studies have specifically investigated the impacts of ADCs on medication selection and preparation error rates in EDs. Our aim was to assess the impact of ADCs on medication selection and preparation error rates in an ED of a tertiary teaching hospital. METHODS: Pre intervention and post intervention study involving direct observations of nurses completing medication selection and preparation activities before and after the implementation of ADCs in the original and new emergency departments within a 377-bed tertiary teaching hospital in Australia. Medication selection and preparation error rates were calculated and compared between these two periods. Secondary end points included the impact on medication error type and severity. RESULTS: A total of 2087 medication selection and preparations were observed among 808 patients pre and post intervention. Implementation of ADCs in the new ED resulted in a 64.7% (1.96% versus 0.69%, respectively, P = 0.017) reduction in medication selection and preparation errors. All medication error types were reduced in the post intervention study period. There was an insignificant impact on medication error severity as all errors detected were categorised as minor. CONCLUSION: The implementation of ADCs could reduce medication selection and preparation errors and improve medication safety in an ED setting.

The oral language skills of young offenders: a pilot invesitgation

Although there is a large body of evidence attesting to the poor social skills of juvenile offenders, few workers have examined the underlying language skills of this population. This pilot study investigated the language skills of a group of young offenders in comparison to non-offending school students. Data were gathered from 15 community-based young offender males, aged between 13 and 21 years (M = 16.5 years, SD = 2.1) from the Victorian southern region Juvenile Justice Units. The comparison group comprised 15 male students, aged between 15 and 17 years (M = I 6.4 years; SD = 0.5 I) from government high schools in south-eastern metropolitan Melbourne. Each participant completed a narrative discourse task and measures of speed of processing, and abstract language. It was hypothesised that the young offender group would perform more poorly on each of the language tasks than the comparison group. Independent t tests (with a modified alpha level to control for family-wise error rates) showed that there were significant differences in the expected direction, on all language measures. Notwithstanding the pilot nature of the investigation, implications of these findings for both further research and intervention/early intervention are described.

Corner Inlet and Nooramunga Habitat Mapping Project – Part 2 : High resolution mapping and change detection analysis using 2011 Rapid Eye Satellite Image compared to 2009 ALOS Satellite Image

Building on a habitat mapping project completed in 2011, Deakin University was commissioned by Parks Victoria (PV) to apply the same methodology and ground-truth data to a second, more recent and higher resolution satellite image to create habitat maps for areas within the Corner Inlet and Nooramunga Marine and Coastal Park and Ramsar area. A ground-truth data set using in situ video and still photographs was used to develop and assess predictive models of benthic marine habitat distributions incorporating data from both RapidEye satellite imagery (corrected for atmospheric and water column effects by CSIRO) and LiDAR (Light Detection and Ranging) bathymetry. This report describes the results of the mapping effort as well as the methodology used to produce these habitat maps.

Overall accuracies of habitat classifications were good, with error rates similar to or better than the earlier classification (>73 % and kappa values > 0.58 for both study localities). The RapidEye classification failed to accurately detect Pyura and reef habitat classes at the Corner Inlet locality, possibly due to differences in spectral frequencies. For comparison, these categories were combined into a ‘non-seagrass’ category, similar to the one used at the Nooramunga locality in the original classification. Habitats predicted with highest accuracies differed from the earlier classification and were Posidonia in Corner Inlet (89%), and bare sediment (no-visible seagrass class) in Nooramunga (90%). In the Corner Inlet locality reef and Pyura habitat categories were not distinguishable in the repeated classification and so were combined with bare sediments. The majority of remaining classification errors were due to the misclassification of Zosteraceae as bare sediment and vice versa. Dominant habitats were the same as those from the 2011 classification with some differences in extent. For the Corner Inlet study locality the no-visible seagrass category remained the most extensive (9059 ha), followed by Posidonia (5,513 ha) and Zosteraceae (5,504 ha). In Nooramunga no-visible seagrass (6,294 ha), Zosteraceae (3,122 ha) and wet saltmarsh (1,562 ha) habitat classes were most dominant.

Change detection analyses between the 2009 and 2011 imagery were undertaken as part of this project, following the analyses presented in Monk et al. (2011) and incorporating error estimates from both classifications. These analyses indicated some shifts in classification between Posidonia and Zosteraceae as well as a general reduction in the area of Zosteraceae. Issues with classification of mixed beds were apparent, particularly in the main Posidonia bed at Nooramunga where a mosaic of Zosteraceae and Posidonia was seen that was not evident in the ALOS classification. Results of a reanalysis of the 1998-2009 change detection illustrating effects of binning of mixed beds is also provided as an appendix.

