Understanding and responding when things go wrong: key principles for primary care educators

Learning from events with unwanted outcomes is an important part of workplace based education and providing evidence for medical appraisal and revalidation. It has been suggested that adopting a ‘systems approach’ could enhance learning and effective change. We believe the following key principles should be understood by all healthcare staff, especially those with a role in developing and delivering educational content for safety and improvement in primary care. When things go wrong, professional accountability involves accepting there has been a problem, apologising if necessary and committing to learn and change. This is easier in a ‘Just Culture’ where wilful disregard of safe practice is not tolerated but where decisions commensurate with training and experience do not result in blame and punishment. People usually attempt to achieve successful outcomes, but when things go wrong the contribution of hindsight and attribution bias as well as a lack of understanding of conditions and available information (local rationality) can lead to inappropriately blame ‘human error’. System complexity makes reduction into component parts difficult; thus attempting to ‘find-and-fix’ malfunctioning components may not always be a valid approach. Finally, performance variability by staff is often needed to meet demands or cope with resource constraints. We believe understanding these core principles is a necessary precursor to adopting a ‘systems approach’ that can increase learning and reduce the damaging effects on morale when ‘human error’ is blamed. This may result in ‘human error’ becoming the starting point of an investigation and not the endpoint

Understanding patient safety performance and educational needs using the ‘Safety-II’ approach for complex systems

Participation in projects to improve patient safety is a key component of general practice (GP) specialty training, appraisal and revalidation. Patient safety training priorities for GPs at all career stages are described in the Royal College of General Practitioners’ curriculum. Current methods that are taught and employed to improve safety often use a ‘find-and-fix’ approach to identify components of a system (including humans) where performance could be improved. However, the complex interactions and inter-dependence between components in healthcare systems mean that cause and effect are not always linked in a predictable manner. The Safety-II approach has been proposed as a new way to understand how safety is achieved in complex systems that may improve quality and safety initiatives and enhance GP and trainee curriculum coverage. Safety-II aims to maximise the number of events with a successful outcome by exploring everyday work. Work-as-done often differs from work-as-imagined in protocols and guidelines and various ways to achieve success, dependent on work conditions, may be possible. Traditional approaches to improve the quality and safety of care often aim to constrain variability but understanding and managing variability may be a more beneficial approach. The application of a Safety-II approach to incident investigation, quality improvement projects, prospective analysis of risk in systems and performance indicators may offer improved insight into system performance leading to more effective change. The way forward may be to combine the Safety-II approach with ‘traditional’ methods to enhance patient safety training, outcomes and curriculum coverage

Improving spatial nitrogen dioxide prediction using diffusion tubes: a case study in West Central Scotland

It has been well documented that air pollution adversely affects health, and epidemiological pollutionhealth studies utilise pollution data from automatic monitors. However, these automatic monitors are small in number and hence spatially sparse, which does not allow an accurate representation of the spatial variation in pollution concentrations required for these epidemiological health studies. Nitrogen dioxide (NO2) diffusion tubes are also used to measure concentrations, and due to their lower cost compared to automatic monitors are much more prevalent. However, even combining both data sets still does not provide sufficient spatial coverage of NO2 for epidemiological studies, and modelled concentrations on a regular grid from atmospheric dispersion models are also available. This paper proposes the first modelling approach to using all three sources of NO2 data to make fine scale spatial predictions for use in epidemiological health studies. We propose a geostatistical fusion model that regresses combined NO2 concentrations from both automatic monitors and diffusion tubes against modelled NO2 concentrations from an atmospheric dispersion model in order to predict fine scale NO2 concentrations across our West Central Scotland study region. Our model exhibits a 47% improvement in fine scale spatial prediction of NO2 compared to using the automatic monitors alone, and we use it to predict NO2 concentrations across West Central Scotland in 2006

How robust are the estimated effects of air pollution on health? Accounting for model uncertainty using Bayesian model averaging

The long-term impact of air pollution on human health can be estimated from small-area ecological studies in which the health outcome is regressed against air pollution concentrations and other covariates, such as socio-economic deprivation. Socio-economic deprivation is multi-factorial and difficult to measure, and includes aspects of income, education, and housing as well as others. However, these variables are potentially highly correlated, meaning one can either create an overall deprivation index, or use the individual characteristics, which can result in a variety of pollution-health effects. Other aspects of model choice may affect the pollution-health estimate, such as the estimation of pollution, and spatial autocorrelation model. Therefore, we propose a Bayesian model averaging approach to combine the results from multiple statistical models to produce a more robust representation of the overall pollution-health effect. We investigate the relationship between nitrogen dioxide concentrations and cardio-respiratory mortality in West Central Scotland between 2006 and 2012

A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health

In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale

Utility Versus Creativity in Biomedical Musification

Sonification techniques provide a well-documented methodology for auditory display of data, which can be particularly useful when combined with other display types for the presentation and analysis of complex data streams (including multidimensional data arrays). Creativity in sonification often becomes a function of the chosen mapping scheme, whereby deliberate specification of data mapping to auditory events provides opportunities to creative expression. Thus, such techniques can be used as part of the music creation process, if mapping strategies are carefully designed with specific musical outcomes in mind. Increasingly this particular type of sonification is therefore referred to as musification. However, the creative decision making process involved in designing these mapping strategies can by its nature compromise the presentation of the data in terms of accuracy, and perhaps in terms of overall utility. This article reviews an example of this work with both creative and utilitarian ends, and considers techniques for the evaluation of the success versus the utility that musification of complex biological or biomedical data might achieve, whilst maintaining the necessary integrity of the source data

