Journal of evaluation in clinical practice
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In this era of increasing complexity, there is a growing gap between what we need our medical experts to do and the training we provide them. While medical education has a long history of being guided by theories of expertise to inform curriculum design and implementation, the theories that currently underpin our educational programs do not account for the expertise necessary for excellence in the changing health care context. ⋯ Three key educational approaches have been shown to foster the development of adaptive expertise: learning that emphasizes understanding, providing students with opportunities to embrace struggle and discovery in their learning, and maximizing variation in the teaching of clinical concepts. There is solid evidence that a commitment to these educational approaches can help medical educators to set trainees on the path towards adaptive expertise.
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There is general consensus that clinical reasoning involves 2 stages: a rapid stage where 1 or more diagnostic hypotheses are advanced and a slower stage where these hypotheses are tested or confirmed. The rapid hypothesis generation stage is considered inaccessible for analysis or observation. Consequently, recent research on clinical reasoning has focused specifically on improving the accuracy of the slower, hypothesis confirmation stage. ⋯ The first perspective takes an epidemiological stance, appealing to the benefits of incorporating population data and evidence-based medicine in every day clinical reasoning. The second builds on the heuristic and bias research programme, appealing to a special class of dual process reasoning models that theorizes a rapid error prone cognitive process for problem solving with a slower more logical cognitive process capable of correcting those errors. Finally, the third perspective borrows from an exemplar model of categorization that explicitly relates clinical knowledge and experience to diagnostic accuracy.
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Randomized Controlled Trial Multicenter Study
The Systematic Tool to Reduce Inappropriate Prescribing (STRIP): Combining implicit and explicit prescribing tools to improve appropriate prescribing.
Inappropriate prescribing is a major health care issue, especially regarding older patients on polypharmacy. Multiple implicit and explicit prescribing tools have been developed to improve prescribing, but these have hardly ever been used in combination. The Systematic Tool to Reduce Inappropriate Prescribing (STRIP) combines implicit prescribing tools with the explicit Screening Tool to Alert physicians to the Right Treatment and Screening Tool of Older People's potentially inappropriate Prescriptions criteria and has shared decision-making with the patient as a critical step. ⋯ In conclusion, the STRIP helps health care providers to systematically identify potentially inappropriate prescriptions and medication-related problems and to change the patient's medication regimen in accordance with the patient's needs and wishes. This article describes the STRIP and the available evidence so far. The OPERAM study is investigating the effect of STRIP use on clinical and economic outcomes.
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Randomized Controlled Trial Multicenter Study
Improving screening and brief intervention activities in primary health care: Secondary analysis of professional accuracy based on the AUDIT-C.
The ODHIN trial found that training and support and financial reimbursement increased the proportion of patients that were screened and given advice for their heavy drinking in primary health care. However, the impact of these strategies on professional accuracy in delivering screening and brief advice is underresearched and is the focus of this paper. ⋯ Although the use of AUDIT-C as a screening tool was accurate, a considerable proportion of risky drinkers did not receive advice, which was reduced with financial incentives.
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Randomized Controlled Trial
Identifying causal mechanisms in health care interventions using classification tree analysis.
Mediation analysis identifies causal pathways by testing the relationships between the treatment, the outcome, and an intermediate variable that mediates the relationship between the treatment and outcome. This paper introduces classification tree analysis (CTA), a machine-learning procedure, as an alternative to conventional methods for analysing mediation effects. ⋯ CTA may uncover mediation effects where conventional approaches do not, because CTA does not require any assumptions about the distribution of variables nor of the functional form of the model, and CTA will systematically identify all statistically viable interactions. The versatility of CTA enables the investigator to explore the theorized underlying causal mechanism of an intervention in a much more comprehensive manner than conventional mediation analytic approaches.