This work has been successful in providing baseline maps at an improved level of detail using a repeatable method meaning that any future changes in intertidal and shallow water marine habitats may be assessed in a consistent way with quantitative error assessments. In wider use, these maps should also allow improved conservation planning, advance fisheries and catchment management, and progress infrastructure planning to limit impacts on the Inlet environment.

Gradient edge map features for frontal face recognition under extreme illumination changes

Our aim in this paper is to robustly match frontal faces in the presence of extreme illumination changes, using only a single training image per person and a single probe image. In the illumination conditions we consider, which include those with the dominant light source placed behind and to the side of the user, directly above and pointing downwards or indeed below and pointing upwards, this is a most challenging problem. The presence of sharp cast shadows, large poorly illuminated regions of the face, quantum and quantization noise and other nuisance effects, makes it difficult to extract a sufficiently discriminative yet robust representation. We introduce a representation which is based on image gradient directions near robust edges which correspond to characteristic facial features. Robust edges are extracted using a cascade of processing steps, each of which seeks to harness further discriminative information or normalize for a particular source of extra-personal appearance variability. The proposed representation was evaluated on the extremely difficult YaleB data set. Unlike most of the previous work we include all available illuminations, perform training using a single image per person and match these also to a single probe image. In this challenging evaluation setup, the proposed gradient edge map achieved 0.8% error rate, demonstrating a nearly perfect receiver-operator characteristic curve behaviour. This is by far the best performance achieved in this setup reported in the literature, the best performing methods previously proposed attaining error rates of approximately 6–7%.

A new look at filtering techniques for illumination invariance in automatic face recognition

Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we propose a novel, general recognition framework for efficient matching of individual face images, sets or sequences. The framework is based on simple image processing filters that compete with unprocessed greyscale input to yield a single matching score between individuals. It is shown how the discrepancy between illumination conditions between novel input and the training data set can be estimated and used to weigh the contribution of two competing representations. We describe an extensive empirical evaluation of the proposed method on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our algorithm consistently demonstrated a dramatic performance improvement over traditional filtering approaches. We demonstrate a reduction of 50-75% in recognition error rates, the best performing method-filter combination correctly recognizing 96% of the individuals.

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.

Characterization and prediction of issue-related risks in software projects

Identifying risks relevant to a software project and planning measures to deal with them are critical to the success of the project. Current practices in risk assessment mostly rely on high-level, generic guidance or the subjective judgements of experts. In this paper, we propose a novel approach to risk assessment using historical data associated with a software project. Specifically, our approach identifies patterns of past events that caused project delays, and uses this knowledge to identify risks in the current state of the project. A set of risk factors characterizing “risky” software tasks (in the form of issues) were extracted from five open source projects: Apache, Duraspace, JBoss, Moodle, and Spring. In addition, we performed feature selection using a sparse logistic regression model to select risk factors with good discriminative power. Based on these risk factors, we built predictive models to predict if an issue will cause a project delay. Our predictive models are able to predict both the risk impact (i.e. the extend of the delay) and the likelihood of a risk occurring. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 48%-81% precision, 23%-90% recall, 29%-71% F-measure, and 70%-92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39-0.75 for Macro-averaged Mean Cost-Error and 0.7-1.2 for Macro-averaged Mean Absolute Error.

Forecasting inflation, industrial output and exchange rates : a template study for India

This paper reports results from a forecasting study for inflation, industrial output and exchange rates for India. We cannot reject the null hypothesis for linearity for all series used except for the growth rate of the foreign exchange series and our analysis is based on linear models, ARIMA and bivariate transfer functions and restricted VAR. Forecasting performance is evaluated using the models’ root mean-squared error differences and Theil’s inequality coefficients from recursive origin static, fixed origin dynamic and rolling origin dynamic forecasts. For models based on weekly data, based on RMSEs, we find that the bivariate models improve upon the forecasts of the ARIMA model while for models based on monthly data the ARIMA model has almost always better performance. In choosing between the two bivariate models on the basis of RMSEs, our overall results tend to support the use of a restricted VAR, as this model had the best forecasting performance more frequently than the transfer function model.

