Impact on maternity professionals of novel approaches to clinical audit feedback

We compared three approaches to feedback of clinical audit findings relating to miscarriage in 15 Scottish maternity services (printed report alone; report plus Action Planning Letter; report plus face-to-face Facilitated Action Planning). We surveyed clinicians to measure Theory of Planned Behaviour constructs (in the context of two audit criteria) before and after feedback (n=253) and assessed perceptions of the audit through in-depth interviews (n=17). Pre-feedback, clinicians had positive attitudes and strong subjective norms and intentions to comply, although perceived behavioural control was lower. Generally, positive attitudes, subjective norms and intentions increased after feedback but for one of the two criteria (providing a 7-day miscarriage service), perceived behavioural control decreased. No changes over time reached statistical significance and analysis of covariance (adjusting for pre-feedback scores) showed no consistent relationships between method of feedback and post-feedback construct scores. Interviews revealed positive perceptions of audit but frustration at lack of capacity to implement changes. While interventions which increased intensity of feedback proved feasible and acceptable to clinicians, we were unable to demonstrate that they increased intention to comply with audit criteria.This study was funded by NHS Quality Improvement Scotland

The waiting time paradox: population based retrospective study of treatment delay and survival of women with endometrial cancer in Scotland

No abstract available

Stochastic learning in a neural network with adapting synapses

We consider a neural network with adapting synapses whose dynamics can be analitically computed. The model is made of $N$ neurons and each of them is connected to $K$ input neurons chosen at random in the network. The synapses are $n$-states variables which evolve in time according to Stochastic Learning rules; a parallel stochastic dynamics is assumed for neurons. Since the network maintains the same dynamics whether it is engaged in computation or in learning new memories, a very low probability of synaptic transitions is assumed. In the limit $N\to\infty$ with $K$ large and finite, the correlations of neurons and synapses can be neglected and the dynamics can be analitically calculated by flow equations for the macroscopic parameters of the system.Comment: 25 pages, LaTeX fil

A recurrent neural network with ever changing synapses

A recurrent neural network with noisy input is studied analytically, on the basis of a Discrete Time Master Equation. The latter is derived from a biologically realizable learning rule for the weights of the connections. In a numerical study it is found that the fixed points of the dynamics of the net are time dependent, implying that the representation in the brain of a fixed piece of information (e.g., a word to be recognized) is not fixed in time.Comment: 17 pages, LaTeX, 4 figure

Accuracy Analysis of an Image Guided Robotic Urology Surgery System

We present an evaluation of the accuracy of a system for image guided radical prostatectomy using the daVinci telemanipulator. The system is split into components and ten sources of error identified. The magnitude of three of these error sources; segmentation of bone from MRI, registration to patient using intraoperative ultrasound, and endoscope tracking error is determined experimentally. The remaining errors are estimated from the literature. We demonstrate that the distribution of ultrasound slices used for registration can reduce the system error by up to 0.7mm. Our results show that our system can localise the prostate to within 3.7mm RMS, and that the largest component of the this error is the segmentation of the pelvic bone from MRI

Threat assessment, sense making, and critical decision-making in police, military, ambulance, and fire services

Military and emergency response remain inherently dangerous occupations that require the ability to accurately assess threats and make critical decisions under significant time pressures. The cognitive processes associated with these abilities are complex and have been the subject of several significant, albeit service specific studies. Here, we present an attempt at finding the commonalities in threat assessment, sense making, and critical decision-making for emergency response across police, military, ambulance, and fire services. Relevant research is identified and critically appraised through a systematic literature review of English-language studies published from January 2000 through July 2020 on threat assessment and critical decision-making theory in dynamic emergency service and military environments. A total of 10,084 titles and abstracts were reviewed, with 94 identified as suitable for inclusion in the study. We then present our findings focused on six lines of enquiry: Bibliometrics, Language, Situation Awareness, Critical Decision Making, Actions, and Evaluation. We then thematically analyse these findings to reveal the commonalities between the four services. Despite existing single or dual service studies in the field, this research is significant in that it is the first examine decision making and threat assessment theory across all four contexts of military, police, fire and ambulance services, but it is also the first to assess the state of knowledge and explore the extent that commonality exists and models or practices can be applied across each discipline. The results demonstrate all military and emergency services personnel apply both intuitive and formal decision-making processes, depending on multiple situational and individual factors. Institutional restriction of decision-making to a single process at the expense of the consideration of others, or the inappropriate training and application of otherwise appropriate decision-making processes in certain circumstances is likely to increase the potential for adverse outcomes, or at the very least restrict peak performance being achieved. The applications of the findings of the study not only extend to facilitating improved practice in each of the individual services examined, but provide a basis to assist future research, and contribute to the literature exploring threat assessment and decision making in dynamic contexts

Hierarchical Self-Programming in Recurrent Neural Networks

We study self-programming in recurrent neural networks where both neurons (the `processors') and synaptic interactions (`the programme') evolve in time simultaneously, according to specific coupled stochastic equations. The interactions are divided into a hierarchy of $L$ groups with adiabatically separated and monotonically increasing time-scales, representing sub-routines of the system programme of decreasing volatility. We solve this model in equilibrium, assuming ergodicity at every level, and find as our replica-symmetric solution a formalism with a structure similar but not identical to Parisi's $L$-step replica symmetry breaking scheme. Apart from differences in details of the equations (due to the fact that here interactions, rather than spins, are grouped into clusters with different time-scales), in the present model the block sizes $m_i$ of the emerging ultrametric solution are not restricted to the interval $[0,1]$, but are independent control parameters, defined in terms of the noise strengths of the various levels in the hierarchy, which can take any value in [0,\infty\ket. This is shown to lead to extremely rich phase diagrams, with an abundance of first-order transitions especially when the level of stochasticity in the interaction dynamics is chosen to be low.Comment: 53 pages, 19 figures. Submitted to J. Phys.