A new title in the acclaimed Understanding series that focuses on the science of healthcare delivery Over the past decade, the subject of Systems Science has skyrocketed in importance in the healthcare field. With its engaging, clinically relevant style, Understanding Healthcare Delivery Science is the perfect introduction to this timely topic. It covers every aspect of what actually constitutes "best care" and how it can be most efficiently delivered from an operational standpoint. The book is exceptional for two other reasons: numerous case vignettes put the content in a clinically relevant…mehr
A new title in the acclaimed Understanding series that focuses on the science of healthcare delivery Over the past decade, the subject of Systems Science has skyrocketed in importance in the healthcare field. With its engaging, clinically relevant style, Understanding Healthcare Delivery Science is the perfect introduction to this timely topic. It covers every aspect of what actually constitutes "best care" and how it can be most efficiently delivered from an operational standpoint. The book is exceptional for two other reasons: numerous case vignettes put the content in a clinically relevant framework, and its comprehensive coverage spans everything from quality and safety to data and policy. Readers will find a valuable opening section that delivers an outstanding introductory discussion of Healthcare Delivery Science Co-author Dr. Michael Howell is a nationally recognized expert on healthcare quality, whose research has been covered by The New York Times, CNN, and Consumer Reports. He has served on national quality- and safety-related national advisory panels for the CDC, Society of Critical Care Medicine, CMS, and others. An active healthcare delivery scientist, Dr. Howell has published more than 90 research articles, editorials, and book chapters on topics related to quality, safety, patient-centeredness, and critical care.
Michael Howell, MD MPH is a nationally recognized expert on healthcare quality and patient safety who has served on quality- and safety-related national advisory panels for the CDC, Medicare, the National Academy of Medicine, and others. An active healthcare delivery scientist with more than 100 research articles, editorials, and book chapters, his research has been covered by The New York Times, CNN, and Consumer Reports. Jennifer Stevens, MD directs the Center for Healthcare Delivery Science at Beth Israel Deaconess Medical Center in Boston, MA. A member of the Harvard Medical School faculty since 2015, she is actively training the next generation of healthcare delivery scientists. Dr. Stevens' research on the opioid epidemic in ICUs, new ways to identify and mitigate patient harm in overtaxed ICUs, and other critical healthcare delivery science issues has been featured in the Washington Post, NPR, and on the front page of the Boston Globe.
Inhaltsangabe
PART I: WHAT IS HEALTHCARE DELIVERY SCIENCE, AND WHY DO WE NEED IT? Chapter 1 Introduction The Problem: How Research and Operations Are Organized in Healthcare Today Historical Context: How Did It Get This Way? Why Now Is Different: Two Key Changes in Context Why It Matters: Problems with Thinking Too Simply About Healthcare Healthcare Delivery Science References Chapter 2 Complexity What Happens When We View Healthcare as Complicated? What Is a Complex Adaptive System? Why It Matters: Fitting the Right Measurement Tool to the Question Healthcare Delivery Science: A Field of Research Where Healthcare Itself Is the Organism Under Study References Chapter 3 Quality and Safety in Healthcare The Best the World Has Ever Seen Three Critical Papers to Know An Inflection Point: To Err Is Human and Crossing the Quality Chasm More Recent Estimates About Deaths from Medical Error International Comparisons Have Improvement Efforts Worked? How We Put It All Together References Chapter 4 What Does the Future Hold? Introduction Value Drives Change The "Postsafety" Era Healthcare Delivery That Delivers Health Consumerism Versus Personalization The Doctor Will See You Now? Informed Healthcare Information Technology (IT) Conclusions References PART II: MAKING CHANGE IN THE REAL WORLD-TOOLS FOR HEALTHCARE IMPROVEMENT Chapter 5 Human Factors Human Factors: An Introduction Cognitive Reasoning, Errors, and Biases in Healthcare Hierarchy: What Is It, How Do We Measure It, and Why Does It Matter? Tools for Understanding Complex Systems Conclusions References Chapter 6 How Teams Work Types of Teams What Do Teams Need to Succeed? Poorly Functioning Teams in Healthcare Teams in Aviation and the Birth of Crew Resource Management (CRM) CRM in Healthcare Leading Teams Through Change References Chapter 7 Leadership and Culture Change Leading Change Is Difficult Where to Start What Is Implementation Science? Implementation