Journal of evaluation in clinical practice
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Comparative Study
Concordance of hospital-based cancer registry data with a clinicians' database for breast cancer.
Reliable information is essential to both clinical and policy decision making. We aimed to shed lights on the similarity and differences between a hospital-based cancer registry with a clinicians' database for breast cancer by comparing the registered data on the same year. ⋯ Although information contained in hospital-based cancer registry and clinicians' database are generally accurate, some important differences were revealed as a result of varying interpretations of clinical information. Analyses of these data sets must be made with attention to details such as eligible patients, registered treatment, and timing of registration.
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In 2009, the UK Department of Health formalized recommended National Health Service practices for the management of tinnitus from primary care onwards. It is timely therefore to evaluate the perceived practicality, utility and impact of those guidelines in the context of current practice. ⋯ While the lack of standardized practice might provide flexibility to meet local demand, it has drawbacks. It makes it difficult to ascertain key standards of best practice, it complicates the process of clinical audit, it implies unequal patient access to care, and it limits the implementation of translational research outcomes. We recommend that core elements of practice should be standardized, including use of a validated tinnitus questionnaires and an agreed pathway for decision making to better understand the rationale for management strategies offered.
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To test the feasibility of classifying emergency department patients into severity grades using data mining methods. ⋯ It is possible to produce a computerized classification model for the severity grade of triage patients, using data mining methods. Learning from patient records regarding which there is a consensus of several physicians is preferable to learning from each physician's patients. Either Naïve Bayes or C4.5 can be used; Naïve Bayes is preferable for unbalanced data sets. An ambiguity in the intermediate severity grades seems to hamper both the physicians' agreement and the classifiers' accuracy.
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Involving members of the public in health research is said to produce higher quality research of greater clinical relevance. However, many of the anecdotal accounts of public involvement published in the academic literature to date have focused on the process of recruiting and involving members of the public and the effect of participation on these individuals rather than on how public involvement influenced the research process or outcomes. To strengthen the evidence base, there is clearly a need for more formal methods of capturing and documenting the impact of public involvement in health research. ⋯ If more evidence can be provided that public involvement enhances research processes and outcomes, researchers may be less inclined to treat this initiative as something they have to do in order to satisfy funding agencies and regulatory bodies and actively embrace this phenomenon, producing accounts of successful public involvement that transcend current barriers to its successful implementation.
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Diagnostic reasoning is a critical aspect of clinical performance, having a high impact on quality and safety of care. Although diagnosis is fundamental in medicine, we still have a poor understanding of the factors that determine its course. According to traditional understanding, all information used in diagnostic reasoning is objective and logically driven. However, these conditions are not always met. Although we would be less likely to make an inaccurate diagnosis when following rational decision making, as described by normative models, the real diagnostic process works in a different way. Recent work has described the major cognitive biases in medicine as well as a number of strategies for reducing them, collectively called debiasing techniques. However, advances have encountered obstacles in achieving implementation into clinical practice. ⋯ Using a cognitive approach, we describe the basis of medical error, with particular emphasis on diagnostic error. We then propose a conceptual scheme of the diagnostic process by the use of fuzzy cognitive maps.