Nursing physical assessment for patient safety in general wards: Reaching consensus on core skills

Aims and objectives To determine consensus across acute care specialty areas on core physical assessment skills necessary for early recognition of changes in patient status in general wards. Background Current approaches to physical assessment are inconsistent and have not evolved to meet increased patient and system demands. New models of nursing assessment are needed in general wards that ensure a proactive and patient safety approach. Design A modified Delphi study. Methods Focus group interviews with 150 acute care registered nurses (RNs) at a large tertiary referral hospital generated a framework of core skills that were developed into a web-based survey. We then sought consensus with a panel of 35 senior acute care RNs following a classical Delphi approach over three rounds. Consensus was predefined as at least 80% agreement for each skill across specialty areas. Results Content analysis of focus group transcripts identified 40 discrete core physical assessment skills. In the Delphi rounds, 16 of these were consensus validated as core skills and were conceptually aligned with the primary survey: (Airway) Assess airway patency; (Breathing) Measure respiratory rate, Evaluate work of breathing, Measure oxygen saturation; (Circulation) Palpate pulse rate and rhythm, Measure blood pressure by auscultation, Assess urine output; (Disability) Assess level of consciousness, Evaluate speech, Assess for pain; (Exposure) Measure body temperature, Inspect skin integrity, Inspect and palpate skin for signs of pressure injury, Observe any wounds, dressings, drains and invasive lines, Observe ability to transfer and mobilise, Assess bowel movements. Conclusions Among a large and diverse group of experienced acute care RNs consensus was achieved on a structured core physical assessment to detect early changes in patient status. Relevance to clinical practice Although further research is needed to refine the model, clinical application should promote systematic assessment and clinical reasoning at the bedside.

Recognition and enforcement in cross-border insolvency law: A proposal for judicial gap-filling

This Article analyzes the recognition and enforcement of cross-border insolvency judgments from the United States, United Kingdom, and Australia to determine whether the UNCITRAL Model Law’s goal of modified universalism is currently being practiced, and subjects the Model Law to analysis through the lens of international relations theories to elaborate a way forward. We posit that courts could use the express language of the Model Law text to confer recognition and enforcement of foreign insolvency judgments. The adoption of our proposal will reduce costs, maximize recovery for creditors, and ensure predictability for all parties.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

Human action recognition from video sequences

This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.

Elliptical summary randomisation for sensor-based human activity recognition

Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.