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2 produkter
2 produkter
1 096 kr
Skickas inom 5-8 vardagar
Praise for the first edition:"DNP students may struggle with data management, since their projects are not research but quality improvement, and this book covers the subject well. I recommend it for DNP students for use during their capstone projects." Score: 98, 5 Stars-- Doody's Medical ReviewsThis unique text and reference—the only book to address the full spectrum of clinical data management for the DNP student—instills a fundamental understanding of how clinical data is gathered, used, and analyzed, and how to incorporate this data into a quality DNP project. The new third edition is updated to reflect changes in national health policy such as quality measurements, bundled payments for specialty care, and Advances to the Affordable Care Act (ACA) and evolving programs through the Centers for Medicare and Medicaid Services (CMS). The third edition reflects the revision of 2021 AACN Essentials and provides data sets and other examples in Excel and SPSS format, along with several new chapters.This resource takes the DNP student step-by-step through the complete process of data management, from planning through presentation, clinical applications of data management that are discipline-specific, and customization of statistical techniques to address clinical data management goals. Chapters are brimming with descriptions, resources, and exemplars that are helpful to both faculty and students. Topics spotlight requisite competencies for DNP clinicians and leaders such as phases of clinical data management, statistics and analytics, assessment of clinical and economic outcomes, value-based care, quality improvement, benchmarking, and data visualization. A progressive case study highlights multiple techniques and methods throughout the text. Purchase includes online access via most mobile devices or computers.New to the Third Edition:New Chapter: Using EMR Data for the DNP ProjectNew chapter solidifies link between EBP and Analytics for the DNP projectNew chapter highlights use of workflow mapping to transition between current and future state, while simultaneously visualizing process measures needed to ensure success of the DNP projectIncludes more examples to provide practical application exercises for studentsKey Features:Disseminates robust strategies for using available data from everyday practice to support trustworthy evaluation of outcomesUses multiple tools to meet data management objectives [SPSS, Excel®, Tableau]Presents case studies to illustrate multiple techniques and methods throughout chaptersIncludes specific examples of the application and utility of these techniques using software that is familiar to graduate nursing studentsOffers real world examples of completed DNP projectsProvides Instructor’s Manual, PowerPoint slides, data sets in SPSS and Excel, and forms for completion of data management and evaluation plan
1 091 kr
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As the focus of the health care delivery system continues to move toward a coordinated and accountable system, there is an increasing need for a single resource that focuses on analytics for population health. Population Health Analytics addresses that need by providing detailed information and a "how to" guide for achieving population health analytics.Comprehensive, current, and practical, this logically organized text builds from understanding data sources, to contextualizing data, modeling data, and gleaning insights from that data, which is a natural progression for organizations in progressing to higher levels of analytic capabilities. Furthermore, these frameworks for the population health process and analytics are grounded in an evidence base that is also aligned with theories and processes used in healthcare disciplines. A first of its kind text, Population Health Analytics, will prepare students to improve health outcomes, understand patterns of health behavior and behavior determinants, develop effective interventions, and track health and cost performance across populations.Key features:- Real world scenarios using case studies, use cases, critical thinking and discussion questions encourage readers to apply data and analytics to improve health.- Readers will learn to discern the intricacies and nuances of creating, preparing, contextualizing, tracking, and reporting data to support clinical quality and healthcare cost improvement.- Guiding principles and processes to develop a population health workforce are introduced, such as the Team Troika in Chapter 31.- Each application of healthcare data is supported with evidence, ample references, and ancillary resources.- Navigate eBook Access (included with each printed text) enabling online or offline access to the text from a computer, tablet, or mobile device.