Cost effectiveness of nutrition support in the prevention of pressure ulcer in hospitals

Background/objectives This study estimates the economic outcomes of a nutrition intervention to at-risk patients compared with standard care in the prevention of pressure ulcer. Subjects/methods Statistical models were developed to predict ‘cases of pressure ulcer avoided’, ‘number of bed days gained’ and ‘change to economic costs’ in public hospitals in 2002–2003 in Queensland, Australia. Input parameters were specified and appropriate probability distributions fitted for: number of discharges per annum; incidence rate for pressure ulcer; independent effect of pressure ulcer on length of stay; cost of a bed day; change in risk in developing a pressure ulcer associated with nutrition support; annual cost of the provision of a nutrition support intervention for at-risk patients. A total of 1000 random re-samples were made and the results expressed as output probability distributions. Results The model predicts a mean 2896 (s.d. 632) cases of pressure ulcer avoided; 12 397 (s.d. 4491) bed days released and corresponding mean economic cost saving of euros 2 869 526 (s.d. 2 078 715) with a nutrition support intervention, compared with standard care. Conclusion Nutrition intervention is predicted to be a cost-effective approach in the prevention of pressure ulcer in at-risk patients.

An ingenuous Decision Support System (iDSS) approach for remote area pregnant women

This paper addresses development of an ingenious decision support system (iDSS) based on the methodology of survey instruments and identification of significant variables to be used in iDSS using statistical analysis. A survey was undertaken with pregnant women and factorial experimental design was chosen to acquire sample size. Variables with good reliability in any one of the statistical techniques such as Chi-square, Cronbach’s α and Classification Tree were incorporated in the iDSS. The ingenious decision support system was implemented with Visual Basic as front end and Microsoft SQL server management as backend. Outcome of the ingenious decision support system include advice on Symptoms, Diet and Exercise to pregnant women.

Rail incident investigator training: Is there support for a standardised Australasian training framework?

Background: Historically rail organisations have been operating in silos and devising their own training agendas. However with the harmonisation of the Australian workplace health and safety legislation and the appointment of a national rail safety regulator in 2013, rail incident investigator experts are exploring the possibility of developing a unified approach to investigator training. Objectives: The Australian CRC for Rail Innovation commissioned a training needs analysis to identify if common training needs existed between organisations and to assess support for the development of a national competency framework for rail incident investigations. Method: Fifty-two industry experts were consulted to explore the possibility of the development of a standardised training framework. These experts were sourced from within 19 Australasian organisations, comprising Rail Operators and Regulators in Queensland, New South Wales, Victoria, Western Australia, South Australia and New Zealand. Results: Although some competency requirements appear to be organisation specific, the vast majority of reported training requirements were generic across the Australasian rail operators and regulators. Industry experts consistently reported strong support for the development of a national training framework. Significance: The identification of both generic training requirements across organisations and strong support for standardised training indicates that the rail industry is receptive to the development of a structured training framework. The development of an Australasian learning framework could: increase efficiency in course development and reduce costs; establish recognised career pathways; and facilitate consistency with regards to investigator training.

Information support for health management in regional Sri Lanka : health managers’ perspectives

Good management, supported by accurate, timely and reliable health information, is vital for increasing the effectiveness of Health Information Systems (HIS). When it comes to managing the under resourced health systems of developing countries, information-based decision making is particularly important. This paper reports findings of a self-report survey that investigated perceptions of local health managers (HMs) of their own regional HIS in Sri Lanka. Data were collected through a validated, pre-tested postal questionnaire, and distributed among a selected group of HMs to elicit their perceptions of the current HIS in relation to information generation, acquisition and use, required reforms to the information system and application of information and communication technology (ICT). Results based on descriptive statistics indicated that the regional HIS was poorly organised and in need of reform; that management support for the system was unsatisfactory in terms of relevance, accuracy, timeliness and accessibility; that political pressure and community and donor requests took precedence over vital health information when management decisions were made; and use of ICT was unsatisfactory. HIS strengths included user-friendly paper formats, a centralised planning system and an efficient disease notification system; weaknesses were lack of comprehensiveness, inaccuracy, and lack of a feedback system. Responses of participants indicated that HIS would be improved by adopting an internationally accepted framework and introducing ICT applications. Perceived barriers to such improvements were high initial cost of educating staff to improve computer literacy, introduction of ICTs, and HIS restructure. We concluded that the regional HIS of Central Province, Sri Lanka had failed to provide much needed information support to HMs. These findings are consistent with similar research in other developing countries and reinforce the need for further research to verify causes of poor performance and to design strategic reforms to improve HIS in regional Sri Lanka.

Spatio temporal feature evaluation for action recognition

Spatio-Temporal interest points are the most popular feature representation in the field of action recognition. A variety of methods have been proposed to detect and describe local patches in video with several techniques reporting state of the art performance for action recognition. However, the reported results are obtained under different experimental settings with different datasets, making it difficult to compare the various approaches. As a result of this, we seek to comprehensively evaluate state of the art spatio- temporal features under a common evaluation framework with popular benchmark datasets (KTH, Weizmann) and more challenging datasets such as Hollywood2. The purpose of this work is to provide guidance for researchers, when selecting features for different applications with different environmental conditions. In this work we evaluate four popular descriptors (HOG, HOF, HOG/HOF, HOG3D) using a popular bag of visual features representation, and Support Vector Machines (SVM)for classification. Moreover, we provide an in-depth analysis of local feature descriptors and optimize the codebook sizes for different datasets with different descriptors. In this paper, we demonstrate that motion based features offer better performance than those that rely solely on spatial information, while features that combine both types of data are more consistent across a variety of conditions, but typically require a larger codebook for optimal performance.

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.