Contact electrification and adhesion between dissimilar materials

Simultaneous measurements of surface force and surface charge demonstrate strong attraction due to the spontaneous transfer of electrical charge from one smooth insulator (mica) to another (silica) as a result of simple, nonsliding contact in dry nitrogen. The measured surface charge densities are 5 to 20 millicoulombs per square meter after contact. The work required to separate the charged surfaces is typically 6 to 9 joules per square meter, comparable to the fracture energies of ionic-covalent materials. Observation of partial gas discharges when the surfaces are approximately 1 micrometer apart gives valuable insight into the charge separation processes underlying static electrical phenomena in general.

Application of a hybrid MBR system to treat herbicides from agricultural runoffs

Agricultural discharge of herbicides to the Great Barrier Reef (GBR) poses significant threat to the marine ecosystem. This study evaluates the performance of a hybrid treatment system consists of a membrane bioreactor (MBR), UV disinfection unit and a granular activated carbon (GAC) column in treating Ametryn which is one of the major herbicides in agricultural discharges. While the MBR alone removes only 40% of Ametryn at a hydraulic retention time of 7.8 hours, the hybrid system removed Ametryn to below detection levels.

Field evaluation of a suite of biomarkers in an Australian Tropical Reef Species, Stripey Seaperch (Lutjanus carponotatus) : Assessment of Produced Formation water from the Harriet A Platform

There is paucity of data regarding hydrocarbon exposure of tropical fish species inhabiting the waters near oil and gas platforms on the Northwest Shelf of Australia. A comprehensive field study assessed the exposure and potential effects associated with the produced water (PW) plume from the Harriet A production platform on the northwest shelf in a local reef species, Stripey seaperch (Lutjanus carponotatus). This field study was a continuation of an earlier pilot study which concluded that there were “warning signs” of potential biological effects on fish populations exposed to PW. A 10-day field caging study was conducted deploying 15 individual fish into 6 separate steel cages set 1-m subsurface at 3 stations in a concentration gradient moving away from the platform. A battery of biomarkers were evaluated including hepatosomatic index (HSI), total cytochrome P450, bile metabolites, CYP1A-, CYP2K- and CYP2M-like proteins, cholinesterase (ChE) activity, and histopathology of liver and gill tissues. Water column and PW effluent samples was also collected. Results confirmed that PAH metabolites in bile, CYP1A-, CYP2K-, and CYP2M-like proteins and liver histopathology provided evidence of significant exposure and effects after 10 days at the near-field site (~200 m off the Harriet A platform). Hepatosomatic index, total cytochrome P450, and ChE did not provide site-specific differences by day 10 of exposure to PW. CYP proteins were shown by principal component analysis (PCA) to be the best diagnostic tool for determining exposure and associated biological effects of PW on L. carponotatus. Using a suite of biomarkers has been widely advocated as a vital component in environmental risk assessments worldwide. This study demonstrates the usefulness of biomarkers for assessing the Harriet A PW discharge into Australian waters with broader applications for other PW discharges. This approach has merit as a valuable addition to environmental management strategies for protecting Australia’s tropical environment and its rich biodiversity.

Treatment of ametryn in wastewater by a hybrid MBR system : a lab-scale study

Agricultural discharge of herbicides to the Great Barrier Reef (GBR) poses significant threat to the marine ecosystem. This study evaluates the performance of a hybrid treatment system consists of a membrane bioreactor (MBR), UV disinfection unit and a granular activated carbon (GAC) column in treating ametryn which is one of the major herbicides in agricultural discharges. While the MBR alone removes only 40% of ametryn at a hydraulic retention time of 7.8 h, the hybrid system removed ametryn to below detection levels.

Reduced emergency calls and improved weekend discharge after introduction of a new electronic handover system

Objectives: To measure the frequency and content of electronic handover before and after implementation of the Blue BARRWUE handover system, and to measure its effect on patient safety and hospital efficiency over weekends.

Design, setting and participants: Point-prevalence study comparing outcomes for general medical inpatients present over weekends before implementation (1 May 2008 to 30 April 2009) and after implementation (1 May 2009 to 30 April 2010) of the Blue BARRWUE handover system at Geelong Hospital.

