Health Care Data and the SAS System

Health Care Data and the SAS System
Author :
Publisher : SAS Press
Total Pages : 0
Release :
ISBN-10 : 1580258654
ISBN-13 : 9781580258654
Rating : 4/5 (54 Downloads)

Synopsis Health Care Data and the SAS System by : Marge Scerbo

New and experienced SAS programmers and analysts working in health care data analysis will find this book invaluable in their daily professional life. A terrific primer for new health care analysts and a reference for long-time practitioners, this book defines the types of health care data and explores a wide range of tasks, including reading, validating, and manipulating the health care data, and producing reports.

Analysis of Observational Health Care Data Using SAS

Analysis of Observational Health Care Data Using SAS
Author :
Publisher : SAS Press
Total Pages : 0
Release :
ISBN-10 : 1607642271
ISBN-13 : 9781607642275
Rating : 4/5 (71 Downloads)

Synopsis Analysis of Observational Health Care Data Using SAS by : Douglas E. Faries

This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data. This book is part of the SAS Press program.

Administrative Healthcare Data

Administrative Healthcare Data
Author :
Publisher : SAS Institute
Total Pages : 250
Release :
ISBN-10 : 9781629593807
ISBN-13 : 162959380X
Rating : 4/5 (07 Downloads)

Synopsis Administrative Healthcare Data by : Craig Dickstein

Explains the source and content of administrative healthcare data, which is the product of financial reimbursement for healthcare services. The book integrates the business knowledge of healthcare data with practical and pertinent case studies as shown in SAS Enterprise Guide.

Applied Health Analytics and Informatics Using SAS

Applied Health Analytics and Informatics Using SAS
Author :
Publisher :
Total Pages : 378
Release :
ISBN-10 : 1642953407
ISBN-13 : 9781642953404
Rating : 4/5 (07 Downloads)

Synopsis Applied Health Analytics and Informatics Using SAS by : Joseph M. Woodside

Leverage health data into insight! Applied Health Analytics and Informatics Using SAS describes health anamatics, a result of the intersection of data analytics and health informatics. Healthcare systems generate nearly a third of the world's data, and analytics can help to eliminate medical errors, reduce readmissions, provide evidence-based care, demonstrate quality outcomes, and add cost-efficient care. This comprehensive textbook includes data analytics and health informatics concepts, along with applied experiential learning exercises and case studies using SAS Enterprise MinerTM within the healthcare industry setting. Topics covered include: Sampling and modeling health data - both structured and unstructured Exploring health data quality Developing health administration and health data assessment procedures Identifying future health trends Analyzing high-performance health data mining models Applied Health Analytics and Informatics Using SAS is intended for professionals, lifelong learners, senior-level undergraduates, graduate-level students in professional development courses, health informatics courses, health analytics courses, and specialized industry track courses. This textbook is accessible to a wide variety of backgrounds and specialty areas, including administrators, clinicians, and executives.

SAS Programming with Medicare Administrative Data

SAS Programming with Medicare Administrative Data
Author :
Publisher : SAS Institute
Total Pages : 272
Release :
ISBN-10 : 9781629591537
ISBN-13 : 162959153X
Rating : 4/5 (37 Downloads)

Synopsis SAS Programming with Medicare Administrative Data by : Matthew Gillingham

SAS Programming with Medicare Administrative Data is the most comprehensive resource available for using Medicare data with SAS. This book teaches you how to access Medicare data and, more importantly, how to apply this data to your research. Knowing how to use Medicare data to answer common research and business questions is a critical skill for many SAS users. Due to its complexity, Medicare data requires specific programming knowledge in order to be applied accurately. Programmers need to understand the Medicare program in order to interpret and utilize its data. With this book, you'll learn the entire process of programming with Medicare data—from obtaining access to data; to measuring cost, utilization, and quality; to overcoming common challenges. Each chapter includes exercises that challenge you to apply concepts to real-world programming tasks. SAS Programming with Medicare Administrative Data offers beginners a programming project template to follow from beginning to end. It also includes more complex questions and discussions that are appropriate for advanced users. Matthew Gillingham has created a book that is both a foundation for programmers new to Medicare data and a comprehensive reference for experienced programmers. This book is part of the SAS Press program.

Statistical Analysis of Medical Data Using SAS

Statistical Analysis of Medical Data Using SAS
Author :
Publisher : CRC Press
Total Pages : 450
Release :
ISBN-10 : 158488469X
ISBN-13 : 9781584884699
Rating : 4/5 (9X Downloads)

Synopsis Statistical Analysis of Medical Data Using SAS by : Geoff Der

Statistical analysis is ubiquitous in modern medical research. Logistic regression, generalized linear models, random effects models, and Cox's regression all have become commonplace in the medical literature. But while statistical software such as SAS make routine application of these techniques possible, users who are not primarily statisticians must take care to correctly implement the various procedures and correctly interpret the output. Statistical Analysis of Medical Data Using SAS demonstrates how to use SAS to analyze medical data. Each chapter addresses a particular analysis method. The authors briefly describe each procedure, but focus on its SAS implementation and properly interpreting the output. The carefully designed presentation relegates the theoretical details to "Displays," so that the code and results can be explored without interruption. All of the code and data sets used in the book are available for download from either the SAS Web site or www.crcpress.com. Der and Everitt, authors of the best-selling Handbook of Statistical Analyses Using SAS, bring all of their considerable talent and experience to bear in this book. Step-by-step instructions, lucid explanations and clear examples combine to form an outstanding, self-contained guide--suitable for medical researchers and statisticians alike--to using SAS to analyze medical data.