Aspects of N = 4 Super Yang-Mills: Amplitudes, Operators and Invariants

In this thesis, we study aspects of scattering amplitudes, half-BPS operators and Yangian Invariants in N = 4 super Yang Mills. We begin by exploring the geometry of Wilson loop diagrams. The Wilson loop in supertwistor space gives an explicit description of perturbative superamplitude integrands in N = 4 super Yang-Mills as a sum of planar Feynman diagrams. Each Feynman diagram can be naturally associated with a geometrical object in the same space as the amplituhedron (although not uniquely). This suggests that the geometrical images of the diagrams would give a tessellation of the amplituhedron. This turns out to be true for NMHV amplitudes, however we prove that for N^2MHV and beyond this is not the case. Specifically, we show that there is no choice of geometric image of the Wilson loop Feynman diagrams that gives a geometric object with no spurious boundaries. We then move to investigate a set of half-BPS operators in N = 4 super Yang-Mills which are appropriate for describing single particle states of superstring theory on AdS5×S5; we refer to these as single particle operators. They are defined to have vanishing two-point function with all multi-trace operators, and so correspond to admixtures of single- and multi-traces. We find explicit formulae for these operators and their two-point function normalisation. We prove that single particle operators in the U(N) gauge theory are single particle operators in the SU(N) theory, and show that at large N these operators interpolate between the single trace operator and the sphere giant graviton. A multipoint orthogonality theorem is presented and proved, which as a consequence enforces all near-extremal correlators to vanish. We compute all maximally and next-to-maximally extremal free correlators, and provide some explicit results for subsets of two- and three-point functions for multi-particle operators. Finally, we calculate the N^2MHV Yangian invariants for N = 4 SYM in amplituhedron coordinates, and see that some have suggestively simple forms

Methods for nanoparticle labeling of ricin and effect on toxicity

The unique optical properties associated with nanostructured materials that support the excitation of surface plasmons offer many new opportunities for the enhanced optical investigation of biological materials that pose a security threat. In particular, ricin is considered a significant bioterrorism risk due to its high toxicity combined with its ready availability as a byproduct in castor oil production. Therefore, the development of optical techniques capable of rapid on-site toxin detection with high molecular specificity and sensitivity continues to be of significant importance. Furthermore, understanding of the ricin cell entry and intracellular pathways remains poor due to a lack of suitable bioanalytical techniques. Initial work aimed at simultaneously tackling both these issues is described where different approaches for the nanoparticle labeling of ricin are investigated along with changes in ricin toxicity associated with the labeling process

A systematic review and meta-analysis of the effectiveness of pharmacist-led medication reconciliation in the community after hospital discharge

BACKGROUND Pharmacists’ completion of medication reconciliation in the community after hospital discharge is intended to reduce harm due to prescribed or omitted medication and increase healthcare efficiency, but the effectiveness of this approach is not clear. We systematically review the literature to evaluate intervention effectiveness in terms of discrepancy identification and resolution, clinical relevance of resolved discrepancies and healthcare utilisation, including readmission rates, emergency department attendance and primary care workload. DESIGN Systematic literature review and meta-analysis of extracted data. METHODS Medline, CINHAL, EMBASE, AMED, ERIC, SCOPUS, NHS evidence and the Cochrane databases were searched using a combination of Medical Subject Heading (MeSH) terms and free text search terms. Controlled studies evaluating pharmacist-led medication reconciliation in the community after hospital discharge were included. Study quality was appraised using CASP. Evidence was assessed through meta-analysis of readmission rates. Discrepancy identification rates, emergency department attendance and primary care workload were assessed narratively. RESULTS Fourteen studies were included comprising five RCTs, six cohort studies and three pre-post intervention studies. Twelve studies had a moderate or high risk of bias. Increased identification and resolution of discrepancies was demonstrated in the four studies where this was evaluated. Reduction in clinically relevant discrepancies was reported in two studies. Meta-analysis did not demonstrate a significant reduction in readmission rate. There was no consistent evidence of reduction in emergency department attendance or primary care workload. CONCLUSIONS Pharmacists can identify and resolve discrepancies when completing medication reconciliation after hospital discharge but patient outcome or care workload improvements were not consistently seen. Future research should examine the clinical relevance of discrepancies and potential benefits on reducing healthcare team workload

An adaptive spatio-temporal smoothing model for estimating trends and step changes in disease risk

Statistical models used to estimate the spatio-temporal pattern in disease risk from areal unit data often represent the risk surface for each time period in terms of known covariates and a set of spatially smooth random effects. The latter act as a proxy for unmeasured spatial confounding, whose spatial structure is often characterised by a spatially smooth evolution between some pairs of adjacent areal units while other pairs exhibit large step changes. This spatial heterogeneity is not consistent with a global smoothing model in which partial correlation exists between all pairs of adjacent spatial random effects, and a novel space-time disease model with an adaptive spatial smoothing specification that can identify step changes is therefore proposed. The new model is motivated by a new study of respiratory and circulatory disease risk across the set of Local Authorities in England, and is rigorously tested by simulation to assess its efficacy. Results from the England study show that the two diseases have similar spatial patterns in risk, and exhibit a number of common step changes in the unmeasured component of risk between neighbouring local authorities