Intervention: Implementation of the Blue BARRWUE handover system and its components (updated working diagnosis, background, alerts, resuscitation status, requests, who to do what and when, updates and executable discharge plan).
Main outcome measures: Presence of any written handover notes or updated working diagnoses in the BOSSnet clinical information system, content of handover notes, frequency of weekend discharges and medical emergency team (MET) calls before and after implementation.

Results: In the 12 months before implementation of the Blue BARRWUE handover system, 976 patients (47.98%) had a handover note in BOSSnet, versus 1646 patients (95.09%) in the 12 months after implementation (P< 0.001; rate ratio [RR], 20.75; 95% CI, 16.33–26.44). Before implementation, 289 patients (14.21%) were discharged over weekends, versus 353 patients (20.39%) after implementation, (P < 0.001; RR, 1.44; 95% CI, 1.25–1.65). MET calls were made for 152 general medical patients before implementation (7.47%), versus 95 general medical patients (5.49%) after implementation (P= 0.01; RR, 0.73; 95% CI, 0.57–0.94).

Conclusions: The Blue BARRWUE system has sustainably improved written handover in our organisation and was associated with improvement in both patient safety and hospital efficiency.

Exploring in-hospital adverse drug events using ICD-10 codes

 Abstract
Objective Adverse drug events (ADEs) during hospital admissions are a widespread problem associated with adverse patient outcomes. The ‘external cause’ codes in the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) provide opportunities for identifying the incidence of ADEs acquired during hospital stays that may assist in targeting interventions to decrease their occurrence. The aim of the present study was to use routine administrative data to identify ADEs acquired during hospital admissions in a suburban healthcare network in Melbourne, Australia.

Methods Thirty-nine secondary diagnosis fields of hospital discharge data for a 1-year period were reviewed for ‘diagnoses not present on admission’ and assigned to the Classification of Hospital Acquired Diagnoses (CHADx) subclasses. Discharges with one or more ADE subclass were extracted for retrospective analysis.

Results From 57 205 hospital discharges, 7891 discharges (13.8%) had at least one CHADx, and 402 discharges (0.7%) had an ADE recorded. The highest proportion of ADEs was due to administration of analgesics (27%) and systemic antibiotics (23%). Other major contributors were anticoagulation (13%), anaesthesia (9%) and medications with cardiovascular side-effects (9%).

Conclusion Hospital data coded in ICD-10 can be used to identify ADEs that occur during hospital stays and also clinical conditions, therapeutic drug classes and treating units where these occur. Using the CHADx algorithm on administrative datasets provides a consistent and economical method for such ADE monitoring.

What is known about the topic? Adverse drug events (ADEs) can result in several different physical consequences, ranging from allergic reactions to death, thereby posing a significant burden on patients and the health system. Numerous studies have compared manual, written incident reporting systems used by hospital staff with computerised automated systems to identify ADEs acquired during hospital admissions. Despite various approaches aimed at improving the detection of ADEs, they remain under-reported, as a result of which interventions to mitigate the effect of ADEs cannot be initiated effectively.

What does this paper add? This research article demonstrates major methodological advances over comparable published studies looking at the effectiveness of using routine administrative data to monitor rates of ADEs that occur during a hospital stay and reviews the type of ADEs and their frequency patterns during patient admission. It also provides an insight into the effect of ADEs that occur within different hospital treating units. The method implemented in this study is unique because it uses a grouping algorithm developed for the Australian Commission on Safety and Quality in Health Care (ACSQHC) to identify ADEs not present on admission from patient data coded in ICD-10. This algorithm links the coded external causes of ADEs with their consequences or manifestations. ADEs identified through the use of programmed code based on this algorithm have not been studied in the past and therefore this paper adds to previous knowledge in this subject area.

What are the implications for health professionals? Although not all ADEs can be prevented with current medical knowledge, this study can assist health professionals in targeting interventions that can efficiently reduce the rate of ADEs that occur during a hospital stay, and improve information available for future medication management decisions.

Forecasting daily patient outflow from a ward having no real-time clinical data

OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data.

METHODS: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features.

RESULTS: Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014.

CONCLUSIONS: In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments.