The use of patient experience survey data by out-of-hours primary care services: a qualitative interview study

Background English National Quality Requirements mandate out-of-hours primary care services to routinely audit patient experience, but do not state how it should be done.

Objectives We explored how providers collect patient feedback data and use it to inform service provision. We also explored staff views on the utility of out-of-hours questions from the English General Practice Patient Survey (GPPS).

Methods A qualitative study was conducted with 31 staff (comprising service managers, general practitioners and administrators) from 11 out-of-hours primary care providers in England, UK. Staff responsible for patient experience audits within their service were sampled and data collected via face-to-face semistructured interviews.

Results Although most providers regularly audited their patients’ experiences by using patient surveys, many participants expressed a strong preference for additional qualitative feedback. Staff provided examples of small changes to service delivery resulting from patient feedback, but service-wide changes were not instigated. Perceptions that patients lacked sufficient understanding of the urgent care system in which out-of-hours primary care services operate were common and a barrier to using feedback to enable change. Participants recognised the value of using patient experience feedback to benchmark services, but perceived weaknesses in the out-of-hours items from the GPPS led them to question the validity of using these data for benchmarking in its current form.

Conclusions The lack of clarity around how out-of-hours providers should audit patient experience hinders the utility of the National Quality Requirements. Although surveys were common, patient feedback data had only a limited role in service change. Data derived from the GPPS may be used to benchmark service providers, but refinement of the out-of-hours items is needed.

A constructive approach for examination timetabling based on adaptive decomposition and ordering

In this study, we investigate an adaptive decomposition and ordering strategy that automatically divides examinations into difficult and easy sets for constructing an examination timetable. The examinations in the difficult set are considered to be hard to place and hence are listed before the ones in the easy set in the construction process. Moreover, the examinations within each set are ordered using different strategies based on graph colouring heuristics. Initially, the examinations are placed into the easy set. During the construction process, examinations that cannot be scheduled are identified as the ones causing infeasibility and are moved forward in the difficult set to ensure earlier assignment in subsequent attempts. On the other hand, the examinations that can be scheduled remain in the easy set.

Within the easy set, a new subset called the boundary set is introduced to accommodate shuffling strategies to change the given ordering of examinations. The proposed approach, which incorporates different ordering and shuffling strategies, is explored on the Carter benchmark problems. The empirical results show that the performance of our algorithm is broadly comparable to existing constructive approaches.

Defining consensus norms for palliative care of people with intellectual disabilities in Europe using Delphi methods: A White Paper from the European Association of Palliative care

Background: People with intellectual disabilities often present with unique challenges that make it more difficult to meet their
palliative care needs.
Aim: To define consensus norms for palliative care of people with intellectual disabilities in Europe.
Design: Delphi study in four rounds: (1) a taskforce of 12 experts from seven European countries drafted the norms, based on available empirical knowledge and regional/national guidelines; (2) using an online survey, 34 experts from 18 European countries evaluated the draft norms, provided feedback and distributed the survey within their professional networks. Criteria for consensus
were clearly defined; (3) modifications and recommendations were made by the taskforce; and (4) the European Association for
Palliative Care reviewed and approved the final version.
Setting and participants: Taskforce members: identified through international networking strategies. Expert panel: a purposive sample identified through taskforce members’ networks.
Results: A total of 80 experts from 15 European countries evaluated 52 items within the following 13 norms: equity of access, communication, recognising the need for palliative care, assessment of total needs, symptom management, end-of-life decision making, involving those who matter, collaboration, support for family/carers, preparing for death, bereavement support, education/training
and developing/managing services. None of the items scored less than 86% agreement, making a further round unnecessary. In light of respondents’ comments, several items were modified and one item was deleted.
Conclusion: This White Paper presents the first guidance for clinical practice, policy and research related to palliative care for people with intellectual disabilities based on evidence and European consensus, setting a benchmark for changes in policy and practice.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

Improving a regional chemotherapy service by learning from deaths occurring within 30 days of treatment with systemic anti-cancer therapy.

Background: Outwith clinical trials, patient outcomes specifically related to SACT (systemic anti-cancer therapy) are not well reported despite a significant proportion of patients receiving active treatment at the end of life. The NCEPOD reviewing deaths within 30 days of SACT found SACT caused or hastened death in 27% of cases.

Method: Across the Northern Ireland cancer network, 95 patients who died within 30 days of SACT for solid tumours were discussed at the Morbidity and Mortality monthly meeting during 2013. Using a structured template, each case was independently reviewed, with particular focus on whether SACT caused or hastened death.

Results: Lung, GI and breast cancers were the most common sites. Performance status was recorded in 92% at time of final SACT cycle (ECOG PS 0-2 89%).

