Breast compression – An exploration of problem solving and decision-making in mammography

Objective: Breast compression decreases radiation dose and reduces potential for motion and geometric unsharpness, yet there is variability in applied compression force within and between some centres. This article explores the problem solving process applied to the application of breast compression force from the mammography practitioners' perspective. Methods: A qualitative analysis was undertaken using an existing full data set of transcribed qualitative data collected in a phenomenological study of mammography practitioner values, behaviours and beliefs. The data emerged from focus groups conducted at six NHS breast screening centres in England (participant n = 41), and semi-structured interviews with mammography educators (n = 6). A researcher followed a thematic content analysis process to extract data related to mammography compression problem solving, developing a series of categories, themes and sub-themes. Emerging themes were then peer-validated by two other researchers, and developed into a model of practice. Results: Seven consecutive stages contributed towards compression force problem solving: assessing the request; first impressions; explanations and consent; handling the breast and positioning; applying compression force; final adjustments; feedback. The model captures information gathering, problem framing, problem solving and decision making which inform an ‘ideal’ compression scenario. Behavioural problem solving, heuristics and intuitive decision making are reflected within this model. Conclusion: The application of compression should no longer be considered as one single task within mammography, but is now recognised as a seven stage problem solving continuum. This continuum model is the first to be applied to mammography, and is adaptable and transferable to other radiography practice settings

Crises and collective socio-economic phenomena: simple models and challenges

Financial and economic history is strewn with bubbles and crashes, booms and busts, crises and upheavals of all sorts. Understanding the origin of these events is arguably one of the most important problems in economic theory. In this paper, we review recent efforts to include heterogeneities and interactions in models of decision. We argue that the Random Field Ising model (RFIM) indeed provides a unifying framework to account for many collective socio-economic phenomena that lead to sudden ruptures and crises. We discuss different models that can capture potentially destabilising self-referential feedback loops, induced either by herding, i.e. reference to peers, or trending, i.e. reference to the past, and account for some of the phenomenology missing in the standard models. We discuss some empirically testable predictions of these models, for example robust signatures of RFIM-like herding effects, or the logarithmic decay of spatial correlations of voting patterns. One of the most striking result, inspired by statistical physics methods, is that Adam Smith's invisible hand can badly fail at solving simple coordination problems. We also insist on the issue of time-scales, that can be extremely long in some cases, and prevent socially optimal equilibria to be reached. As a theoretical challenge, the study of so-called "detailed-balance" violating decision rules is needed to decide whether conclusions based on current models (that all assume detailed-balance) are indeed robust and generic.Comment: Review paper accepted for a special issue of J Stat Phys; several minor improvements along reviewers' comment

A muon-track reconstruction exploiting stochastic losses for large-scale Cherenkov detectors

IceCube is a cubic-kilometer Cherenkov telescope operating at the South Pole. The main goal of IceCube is the detection of astrophysical neutrinos and the identification of their sources. High-energy muon neutrinos are observed via the secondary muons produced in charge current interactions with nuclei in the ice. Currently, the best performing muon track directional reconstruction is based on a maximum likelihood method using the arrival time distribution of Cherenkov photons registered by the experiment\u27s photomultipliers. A known systematic shortcoming of the prevailing method is to assume a continuous energy loss along the muon track. However at energies >1 TeV the light yield from muons is dominated by stochastic showers. This paper discusses a generalized ansatz where the expected arrival time distribution is parametrized by a stochastic muon energy loss pattern. This more realistic parametrization of the loss profile leads to an improvement of the muon angular resolution of up to 20% for through-going tracks and up to a factor 2 for starting tracks over existing algorithms. Additionally, the procedure to estimate the directional reconstruction uncertainty has been improved to be more robust against numerical errors

Collaborative Rural Healthcare Network: A Conceptual Model

Healthcare is a critical issue in rural communities throughout the world. Provision of timely and cost effective health care in these communities is a challenge since it is coupled with a lack of adequate infrastructure and manpower support. Twenty percent of the United States of America‘s population resides in rural communities, i.e., 59 million people; however, only nine percent of the nation’s physicians practice in rural communities. Shortage of health care personnel and the lack of equipment and facilities often force rural residents to travel long distances to receive needed medical treatment. Researchers and practitioners are in search of solutions to address these unique challenges. In this research, we present a proposed collaborative model of a health information system for rural communities and the challenges and opportunities of this global issue

Predicting nosocomial infection by using data mining technologies

The existence of nosocomial infection prevision systems in healthcare environments can contribute to improve the quality of the healthcare institution and also to reduce the costs with the treatment of the patients that acquire these infections. The analysis of the information available allows to efficiently prevent these infections and to build knowledge that can help to identify their eventual occurrence. This paper presents the results of the application of predictive models to real clinical data. Good models, induced by the Data Mining (DM) classification techniques Support Vector Machines and Naïve Bayes, were achieved (sensitivities higher than 91.90%). Therefore, with these models that be able to predict these infections may allow the prevention and, consequently, the reduction of nosocomial infection incidence. They should act as a Clinical Decision Support System (CDSS) capable of reducing nosocomial infections and the associated costs, improving the healthcare and, increasing patients’ safety and well-being.FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013info:eu-repo/semantics/publishedVersio