Health Care Analytics the Ultimate Step-By-Step Guide

Health Care Analytics the Ultimate Step-By-Step Guide

Author: Gerardus Blokdyk

Publisher: 5starcooks

Published: 2018-01-27

Total Pages: 128

ISBN-13: 9780655152989

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Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Health care analytics? Does Health care analytics appropriately measure and monitor risk? How will you know that the Health care analytics project has been successful? Does the Health care analytics task fit the client's priorities? Do we aggressively reward and promote the people who have the biggest impact on creating excellent Health care analytics services/products? Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role... In EVERY company, organization and department. Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Health care analytics investments work better. This Health care analytics All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Health care analytics Self-Assessment. Featuring 724 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Health care analytics improvements can be made. In using the questions you will be better able to: - diagnose Health care analytics projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Health care analytics and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Health care analytics Scorecard, you will develop a clear picture of which Health care analytics areas need attention. Your purchase includes access details to the Health care analytics self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.


Healthcare Analytics Made Simple

Healthcare Analytics Made Simple

Author: Vikas (Vik) Kumar

Publisher: Packt Publishing Ltd

Published: 2018-07-31

Total Pages: 258

ISBN-13: 1787283224

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Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python and SQL Build predictive models on real healthcare data with pandas and scikit-learn Use analytics to improve healthcare performance Book Description In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. What you will learn Gain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processes Use SQL and Python to analyze data Measure healthcare quality and provider performance Identify features and attributes to build successful healthcare models Build predictive models using real-world healthcare data Become an expert in predictive modeling with structured clinical data See what lies ahead for healthcare analytics Who this book is for Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.


The Health Care Data Guide

The Health Care Data Guide

Author: Lloyd P. Provost

Publisher: John Wiley & Sons

Published: 2011-12-06

Total Pages: 480

ISBN-13: 1118085884

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The Health Care Data Guide is designed to help students and professionals build a skill set specific to using data for improvement of health care processes and systems. Even experienced data users will find valuable resources among the tools and cases that enrich The Health Care Data Guide. Practical and step-by-step, this book spotlights statistical process control (SPC) and develops a philosophy, a strategy, and a set of methods for ongoing improvement to yield better outcomes. Provost and Murray reveal how to put SPC into practice for a wide range of applications including evaluating current process performance, searching for ideas for and determining evidence of improvement, and tracking and documenting sustainability of improvement. A comprehensive overview of graphical methods in SPC includes Shewhart charts, run charts, frequency plots, Pareto analysis, and scatter diagrams. Other topics include stratification and rational sub-grouping of data and methods to help predict performance of processes. Illustrative examples and case studies encourage users to evaluate their knowledge and skills interactively and provide opportunity to develop additional skills and confidence in displaying and interpreting data. Companion Web site: www.josseybass.com/go/provost


The Health Care Data Guide

The Health Care Data Guide

Author: Lloyd P. Provost

Publisher: John Wiley & Sons

Published: 2022-05-26

Total Pages: 659

ISBN-13: 1119690129

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An Essential text on transforming raw data into concrete health care improvements Now in its second edition, The Health Care Data Guide: Learning from Data for Improvement delivers a practical blueprint for using available data to improve healthcare outcomes. In the book, a team of distinguished authors explores how health care practitioners, researchers, and other professionals can confidently plan and implement health care enhancements and changes, all while ensuring those changes actually constitute an improvement. This book is the perfect companion resource to The Improvement Guide: A Practical Approach to Enhancing Organizational Peformance, Second Edition, and offers fulsome discussions of how to use data to test, adapt, implement, and scale positive organizational change. The Health Care Data Guide: Learning from Data for Improvement, Second Edition provides: Easy to use strategies for learning more readily from existing health care data Clear guidance on the most useful graph for different types of data used in health care A step-by-step method for making use of highly aggregated data for improvement Examples of using patient-level data in care Multiple methods for making use of patient and other feedback data A vastly better way to view data for executive leadership Solutions for working with rare events data, seasonality and other pesky issues Use of improvement methods with epidemic data Improvement case studies using data for learning A must read resource for those committed to improving health care including allied health professionals in all aspects of health care, physicians, managers, health care leaders, and researchers.


The Power of Prediction in Health Care: A Step-by-step Guide to Data Science in Health Care

The Power of Prediction in Health Care: A Step-by-step Guide to Data Science in Health Care

Author: Rafiq Muhammad

Publisher: Rafiq Muhammad

Published: 2024-01-12

Total Pages: 181

ISBN-13: 9198900749

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Are you an aspiring data science student or early career researcher taking your first steps into data science? Are you overwhelmed and lost in the vast sea of information? This simplified data science guide is for you. This book provides a step-by-step approach to how data science projects can be conceptualized, designed, and developed in health care by aspiring data scientists. We will start on an educational journey that equips graduate students and early career researchers with hands-on knowledge and practical skills so they may fully realize the amazing potential of data science in healthcare. The book provides: step-by-step approach to designing and developing data science projects in healthcare easy-to-understand structure to facilitate the development of data science projects for beginners links to useful resources and tools (mostly free and open source) that help build and execute AI projects in healthcare links to free-to-use healthcare databases Data science case study examples that demonstrate how to build data science projects Whether you are a healthcare professional looking to enhance your skills or a data scientist seeking to work in the healthcare industry, "The Power of Prediction in Health Care" is an essential guide to unlocking the potential of data science in healthcare. With real-world examples and practical advice, this book will empower you to make data-driven decisions that improve patient outcomes and transform healthcare.