Science Frameworks Integrating Implementation Science Frameworks for the Purpose of Change Management References Chapter 8 Standard Quality Improvement Tools and Techniques Introduction Preventing Adverse Events and Improving Patient Safety Identifying Patient Safety Events Root Cause Analysis (RCA) Failure Mode Effects (and Criticality) Analysis (FMEA and FMECA) Safety I and Safety II Process Improvement and Quality Improvement References Chapter 9 Lean Improvement Techniques in Healthcare A Brief History of Lean The Rules of Lean A Concrete Definition of the Ideal The 8 Wastes Tools from Lean Summary References Chapter 10 Partnering with Community, Professional, and Policy Organizations Introduction How Health Is Created Key Stakeholders in Shaping Health Engaging with Local Public Health Agencies Approaches to Successful Partnerships Concluding Thoughts Acknowledgments References PART III: SEEING THE TRUTH-ANALYTICS IN HEALTHCARE Chapter 11 Data in Healthcare Part 1: Fundamental Issues in Healthcare Data Part 2: The Importance of Understanding Data Lineage, and How This Leads Mature Organizations to Both Informal and Formal Data Governance Part 3: Basic Understanding of Relational Database Structures Part 4: Review of Common Approaches to Actually Accessing Healthcare Data Conclusion References Chapter 12 Measuring Quality and Safety Quality Measurement Frameworks What Are You Trying to Achieve? Improvement, Comparison, or Accountability What Makes a Good Measure? Challenges Common Measure Sets and Major Pay-For-Performance Programs References Chapter 13 Overview of Analytic Techniques and Common Pitfalls Dinosaur Footprints and What They Tell Us About Data Analysis in Healthcare The Four Horsemen of Mistaken Conclusions The Critical Importance of Missing Data The Shape of Data: Categories of Data and Why They Matter Overview of Analytic Methods References Chapter 14 Everyday Analytics Summarizing Your Data Displaying Data Outcomes Over Time, Part I - Run Charts How to Tell if Two Groups Are Different: Univariable Tests of Difference and Measures of Comparison Outcomes Over Time, Part 2-Statistical Process Control (SPC) Charts Everyday Analytics References Chapter 15 Survey-Based Data Introduction Perhaps the Most Important Thing You'll Learn in This Chapter What Are Some of the Main Purposes of Surveys? Overview of Conducting a Survey Some Pitfalls References Chapter 16 Predictive Modeling 1.0 and 2.0 What to Expect in This Chapter Predictive Modeling 1.0 Predictive Modeling 2.0 Taking Predictions to the Next Level References Chapter 17 Predictive Modeling 3.0: Machine Learning Definitions: What Is Artificial Intelligence? Machine Learning? A Brief History of Artificial Intelligence Translating Epidemiology to Machine Learning Categories of Machine Learning Used in Healthcare Pitfalls in Using Machine Learning in Healthcare The Future References Chapter 18 What Everyone Should Know About Risk Adjustment What Is Risk Adjustment, and Why We Should Care? What Risk Adjustments Are Available, and How Should We Assess Them? Examples of Risk Adjustment Gone Awry Using Risk Adjustment in Local Healthcare Delivery Science References Chapter 19 Modeling Patient Flow: Understanding Throughput and Census Why Does Understanding Patient Flow Matter? Understanding Patient Flow Conceptually Analytical Approaches to Understanding Patient Flow Summary References Chapter 20 Program Evaluation Causal Methods Quasi-Experimental Designs-Causal Inference in Observational Data Evaluations in the Real World References Chapter 21 How to Embed Healthcare Delivery Science Into Your Health System Introduction How Do I Join (or Build) a Community of Healthcare Delivery Science? How to Embed Healthcare Delivery Science in Your Health System Summary Reference Index
PART I: WHAT IS HEALTHCARE DELIVERY SCIENCE, AND WHY DO WE NEED IT? Chapter 1 Introduction The Problem: How Research and Operations Are Organized in Healthcare Today Historical Context: How Did It Get This Way? Why Now Is Different: Two Key Changes in Context Why It Matters: Problems with Thinking Too Simply About Healthcare Healthcare Delivery Science References Chapter 2 Complexity What Happens When We View Healthcare as Complicated? What Is a Complex Adaptive System? Why It Matters: Fitting the Right Measurement Tool to the Question Healthcare Delivery Science: A Field of Research Where Healthcare Itself Is the Organism Under Study References Chapter 3 Quality and Safety in Healthcare The Best the World Has Ever Seen Three Critical Papers to Know An Inflection Point: To Err Is Human and Crossing the Quality Chasm More Recent Estimates About Deaths from Medical Error International Comparisons Have Improvement Efforts Worked? How We Put It All Together References Chapter 4 What Does the Future Hold? Introduction Value Drives