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As the science of quality improvement in health care advances, the importance of sharing its accomplishments through the published literature increases. Current reporting of improvement work in health care varies widely in both content and quality. It is against this backdrop that a group of stakeholders from a variety of disciplines has created the Standards for QUality Improvement Reporting Excellence, which we refer to as the SQUIRE publication guidelines or SQUIRE statement. The SQUIRE statement consists of a checklist of 19 items that authors need to consider when writing articles that describe formal studies of quality improvement. Most of the items in the checklist are common to all scientific reporting, but virtually all of them have been modified to reflect the unique nature of medical improvement work.This “Explanation and Elaboration” document (E & E) is a companion to the SQUIRE statement. For each item in the SQUIRE guidelines the E & E document provides one or two examples from the published improvement literature, followed by an analysis of the ways in which the example expresses the intent of the guideline item. As with the E & E documents created to accompany other biomedical publication guidelines, the purpose of the SQUIRE E & E document is to assist authors along the path from completion of a quality improvement project to its publication. The SQUIRE statement itself, this E & E document, and additional information about reporting improvement work can be found at http://www.squire-statement.org.

In this paper, we present a hierarchical mapping method that allows us to obtain accurate metric maps of large environments in real time. The lower (or local) map level is composed of a set of local maps that are guaranteed to be statistically independent. The upper (or global) level is an adjacency graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained at this level in a relative stochastic map. We propose a close to optimal loop closing method that, while maintaining independence at the local level, imposes consistency at the global level at a computational cost that is linear with the size of the loop. Experimental results demonstrate the efficiency and precision of the proposed method by mapping the Ada Byron building at our campus. We also analyze, using simulations, the precision and convergence of our method for larger loops.

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Many reasons offered by physicians for not intensifying medications suggest that low rates of intensification do not necessarily reflect poor quality of care. The empirically derived model of clinical inaction can be used as a guide to construct performance measures for monitoring clinical inertia that better focus on true quality problems.

University of Alabama at Birmingham and the Justin E. Rodgers Foundation.