Analytics in Healthcare and the Life Sciences

Analytics in Healthcare and the Life Sciences

Author: Dwight McNeill

Publisher: Pearson Education

Published: 2014

Total Pages: 351

ISBN-13: 0133407330

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Make healthcare analytics work: leverage its powerful opportunities for improving outcomes, cost, and efficiency.This book gives you thepractical frameworks, strategies, tactics, and case studies you need to go beyond talk to action. The contributing healthcare analytics innovators survey the field's current state, present start-to-finish guidance for planning and implementation, and help decision-makers prepare for tomorrow's advances. They present in-depth case studies revealing how leading organizations have organized and executed analytic strategies that work, and fully cover the primary applications of analytics in all three sectors of the healthcare ecosystem: Provider, Payer, and Life Sciences. Co-published with the International Institute for Analytics (IIA), this book features the combined expertise of IIA's team of leading health analytics practitioners and researchers. Each chapter is written by a member of the IIA faculty, and bridges the latest research findings with proven best practices. This book will be valuable to professionals and decision-makers throughout the healthcare ecosystem, including provider organization clinicians and managers; life sciences researchers and practitioners; and informaticists, actuaries, and managers at payer organizations. It will also be valuable in diverse analytics, operations, and IT courses in business, engineering, and healthcare certificate programs.


Artificial Intelligence in Healthcare

Artificial Intelligence in Healthcare

Author: Adam Bohr

Publisher: Academic Press

Published: 2020-06-21

Total Pages: 385

ISBN-13: 0128184396

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Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data


Healthcare Analytics for Quality and Performance Improvement

Healthcare Analytics for Quality and Performance Improvement

Author: Trevor L. Strome

Publisher: John Wiley & Sons

Published: 2013-10-02

Total Pages: 246

ISBN-13: 1118760158

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Improve patient outcomes, lower costs, reduce fraud—all with healthcare analytics Healthcare Analytics for Quality and Performance Improvement walks your healthcare organization from relying on generic reports and dashboards to developing powerful analytic applications that drive effective decision-making throughout your organization. Renowned healthcare analytics leader Trevor Strome reveals in this groundbreaking volume the true potential of analytics to harness the vast amounts of data being generated in order to improve the decision-making ability of healthcare managers and improvement teams. Examines how technology has impacted healthcare delivery Discusses the challenge facing healthcare organizations: to leverage advances in both clinical and information technology to improve quality and performance while containing costs Explores the tools and techniques to analyze and extract value from healthcare data Demonstrates how the clinical, business, and technology components of healthcare organizations (HCOs) must work together to leverage analytics Other industries are already taking advantage of big data. Healthcare Analytics for Quality and Performance Improvement helps the healthcare industry make the most of the precious data already at its fingertips for long-overdue quality and performance improvement.


Using Predictive Analytics to Improve Healthcare Outcomes

Using Predictive Analytics to Improve Healthcare Outcomes

Author: John W. Nelson

Publisher: John Wiley & Sons

Published: 2021-07-21

Total Pages: 188

ISBN-13: 1119747759

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Using Predictive Analytics to Improve Healthcare Outcomes Winner of the American Journal of Nursing (AJN) Informatics Book of the Year Award 2021! Discover a comprehensive overview, from established leaders in the field, of how to use predictive analytics and other analytic methods for healthcare quality improvement. Using Predictive Analytics to Improve Healthcare Outcomes delivers a 16-step process to use predictive analytics to improve operations in the complex industry of healthcare. The book includes numerous case studies that make use of predictive analytics and other mathematical methodologies to save money and improve patient outcomes. The book is organized as a “how-to” manual, showing how to use existing theory and tools to achieve desired positive outcomes. You will learn how your organization can use predictive analytics to identify the most impactful operational interventions before changing operations. This includes: A thorough introduction to data, caring theory, Relationship-Based Care®, the Caring Behaviors Assurance System©, and healthcare operations, including how to build a measurement model and improve organizational outcomes. An exploration of analytics in action, including comprehensive case studies on patient falls, palliative care, infection reduction, reducing rates of readmission for heart failure, and more—all resulting in action plans allowing clinicians to make changes that have been proven in advance to result in positive outcomes. Discussions of how to refine quality improvement initiatives, including the use of “comfort” as a construct to illustrate the importance of solid theory and good measurement in adequate pain management. An examination of international organizations using analytics to improve operations within cultural context. Using Predictive Analytics to Improve Healthcare Outcomes is perfect for executives, researchers, and quality improvement staff at healthcare organizations, as well as educators teaching mathematics, data science, or quality improvement. Employ this valuable resource that walks you through the steps of managing and optimizing outcomes in your clinical care operations.