Crime rates, male youth unemployment and real income in Australia: evidence from Granger causality tests

This article applies Granger causality tests to examine the relationship between seven different categories of property crime and violent crime against the person, male youth unemployment and real male average weekly earnings in Australia from 1964 to 2001 within a cointegration and vector error correction framework. It is found that fraud, homicide and motor vehicle theft are cointegrated with male youth unemployment and real male average weekly earnings. However, there is no evidence of a long-run relationship between either break and enter, robbery, serious assault or stealing with male youth unemployment and real male average weekly earnings.

Forecasting construction demand : a vector error correction model with dummy variables

Modelling the level of demand for construction is vital in policy formulation and implementation as the construction industry plays an important role in a country’s economic development process. In construction economics, research efforts on construction demand modelling and forecasting are various, but few researchers have considered the impact of global economy events in construction demand modelling. An advanced multivariate modelling technique, namely the vector error correction (VEC) model with dummy variables, was adopted to predict demand in the Australian construction market. The results of prediction accuracy tests suggest that the general VEC model and the VEC model with dummy variables are both acceptable for forecasting construction economic indicators. However, the VEC model that considers external impacts achieves higher prediction accuracy than the general VEC model. The model estimates indicate that the growth in population, changes in national income, fluctuations in interest rates and changes in householder expenditure all play significant roles when explaining variations in construction demand. The VEC model with disturbances developed can serve as an experimentation using an advanced econometrical method which can be used to analyse the effect of specific events or factors on the construction market growth.

Use of respiration rates of scyphozoan jellyfish to estimate their effects on the food web

One of the main objectives of research on jellyfish is to determine their effects on the food web. They are voracious consumers that have similar diets to those of zooplanktivorous fish, as well as eating microplankton and ichthyoplankton. Respiration rates (RRs) can be used to estimate the amount of food needed to balance metabolism, and thereby estimate minimum ingestion. We compiled RRs for scyphozoan medusae in three suborders (Semeaostomeae, Rhizostomeae, and Coronatae) to determine if a single regression could relate RRs to mass for diverse scyphomedusan species. Temperature (7–30°C) was not a significant factor. RRs versus wet weight (WW) regressions differed significantly for semeaostome and rhizostome medusae; however, RRs versus carbon mass over five-orders of magnitude did not differ significantly among suborders. RRs were isometric against medusa carbon mass, with data for all species scaling to the power 0.94. The scyphomedusa respiration rate (SRR) regression enables estimation of RR for any scyphomedusa from its carbon mass. The error of the SRR regression was ±72%, which is small in comparison with the 1,000-fold variation in field sampling. This predictive equation (RR in ml O2 d−1 = 83.37 * g C0.940) can be used to estimate minimum ingestion by scyphomedusae without exhaustive collection of feeding data. In addition, effects of confinement on RRs during incubation of medusae were tested. Large medusae incubated in small container volumes (CV) relative to their size (ratios of CV:WW < 50) had RRs ~one-tenth those of medusae in relatively larger containers. Depleted oxygen during incubation did not depress RRs of the medusae; however, swimming may have been restricted and respiration reduced in consequence. We briefly review other problems with RR experiments and suggest protocols and limitations for estimating ingestion rates of jellyfish from metabolic rates.

A panel vector error correction approach to forecasting demand in regional construction markets

Reliable forecasting as to the level of aggregate demand for construction is of vital importance to developers, builders and policymakers. Previous construction demand forecasting studies mainly focused on temporal estimating using national aggregate data. The construction market can be better represented by a group of interconnected regions or local markets rather than a national aggregate, and yet regional forecasting techniques have rarely been applied. Furthermore, limited research has applied regional variations in construction markets to construction demand modelling and forecasting. A new comprehensive method is used, a panel vector error correction approach, to forecast regional construction demand using Australia’s state-level data. The links between regional construction demand and general economic indicators are investigated by panel cointegration and causality analysis. The empirical results suggest that both long-run and causal links are found between regional construction demand and construction price, state income, population, unemployment rates and interest rates. The panel vector error correction model can provide reliable and robust forecasting with less than 10% of the mean absolute percentage error for a medium-term trend of regional construction demand and outperforms the conventional forecasting models (panel multiple regression and time series multiple regression model). The key macroeconomic factors of construction demand variations across regions in Australia are also presented. The findings and robust econometric techniques used are valuable to construction economists in examining future construction markets at a regional level.