The Health Care Data Guide

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.

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.

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