Journal of evaluation in clinical practice
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Shared decision-making (SDM) is considered the "final stage" that completes the implementation of evidence-based medicine. Yet, it is also considered the most neglected stage. SDM shifts the epistemological authority of medical knowledge to one that deliberately includes patients' values and preferences. Although this redefines the work of the clinical encounter, it remains unclear what a shared decision is and how it is practiced. ⋯ There is a need for a more nuanced understanding of SDM as a "graded" framework that allows for flexibility in decision-making styles to accommodate patient's unique preferences and needs and to expand the manoeuvring space for decision-making. The strategies in this study show how our understanding of SDM as a process of multi-dyadic interactions that spatially exceed the consulting room offers new avenues to make SDM workable in contemporary medicine.
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Misunderstanding medication dosage regimen instructions can lead to unintentional misuse of a prescribed medicine, non-adherence to providers' instructions, and other treatment-related issues. We aimed to evaluate the frequency of and factors associated with older patients' misunderstanding of medication dosage regimen instructions after consultation with a general practitioner. ⋯ The results showed that older people's misunderstandings of medication dosage regimen instructions after consultation with a general practitioner was greater than expected due to a range of factors, especially polypharmacy, poor literacy, poor memory, and having a job at the time of the interview. Health services and professionals should implement strategies to increase the quality of the guidance given to elderly individuals and to ensure their adherence to the regimen instructions of their medications.
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The Getting It Right First Time programme aims to reduce variation in clinical practice that unduly impacts on outcomes for patients in the National Health Service (NHS) in England; often termed "unwarranted variation." However, there is no "gold standard" method for detecting unwarranted variation. The aim of this study was to describe a method to allow such variation in recorded practice or patient outcomes between NHS trusts to be detected using data over multiple time periods. By looking at variation over time, it was hoped that patterns that could be missed by looking at data at a single time point, or averaged over a longer time period, could be identified. ⋯ The time-series method may complement other methods to help identify unwarranted variation.
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To estimate in-hospital and 30-day outcomes after transcatheter aortic valve replacement (TAVR) in South America through a systematic review and meta-analysis of observational data. ⋯ As compared with published international registries, the overall results of TAVR in South America seemed underrated. Significant heterogeneity was observed in procedural success, pacemaker requirement, and post-procedure moderate or severe aortic regurgitation. This study provides a real-life framework for the analysis of the performance of this technology in the region, intended to be a starting point for quality improvement.
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New therapies are increasingly approved by regulatory agencies such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) based on testing in non-randomized clinical trials. These treatments have typically displayed "dramatic effects" (ie, effects that are considered large enough to obviate the combined effects of biases and random errors that may affect the study results). The agencies, however, have not identified how large these effects should be to avoid the need for further testing in randomized controlled trials (RCTs). We investigated the effect size that would circumvent the need for further RCTs testing by the regulatory agencies. We hypothesized that the approval of therapeutic interventions by regulators is based on heuristic decision making whose accuracy can be best characterized by the application of signal detection theory (SDT). ⋯ Drug developers and practitioners alike can use the change in one logarithm of effect size as a benchmark to decide if further testing in RCTs should be pursued, or as a guide to interpreting the results reported in non-randomized studies. However, further research would be useful to better characterize the threshold of effect size above which testing in RCTs is not needed.