Change The "Postsafety" Era Healthcare Delivery That Delivers Health Consumerism Versus Personalization The Doctor Will See You Now? Informed Healthcare Information Technology (IT) Conclusions References PART II: MAKING CHANGE IN THE REAL WORLD-TOOLS FOR HEALTHCARE IMPROVEMENT Chapter 5 Human Factors Human Factors: An Introduction Cognitive Reasoning, Errors, and Biases in Healthcare Hierarchy: What Is It, How Do We Measure It, and Why Does It Matter? Tools for Understanding Complex Systems Conclusions References Chapter 6 How Teams Work Types of Teams What Do Teams Need to Succeed? Poorly Functioning Teams in Healthcare Teams in Aviation and the Birth of Crew Resource Management (CRM) CRM in Healthcare Leading Teams Through Change References Chapter 7 Leadership and Culture Change Leading Change Is Difficult Where to Start What Is Implementation Science? Implementation Science Frameworks Integrating Implementation Science Frameworks for the Purpose of Change Management References Chapter 8 Standard Quality Improvement Tools and Techniques Introduction Preventing Adverse Events and Improving Patient Safety Identifying Patient Safety Events Root Cause Analysis (RCA) Failure Mode Effects (and Criticality) Analysis (FMEA and FMECA) Safety I and Safety II Process Improvement and Quality Improvement References Chapter 9 Lean Improvement Techniques in Healthcare A Brief History of Lean The Rules of Lean A Concrete Definition of the Ideal The 8 Wastes Tools from Lean Summary References Chapter 10 Partnering with Community, Professional, and Policy Organizations Introduction How Health Is Created Key Stakeholders in Shaping Health Engaging with Local Public Health Agencies Approaches to Successful Partnerships Concluding Thoughts Acknowledgments References PART III: SEEING THE TRUTH-ANALYTICS IN HEALTHCARE Chapter 11 Data in Healthcare Part 1: Fundamental Issues in Healthcare Data Part 2: The Importance of Understanding Data Lineage, and How This Leads Mature Organizations to Both Informal and Formal Data Governance Part 3: Basic Understanding of Relational Database Structures Part 4: Review of Common Approaches to Actually Accessing Healthcare Data Conclusion References Chapter 12 Measuring Quality and Safety Quality Measurement Frameworks What Are You Trying to Achieve? Improvement, Comparison, or Accountability What Makes a Good Measure? Challenges Common Measure Sets and Major Pay-For-Performance Programs References Chapter 13 Overview of Analytic Techniques and Common Pitfalls Dinosaur Footprints and What They Tell Us About Data Analysis in Healthcare The Four Horsemen of Mistaken Conclusions The Critical Importance of Missing Data The Shape of Data: Categories of Data and Why They Matter Overview of Analytic Methods References Chapter 14 Everyday Analytics Summarizing Your Data Displaying Data Outcomes Over Time, Part I - Run Charts How to Tell if Two Groups Are Different: Univariable Tests of Difference and Measures of Comparison Outcomes Over Time, Part 2-Statistical Process Control (SPC) Charts Everyday Analytics References Chapter 15 Survey-Based Data Introduction Perhaps the Most Important Thing You'll Learn in This Chapter What Are Some of the Main Purposes of Surveys? Overview of Conducting a Survey Some Pitfalls References Chapter 16 Predictive Modeling 1.0 and 2.0 What to Expect in This Chapter Predictive Modeling 1.0 Predictive Modeling 2.0 Taking Predictions to the Next Level References Chapter 17 Predictive Modeling 3.0: Machine Learning Definitions: What Is Artificial Intelligence? Machine Learning? A Brief History of Artificial Intelligence Translating Epidemiology to Machine Learning Categories of Machine Learning Used in Healthcare Pitfalls in Using Machine Learning in Healthcare The Future References Chapter 18 What Everyone Should Know About Risk Adjustment What Is Risk Adjustment, and Why We Should Care? What Risk Adjustments Are Available, and How Should We Assess Them? Examples of Risk Adjustment Gone Awry Using Risk Adjustment in Local Healthcare Delivery Science References Chapter 19 Modeling Patient Flow: Understanding Throughput and Census Why Does Understanding Patient Flow Matter? Understanding Patient Flow Conceptually Analytical Approaches to Understanding Patient Flow Summary References Chapter 20 Program Evaluation Causal Methods Quasi-Experimental Designs-Causal Inference in Observational Data Evaluations in the Real World References Chapter 21 How to Embed Healthcare Delivery Science Into Your Health System Introduction How Do I Join (or Build) a Community of Healthcare Delivery Science? How to Embed Healthcare Delivery Science in Your Health System Summary Reference Index
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Shop der buecher.de GmbH & Co. KG Bürgermeister-Wegele-Str. 12, 86167 Augsburg Amtsgericht Augsburg HRA 13309
