Journal of evaluation in clinical practice
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Although clinical practice guidelines have been promoted widely, there is considerable concern that physicians have not incorporated them into their practice. Models suggest that a 'knowledge-attitude-behaviour' sequence is important in modifying physician practice patterns. To address this, we examined physicians' knowledge of, attitudes towards and compliance with a widely implemented guideline - the Agency for Health Care Policy and Research (AHCPR) smoking cessation guideline. ⋯ In spite of little familiarity with the guideline, the responding physicians reported practice patterns consistent with adherence to it. Knowledge is only one of a spectrum of barriers that affects physician adherence to guidelines. There are numerous opportunities for health care organizations to overcome the barriers to physician adoption of clinical practice guidelines in their day-to-day practice.
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Increasingly, clinical research is evaluated on the quality of its statistical analysis. Traditionally, statistical analyses in clinical research have been carried out from a 'frequentist' perspective. The presence of an alternative paradigm - the Bayesian paradigm - has been relatively unknown in clinical research until recently. ⋯ In some analyses, the two methods are seen to produce comparable results; in others, they produce different results. It is noted that in this example, there are clinically relevant questions that are more easily addressed from a Bayesian perspective. Finally, areas in clinical research where Bayesian ideas are increasingly common are highlighted.
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Commonly used methods for guideline development and dissemination do not enable developers to tailor guidelines systematically to specific patient populations and update guidelines easily. We developed a web-based system, ALCHEMIST, that uses decision models and automatically creates evidence-based guidelines that can be disseminated, tailored and updated over the web. Our objective was to demonstrate the use of this system with clinical scenarios that provide challenges for guideline development. ⋯ Finally, we demonstrate how a clinician could use ALCHEMIST to incorporate a woman's utilities for relevant health states and thereby develop patient-specific recommendations for BRCA testing; the patient-specific recommendation improved quality-adjusted life expectancy by 37 days. The ALCHEMIST system enables guideline developers to publish both a guideline and an interactive decision model on the web. This web-based tool enables guideline developers to tailor guidelines systematically, to update guidelines easily, and to make the underlying evidence and analysis transparent for users.
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The objectives of this study were to describe ways in which doctors make suboptimal diagnostic and treatment decisions, and to discuss possible means of alleviating those biases, using a review of past studies from the psychological and medical decision-making literatures. A number of biases can affect the ways in which doctors gather and use evidence in making diagnoses. Biases also exist in how doctors make treatment decisions once a definitive diagnosis has been made. ⋯ None the less, they can have potentially grave consequences in medical settings, such as erroneous diagnosis or patient mismanagement. No surefire methods exist for eliminating biases in medical decision making, but there is some evidence that the adoption of an evidence-based medicine approach or the incorporation of formal decision analytic tools can improve the quality of doctors' reasoning. Doctors' reasoning is vulnerable to a number of biases that can lead to errors in diagnosis and treatment, but there are positive signs that means for alleviating some of these biases are available.
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The objective of the Mayo Health System Diabetes Translation Project is to assess the impact of three different models of care on the overall quality of diabetes care in the community. The unit of study is the primary care practice with a different model of care implemented at each of three sites. The design incorporates a comparison of a diabetes guideline implementation team initiative (Practice model A), a guideline initiative combined with clinical use of a Diabetes Electronic Management System (DEMS) by primary care providers (Practice model B) and a guideline initiative combined with DEMS utilization combined with electronic review of DEMS patient encounters by an endocrinologist (Practice model C). ⋯ Baseline data revealed significant differences across sites in adherence to certain key indicators of the quality of diabetes care including: frequency of documentation of eye examinations (19, 39 and 37% for sites A, B and C, respectively), haemoglobin A1c monitoring (64, 89 and 77%) and microalbumin monitoring (3, 15 and 6%). The interventions being assessed in this study include traditional (diabetes education; guideline implementation) and modern (DEMS; telemedicine specialist review) methods for improving the quality of diabetes care. In spite of variation in baseline quality indicators, the setting and design should lead to broad applicability of the results and help determine an optimal model of diabetes care in the community.