The strength of a recommendation reflects the extent to which guideline developers can, across the range of patients for whom the recommendations are intended, be confident that the desirable effects of following the recommendation outweigh the undesirable effects. Four factors influence the strength of a recommendation: the quality of evidence supporting the recommendation, the balance between desirable and undesirable effects, the uncertainty or variability of patient values and preferences, and costs. Strong and weak (also called "conditional") recommendations have distinct implications for patients, clinicians, and policy makers. Adherence to strong recommendations or, in the case of weak (conditional) recommendations, documentation of discussion or shared decision making with a patient, might be used as quality measures or performance indicators. Clinicians desire guidance regardless of the quality of the underlying evidence. Very low-quality evidence should ideally result in either appropriately labeled recommendations (i.e., as based on very low-quality evidence) or a statement that the guideline panel did not reach consensus on the recommendation due to the lack of confidence in the effect estimates. However, guideline panels often have more resources, time, and information than practicing clinicians. Therefore, they may be in a position to use their best judgments to make recommendations even when there is very low-quality evidence, although some guideline developers disagree with this approach and prefer a general approach of not making recommendations in the face of very low-quality evidence. Guideline panels should consider making research recommendations when there is important uncertainty about the desirable and undesirable effects of an intervention, further research could reduce that uncertainty, and the potential benefits and savings of reducing the uncertainty outweigh the potential harms of not making the research recommendation. Recommendations for additional research should be as precise and specific as possible.
System and process auditors assure -from an information processing perspective -the correctness and integrity of the data that is aggregated in a company's financial statements. To do so, they assess whether a company's business processes and information systems process financial data correctly. The audit process is a complex endeavor that in practice has to rely on simplifying assumptions. These simplifying assumptions mainly result from the need to restrict the audit scope and to focus it on the major risks. This article describes a generalized audit process. According to our experience with this process, there is a risk that material deficiencies remain undiscovered when said simplifying assumptions are not satisfied. To address this risk of deficiencies, the article compiles thirteen control patterns, which -according to our experience -are particularly suited to help information systems satisfy the simplifying assumptions. As such, use of these proven control patterns makes information systems easier to audit and IT architects can use them to build systems that meet audit requirements by design. Additionally, the practices and advice offered in this interdisciplinary article help bridge the gap between the architects and auditors of information systems and show either role how to benefit from an understanding of the other role's terminology, techniques, and general work approach.
Many authorities suggest that there is a need to include explicit consideration of costs, resource use, and affordability during guideline development. Where drug use is at issue, "explicit consideration" may need to involve only noting whether the price (easily determined and usually the main component of "acquisition cost") of a drug is high or low. Complex interventions such as rehabilitation services are to a greater degree setting- and system-dependent. Resources used, and the costs of those resources, will vary among systems, and formal identification by a guideline group of the resource requirements of a complex intervention is essential. A clinical guideline usually contains multiple recommendations, and in some cases there are hundreds. Defining costs and resource use for all of them-especially for multiple settings-is unlikely to be feasible. At present, disaggregated resource utilization accompanied by some cost information seems to be the most promising approach. The method for assigning values to costs, including external or indirect cost (such as time off work), can have a significant impact on the outcome of any economic evaluation. The perspective that the guideline assumes should be made explicit. Standards for evidence for clinical data are usually good-quality trials reporting a relevant endpoint that should be summarized in a systematic review. Like others, we are therefore proposing that the ideal sources of evidence for cost and resource utilization data for guideline development are systematic reviews of randomized controlled trials that report resource utilization, with direct comparisons between the interventions of interest.
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