In 57% the cause of death was progressive disease. Other causes included thromboembolism (13%) and infection (5% neutropenic sepsis, 6% non-neutropenic sepsis). In 26% with death from progressive disease, the patient was first cycle of first line treatment for metastatic disease. In the majority discussion regarding treatment aims and risks was documented. Only one patient was receiving SACT with curative intent, who died from appropriately managed neutropenic sepsis.

A definitive decision regarding SACT's role in death was made in 60%: in 49% SACT was deemed non-contributory and in 11% SACT was deemed the cause of death. In 40% SACT did not play a major role, but a definitive negative association could not be made.

Conclusion: Development of a robust review process of 30-day mortality after SACT established a benchmark for SACT delivery for future comparisons and identified areas for SACT service organisation improvement. Moreover it encourages individual practice reflection and highlights the importance of balancing patients' needs and concerns with realistic outcomes and risks, particularly in heavily pre-treated patients or those of poor performance status.

Clustering-Based Ensemble Method as an Alternative to Stacking

One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.

Scaling Up Market Anomalies

This paper implements momentum among a host of market anomalies. Our investment universe consists of the 15 top (long-leg) and 15 bottom (short-leg) anomaly portfolios. The proposed active strategy buys (sells short) a subset of the top (bottom) anomaly portfolios based on past one-month return. The evidence shows statistically strong and economically meaningful persistence in anomaly payoffs. Our strategy consistently outperforms a naive benchmark that equal weights anomalies and yields an abnormal monthly return ranging between 1.27% and 1.47%. The persistence is robust to the post-2000 period, and various other considerations, and is stronger following episodes of high investor sentiment.

Container-based Cloud Virtual Machine Benchmarking

With the availability of a wide range of cloud Virtual Machines (VMs) it is difficult to determine which VMs can maximise the performance of an application. Benchmarking is commonly used to this end for capturing the performance of VMs. Most cloud benchmarking techniques are typically heavyweight - time consuming processes which have to benchmark the entire VM in order to obtain accurate benchmark data. Such benchmarks cannot be used in real-time on the cloud and incur extra costs even before an application is deployed.

In this paper, we present lightweight cloud benchmarking techniques that execute quickly and can be used in near real-time on the cloud. The exploration of lightweight benchmarking techniques are facilitated by the development of DocLite - Docker Container-based Lightweight Benchmarking. DocLite is built on the Docker container technology which allows a user-defined portion (such as memory size and the number of CPU cores) of the VM to be benchmarked. DocLite operates in two modes, in the first mode, containers are used to benchmark a small portion of the VM to generate performance ranks. In the second mode, historic benchmark data is used along with the first mode as a hybrid to generate VM ranks. The generated ranks are evaluated against three scientific high-performance computing applications. The proposed techniques are up to 91 times faster than a heavyweight technique which benchmarks the entire VM. It is observed that the first mode can generate ranks with over 90% and 86% accuracy for sequential and parallel execution of an application. The hybrid mode improves the correlation slightly but the first mode is sufficient for benchmarking cloud VMs.

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints. Evaluation on a dual-socket 16-core Intel platform using 9 benchmark kernels shows that the proposed framework picks the optimal configuration with high accuracy. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.

A Hybrid Swarm Based Approach in Addressing University Timetabling Problems

This paper is concerned with the application of an automated hybrid approach in addressing the university timetabling problem. The approach described is based on the nature-inspired artificial bee colony (ABC) algorithm. An ABC algorithm is a biologically-inspired optimization approach, which has been widely implemented in solving a range of optimization problems in recent years such as job shop scheduling and machine timetabling problems. Although the approach has proven to be robust across a range of problems, it is acknowledged within the literature that there currently exist a number of inefficiencies regarding the exploration and exploitation abilities. These inefficiencies can often lead to a slow convergence speed within the search process. Hence, this paper introduces a variant of the algorithm which utilizes a global best model inspired from particle swarm optimization to enhance the global exploration ability while hybridizing with the great deluge (GD) algorithm in order to improve the local exploitation ability. Using this approach, an effective balance between exploration and exploitation is attained. In addition, a traditional local search approach is incorporated within the GD algorithm with the aim of further enhancing the performance of the overall hybrid method. To evaluate the performance of the proposed approach, two diverse university timetabling datasets are investigated, i.e., Carter's examination timetabling and Socha course timetabling datasets. It should be noted that both problems have differing complexity and different solution landscapes. Experimental results demonstrate that the proposed method is capable of producing high quality solutions across both these benchmark problems, showing a good degree of generality in the approach. Moreover, the proposed method produces best results on some instances as compared with other approaches presented in the literature.

