165,790 research outputs found
Assessment of the added value of the Twente Photoacoustic Mammoscope in breast cancer diagnosis\ud
Purpose: Photoacoustic (PA) imaging is a recently developed breast cancer imaging technique. In order to enhance successful clinical implementation, we quantified the potential clinical value of different scenarios incorporating PA imaging by means of multi-criteria analysis. From this analysis, the most promising area of application for PA imaging in breast cancer diagnosis is determined, and recommendations are provided to optimize the design of PA imaging. - \ud
Methods: The added value of PA imaging was assessed in two areas of application in the diagnostic track. These areas include PA imaging as an alternative to x-ray mammography and ultrasonography in early stage diagnosis, and PA imaging as an alternative to Magnetic Resonance Imaging (MRI) in later stage diagnosis. The added value of PA imaging was assessed with respect to four main criteria (costs, diagnostic performance, patient comfort and risks). An expert panel composed of medical, technical and management experts was asked to assess the relative importance of the criteria in comparing the alternative diagnostic devices. The judgments of the experts were quantified based on the validated pairwise comparison technique of the Analytic Hierarchy Process, a technique for multi-criteria analysis. Sensitivity analysis was applied to account for the uncertainty of the outcomes. - \ud
Results: Among the considered alternatives, PA imaging is the preferred technique due to its non-invasiveness, low cost and low risks. However, the experts do not expect large differences in diagnostic performance. The outcomes suggest that design changes to improve the diagnostic performance of PA imaging should focus on the quality of the reconstruction algorithm, detector sensitivity, detector bandwidth and the number of wavelengths used. - \ud
Conclusion: The AHP method was useful in recommending the most promising area of application in the diagnostic track for which PA imaging can be implemented, this being early diagnosis, as a substitute for the combined use of x-ray mammography and ultrasonography
Advancing Physician Performance Measurement: Using Administrative Data to Assess Physician Quality and Efficiency
Summarizes national initiatives to advance the practice of standardized measurement and outlines goals for developing a method for tracking efficiency and quality that will reward physicians and enable patients to make informed healthcare choices
Tone from the Top in Risk Management: A Complementarity Perspective on How Control Systems Influence Risk Awareness
Prompted by the weaknesses of standardized risk management approaches in the aftermath of the
2008 financial crisis, scholars, regulators, and practitioners alike emphasize the importance of
creating a risk-aware culture in organizations. Recent insights highlight the special role of tone
from the top as crucial driver of risk awareness. In this study, we take a systems-perspective on
control system design to investigate the role of tone from the top in creating risk awareness. In
particular, we argue that both interactive and diagnostic use of budgets and performance measures
interact with tone from the top in managing risk awareness. Our results show that interactive control
strengthens the effect of tone from the top on risk awareness, while tone from the top and diagnostic
control are, on average, not interrelated with regard to creating risk awareness. To shed light on the
boundary conditions of the proposed interdependencies, we further investigate whether the
predicted interdependencies are sensitive to the level of perceived environmental uncertainty. We
find that the effect of tone from the top and interactive control becomes significantly stronger in a
situation of high perceived environmental uncertainty. Most interestingly, tone from the top and
diagnostic control are complements with regard to risk awareness in settings of low perceived
environmental uncertainty and substitutes at high levels of perceived environmental uncertainty.Series: Department of Strategy and Innovation Working Paper Serie
Email for clinical communication between healthcare professionals
Email is one of the most widely used methods of communication, but its use in healthcare is still uncommon. Where email communication has been utilised in health care, its purposes have included clinical communication between healthcare professionals, but the effects of using email in this way are not well known. We updated a 2012 review of the use of email for two-way clinical communication between healthcare professionals
Community Development Evaluation Storymap and Legend
Community based organizations, funders, and intermediary organizations working in the community development field have a shared interest in building stronger organizations and stronger communities. Through evaluation these organizations can learn how their programs and activities contribute to the achievement of these goals, and how to improve their effectiveness and the well-being of their communities. Yet, evaluation is rarely seen as part of a non-judgemental organizational learning process. Instead, the term "evaluation" has often generated anxiety and confusion. The Community Development Storymap project is a response to those concerns.Illustrations found in this document were produced by Grove Consultants
An Empirical Comparison of Three Inference Methods
In this paper, an empirical evaluation of three inference methods for
uncertain reasoning is presented in the context of Pathfinder, a large expert
system for the diagnosis of lymph-node pathology. The inference procedures
evaluated are (1) Bayes' theorem, assuming evidence is conditionally
independent given each hypothesis; (2) odds-likelihood updating, assuming
evidence is conditionally independent given each hypothesis and given the
negation of each hypothesis; and (3) a inference method related to the
Dempster-Shafer theory of belief. Both expert-rating and decision-theoretic
metrics are used to compare the diagnostic accuracy of the inference methods.Comment: Appears in Proceedings of the Fourth Conference on Uncertainty in
Artificial Intelligence (UAI1988
A multimodal neuroimaging classifier for alcohol dependence
With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence
A multimodal neuroimaging classifier for alcohol dependence
With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence
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