Lloyd P. Provost, Sandra K. Murray

The Health Care Data Guide (eBook, ePUB)

Learning from Data for Improvement

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 656
• Erscheinungstermin: 26. Mai 2022
• Englisch
• ISBN-13: 9781119690122
• Artikelnr.: 64138753

Autorenporträt

LLOYD P. PROVOST is a cofounder of Associates in Process Improvement, the developers of the Model for Improvement roadmap and the Quality as a Business Strategy template for focusing organizations on improvement. Lloyd is a senior fellow at the Institute for Healthcare Improvement, where he supports the use of data for learning in programs. SANDRA K. MURRAY is a principal in Corporate Transformation Concepts, an independent consulting firm. She is faculty for the Institute for Healthcare Improvement's year-long Improvement Advisor Professional Development Program and their Breakthrough Series College. Sandra has taught numerous programs through the National Association for Healthcare Quality. Her cohort of client organizations encompasses the spectrum of health care delivery.

Inhaltsangabe

Figures, Tables, and Exhibits xiii

Preface xxix

Acknowledgments xxxiii

The Authors xxxv

About the Companion Website xxxvii

Part I Using Data for Improvement 1

Chapter 1 Improvement Methodology 3

Fundamental Questions for Improvement 4

What Are We Trying to Accomplish? 5

How Will We Know that a Change is an Improvement? 7

What Changes Can We Make That Will Result in Improvement? 8

The PDSA Cycle for Improvement 9

Tools and Methods to Support the Model for Improvement 13

Designing PDSA Cycles for Testing Changes 15

Analysis of Data from PDSA Cycles 19

Summary26 Key Terms 26

Chapter 2 Using Data for Improvement 27

What Does the Concept of Data Mean? 27

How are Data Used? 29

Types of Data 36

Using A Family of Measures 43

The Importance of Operational Definitions 47

Data for Different Types of Studies 51

Sampling53 Sampling Strategies 55

What About Sample Size? 58

Stratification of Data 61

What about Case-Mix Adjustment? 63

Transforming Data 65

Analysis and Presentation of Data 68

Summary75 Key Terms 75

Chapter 3 Understanding Variation Using Run Charts 77

Introduction77 What Is a Run Chart? 77

Use of a Run Chart 80

Constructing a Run Chart 80

Examples of Run Charts for Improvement Projects 84

Rules to Aid in Interpreting Run Charts 89

Special Issues in Using Run Charts 97

Stratification with Run Charts 113

Using the Cumulative Sum Statistic with Run Charts 116

Summary120 Key Terms 121

Chapter 4 Learning from Variation in Data 123

The Concept of Variation 123

Introduction to Shewhart Charts 129

Depicting and Interpreting Variation Using Shewhart Charts 135

The Role of Annotation with Shewhart Charts 140

Establishing Limits for Shewhart Charts 141

Revising Limits for Shewhart Charts 145

Stratification with Shewhart Charts 147

Shewhart Charts and Targets, Goals, or Other Specifications 152

Special Cause: Is It Good or Bad? 155

Summary157 Key Terms 158

Chapter 5 Understanding Variation Using Shewhart Charts 159

Selecting the Type of Shewhart Chart 160

Shewhart Charts for Continuous Data 163

I Charts 164

Examples of Shewhart Charts for Individual Measurements 166

Rational Ordering with an I Chart 168

Example of I Chart for Deviations from a Target 170

Xbar S Shewhart Charts 171

Shewhart Charts for Attribute Data 177

Subgroup Size for Attribute Charts 178

The P Chart for Classification Data 180

Examples of P Charts 182

Creation of Funnel Limits for a P Chart 186

Shewhart Charts for Counts of Nonconformities 188

c charts 190

U Chart 192

Creation of Funnel Limits for a U Chart 195

Alternatives for Attribute Charts for Rare Events 197

G Chart for Opportunities Between Rare Events 198

T Chart for Time Between Rare Events 202

Process Capability 206

Process Capability from an I Chart 208

Capability of a Process from Xbar and S Charts 208

Capability of a Process from Attribute Control Charts 210

Capability from a P Chart 210

Capability from a C or U Chart 210

Summary211 Key Terms 212

Appendix 5.1 Calculating Shewhart Limits 213

I Chart