"The Third Edition retains the general organization of the prior two editions, but it incorporates new material throughout the text. The book is organized into six parts: Part I covers basic sampling from simple random sampling to unequal probability sampling; Part II treats the use of auxiliary data with ratio and regression estimation and looks at the ideas of sufficient data, model, and design in practical sampling; Part III covers major useful designs such as stratified, cluster and systematic, multistage, and double and network sampling; Part IV examines detectability methods for elusive populations, and basic problems in detectability, visibility, and catchability are discussed; Part V concerns spatial sampling with the prediction methods of geostatistics, considerations of efficient spatial designs, and comparisons of different observational methods including plot shapes and detection aspects; and Part VI introduces adaptive sampling designs in which the sampling procedure depends on what is observed during the survey. For this new edition, the author has focused on thoroughly updating the book with a special emphasis on the first 14 chapters since these topics are invariably covered in basic sampling courses. The author has also implemented new approaches to explain the various techniques in the book, and as a result, new examples and explanations have been added throughout. In an effort to improve the presentation and visualization of the book, new figures as well as replacement figures for previously existing figures have been added. This book has continuously stood out from other sampling texts since the figures evoke the idea of each sampling design. The new figures will help readers to better visualize and understand the underlying concepts such as the different sampling strategies"--
Many hospitals face the problem of insufficient capacity to meet demand for inpatient beds, especially during demand surges. This results in quality degradation of patient care due to large delays from admission time to the hospital until arrival at a floor. In addition, there is loss of revenue because of the inability to provide service to potential patients. A solution to the problem is to proactively transfer patients between floors in anticipation of a demand surge. Optimal reallocation poses an extraordinarily complex problem that can be modeled as a finite-horizon Markov decision process. Based on the optimization model, a decision-support system has been developed and implemented at Windham Hospital in Willimantic, Connecticut. Projections from an initial trial period indicate very significant financial gains of about 1% of their total revenue, with no negative impact on any standard quality of care or staffing effectiveness indicators. In addition, the hospital showed a marked improvement in quality of care because of a resulting decrease of almost 50% in the average time that an admitted patient has to wait from admission until being transferred to a floor.