Data-Driven Healthcare

Data-Driven Healthcare
Author :
Publisher : John Wiley & Sons
Total Pages : 224
Release :
ISBN-10 : 9781118973899
ISBN-13 : 1118973895
Rating : 4/5 (99 Downloads)

Synopsis Data-Driven Healthcare by : Laura B. Madsen

Healthcare is changing, and data is the catalyst Data is taking over in a powerful way, and it's revolutionizing the healthcare industry. You have more data available than ever before, and applying the right analytics can spur growth. Benefits extend to patients, providers, and board members, and the technology can make centralized patient management a reality. Despite the potential for growth, many in the industry and government are questioning the value of data in health care, wondering if it's worth the investment. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry tackles the issue and proves why BI is not only worth it, but necessary for industry advancement. Healthcare BI guru Laura Madsen challenges the notion that data have little value in healthcare, and shows how BI can ease regulatory reporting pressures and streamline the entire system as it evolves. Madsen illustrates how a data-driven organization is created, and how it can transform the industry. Learn why BI is a boon to providers Create powerful infographics to communicate data more effectively Find out how Big Data has transformed other industries, and how it applies to healthcare Data-Driven Healthcare: How Analytics and BI are Transforming the Industry provides tables, checklists, and forms that allow you to take immediate action in implementing BI in your organization. You can't afford to be behind the curve. The industry is moving on, with or without you. Data-Driven Healthcare: How Analytics and BI are Transforming the Industry is your guide to utilizing data to advance your operation in an industry where data-fueled growth will be the new norm.

Health Analytics

Health Analytics
Author :
Publisher : John Wiley & Sons
Total Pages : 277
Release :
ISBN-10 : 9781118383049
ISBN-13 : 1118383044
Rating : 4/5 (49 Downloads)

Synopsis Health Analytics by : Jason Burke

A hands-on, analytics road map for health industry leaders The industry-wide transformation taking place across the health and life sciences ecosystem is mandating that organizations adopt new decision-making capabilities, based on science and real-world information. Analytics will be a required competency for the modern health enterprise; this book is about how to "cross the chasm." The ultimate analytics guide for the health industry leader, this essential book equips business leaders with little-to-no experience in analytics to understand how to incorporate analytics as a cornerstone of their 21st century competitive business strategy. Paints the picture for a new health enterprise, one focused on the patient Explores the financial components of this new operating model, using analytics to optimize the tradeoffs between cost and value Deals with the rising role of the consumer, using analytics to create a completely new health engagement model with individual recipients of care Looks at how analytics can drive innovations in care practice, patient-experienced medical outcomes, and analytically driven novel therapies optimized for the individual patient Presents a variety of text, tables, and graphics illustrating the various concepts being described Within each section and chapter, Health Analytics assesses the current landscape, proposing a new model/concept, sharing real-world stories of how the old and new world come together, and framing a "how-to" for the reader in terms of growing that particular set of capabilities in their own enterprises.

An Introduction to Statistical Computing with SAS (First Edition)

An Introduction to Statistical Computing with SAS (First Edition)
Author :
Publisher :
Total Pages : 288
Release :
ISBN-10 : 1516596471
ISBN-13 : 9781516596478
Rating : 4/5 (71 Downloads)

Synopsis An Introduction to Statistical Computing with SAS (First Edition) by : Brianna Magnusson

SAS Data Management for Public Health: An Introduction equips readers with the tools and knowledge they need to prepare public health data in SAS Data Management software for use in analysis. Highly accessible in nature, the book is specifically designed to help students who are new to SAS learn and master the system. The book is organized into 20 lessons. The opening lessons introduce SAS and provide tips and best practices for exploring data. Students are introduced to PROC MEANS, FREQ, UNIVARIATE, and PROC SGPLOT. They learn how to import data; merge, concatenate, and manage variables; perform data cleanup; and recode categorical and continuous variables. Specific lessons address comments, labels, and titles, formatting variables, conditional recoding, DO groups, arrays for recoding, and categorical data analysis. Closing lessons introduce stratified and subpopulation analysis, as well as logistic regression. The book includes an appendix to help students navigate and use SAS Studio. SAS Data Management for Public Health is an ideal resource for standalone courses in which SAS is taught or to complement any biostatistics or epidemiology course where students need to use SAS to analyze their data. Brianna Magnusson holds a Ph.D. in epidemiology and a M.P.H. from Virginia Commonwealth University. She is an associate professor in the Department of Public Health at Brigham Young University. Dr. Magnusson's research focuses on sexual and reproductive health with emphasis on factors influencing sexual decision-making. Caroline Stampfel holds an M.P.H. with a concentration in environmental epidemiology from the Yale School of Public Health. She serves as the director of programs for the Association of Maternal & Child Health Programs and leads a team of maternal and child health experts using data-driven, innovative approaches to improve the health and well-being of women, children, youth, families, and communities.

Real World Health Care Data Analysis

Real World Health Care Data Analysis
Author :
Publisher :
Total Pages : 0
Release :
ISBN-10 : 1642958018
ISBN-13 : 9781642958010
Rating : 4/5 (18 Downloads)

Synopsis Real World Health Care Data Analysis by : Douglas Faries

Real world health care data from observational studies, pragmatic trials, patient registries, and databases is common and growing in use. Real World Health Care Data Analysis: Causal Methods and Implementation in SAS® brings together best practices for causal-based comparative effectiveness analyses based on real world data in a single location. Example SAS code is provided to make the analyses relatively easy and efficient.The book also presents several emerging topics of interest, including algorithms for personalized medicine, methods that address the complexities of time varying confounding, extensions of propensity scoring to comparisons between more than two interventions, sensitivity analyses for unmeasured confounding, and implementation of model averaging.