A Hybrid Imperialist Approach in Solving University Timetabling Problems. Information Sciences. Vol. 283, 1-21

Generating timetables for an institution is a challenging and time consuming task due to different demands on the overall structure of the timetable. In this paper, a new hybrid method which is a combination of a great deluge and artificial bee colony algorithm (INMGD-ABC) is proposed to address the university timetabling problem. Artificial bee colony algorithm (ABC) is a population based method that has been introduced in recent years and has proven successful in solving various optimization problems effectively. However, as with many search based approaches, there exist weaknesses in the exploration and exploitation abilities which tend to induce slow convergence of the overall search process. Therefore, hybridization is proposed to compensate for the identified weaknesses of the ABC. Also, inspired from imperialist competitive algorithms, an assimilation policy is implemented in order to improve the global exploration ability of the ABC algorithm. In addition, Nelder–Mead simplex search method is incorporated within the great deluge algorithm (NMGD) with the aim of enhancing the exploitation ability of the hybrid method in fine-tuning the problem search region. The proposed method is tested on two differing benchmark datasets i.e. examination and course timetabling datasets. A statistical analysis t-test has been conducted and shows the performance of the proposed approach as significantly better than basic ABC algorithm. Finally, the experimental results are compared against state-of-the art methods in the literature, with results obtained that are competitive and in certain cases achieving some of the current best results to those in the literature.

DocLite: A Docker-Based Lightweight Cloud Benchmarking Tool

Existing benchmarking methods are time consuming processes as they typically benchmark the entire Virtual Machine (VM) in order to generate accurate performance data, making them less suitable for real-time analytics. The research in this paper is aimed to surmount the above challenge by presenting DocLite - Docker Container-based Lightweight benchmarking tool. DocLite explores lightweight cloud benchmarking methods for rapidly executing benchmarks in near real-time. DocLite is built on the Docker container technology, which allows a user-defined memory size and number of CPU cores of the VM to be benchmarked. The tool incorporates two benchmarking methods - the first referred to as the native method employs containers to benchmark a small portion of the VM and generate performance ranks, and the second uses historic benchmark data along with the native method as a hybrid to generate VM ranks. The proposed methods are evaluated on three use-cases and are observed to be up to 91 times faster than benchmarking the entire VM. In both methods, small containers provide the same quality of rankings as a large container. The native method generates ranks with over 90% and 86% accuracy for sequential and parallel execution of an application compared against benchmarking the whole VM. The hybrid method did not improve the quality of the rankings significantly.

Dynamic State Estimation of Power Systems with Quantization Eects: A Recursive Filter Approach

In this paper, a recursive filter algorithm is developed to deal with the state estimation problem for power systems with quantized nonlinear measurements. The measurements from both the remote terminal units and the phasor measurement unit are subject to quantizations described by a logarithmic quantizer. Attention is focused on the design of a recursive filter such that, in the simultaneous presence of nonlinear measurements and quantization effects, an upper bound for the estimation error covariance is guaranteed and subsequently minimized. Instead of using the traditional approximation methods in nonlinear estimation that simply ignore the linearization errors, we treat both the linearization and quantization errors as norm-bounded uncertainties in the algorithm development so as to improve the performance of the estimator. For the power system with such kind of introduced uncertainties, a filter is designed in the framework of robust recursive estimation, and the developed filter algorithm is tested on the IEEE benchmark power system to demonstrate its effectiveness.

Inexact-aware architecture design for ultra-low power bio-signal analysis

This study introduces an inexact, but ultra-low power, computing architecture devoted to the embedded analysis of bio-signals. The platform operates at extremely low voltage supply levels to minimise energy consumption. In this scenario, the reliability of static RAM (SRAM) memories cannot be guaranteed when using conventional 6-transistor implementations. While error correction codes and dedicated SRAM implementations can ensure correct operations in this near-threshold regime, they incur in significant area and energy overheads, and should therefore be employed judiciously. Herein, the authors propose a novel scheme to design inexact computing architectures that selectively protects memory regions based on their significance, i.e. their impact on the end-to-end quality of service, as dictated by the bio-signal application characteristics. The authors illustrate their scheme on an industrial benchmark application performing the power spectrum analysis of electrocardiograms. Experimental evidence showcases that a significance-based memory protection approach leads to a small degradation in the output quality with respect to an exact implementation, while resulting in substantial energy gains, both in the memory and the processing subsystem.

Electron-impact ionization of the metastable excited states of Li +

Electron-impact ionization cross sections for the 1s2s 1S and 1s2s 3S metastable states of Li+ are calculated using both perturbative distorted-wave and non-perturbative close-coupling methods. Term-resolved distorted-wave calculations are found to be approximately 15% above term-resolved R-matrix with pseudostates calculations. On the other hand, configuration-average time-dependent close-coupling calculations are found to be in excellent agreement with the configuration-average R-matrix with pseudostates calculations. The non-perturbative R-matrix and close-coupling calculations provide a benchmark for experimental studies of electron-impact ionization of metastable states along the He isoelectronic sequence.