The Health Care Data Guide

Learning from Data for Improvement
1. Auflage 2011
ISBN: 978-0-470-90258-5
Verlag: Wiley

Buch, Englisch, 480 Seiten, Format (B × H): 216 mm x 277 mm, Gewicht: 1190 g

Step by step this comprehensive resource explores the statistical process control (SPC), a philosophy, a strategy, and a set of methods for ongoing improvement of processes and systems to yield better outcomes in health care organizations. It includes information on processes, stratification, rational subgrouping and stability and capability analysis, measurement, data collection methods, planned experimentation, and graphical methods. This book shows how to apply SPC to evaluate current process performance, search for ideas for improvement, tell if changes have resulted in evidence of improvement, and track implementation efforts to document sustainability of the improvement.

Autoren/Hrsg.

Provost, Lloyd P

Murray, Sandra

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Public Health, Gesundheitsmanagement, Gesundheitsökonomie, Gesundheitspolitik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik

Inhaltsverzeichnis

Figures, Tables, and Exhibits xi

Preface xxv

The Authors xxix

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? 6

What Changes Can We Make That Will Result in Improvement? 7

The PDSA Cycle for Improvement 8

Tools and Methods to Support the Model for Improvement 11

Analysis of Data from PDSA Cycles 18

Chapter 2 Using Data for Improvement 25

What Does the Concept of Data Mean? 25

How Are Data Used? 26

Types of Data 32

The Importance of Operational Defi nitions 37

Data for Different Types of Studies 40

Use of Sampling 42

What About Sample Size? 45

Stratifi cation of Data 49

What About Risk or Case-Mix Adjustment? 51

Transforming Data 52

Analysis and Presentation of Data 58

Using a Family of Measures 61

Chapter 3 Understanding Variation Using Run Charts 67

Introduction 67

What Is a Run Chart? 67

Use of a Run Chart 68

Constructing a Run Chart 69

Examples of Run Charts for Improvement Projects 70

Probability-Based Tests to Aid in Interpreting Run Charts 76

Special Issues in Using Run Charts 85

Stratification with Run Charts 99

Using the Cumulative Sum Statistic with Run Charts 101

Chapter 4 Learning from Variation in Data 107

The Concept of Variation 107

Depicting Variation 110

Introduction to Shewhart Charts 113

Interpretation of a Shewhart Chart 116

Establishing and Revising Limits for Shewhart Charts 121

When Do We Revise Limits? 124

Stratifi cation with Shewhart Charts 126

Rational Subgrouping 128

Shewhart Charts with Targets, Goals, or Other Specifi cations 131

Special Cause: Is It Good or Bad? 133

Other Tools for Learning from Variation 136

Chapter 5 Understanding Variation Using Shewhart Charts 149

Selecting the Type of Shewhart Chart 149

Shewhart Charts for Continuous Data 152

I Charts 152

Examples of Shewhart Charts for Individual Measurements 155

Rational Ordering with an Individual Chart 158

Effect of the Distribution of the Measurements 158

Example of Individual Chart for Deviations from a Target 159

X - and S Shewhart Charts 160

Shewhart Charts for Attribute Data 163

The P Chart for Classifi cation Data 166

C and U Charts for Counts of Nonconformities 173

Process Capability 184

Process Capability from an I Chart 186

Capability of a Process from X- and S Chart (or R chart) 187

Capability of a Process from Attribute Control Charts 188

Capability from a P Chart 188

Capability from a C or U Chart 189

Appendix 5.1 Calculating Shewhart Limits 192

I Chart 192

X - and S Charts 193

X - and S Control Chart Calculation Form 195

P Chart 197

P Chart Calculation Form: Constant Subgroup Size 197

P Chart Calculation Form: Variable Subgroup Size 198

C Chart 199

U Chart 200

Chapter 6 Shewhart Chart Savvy: Dealing with Some Issues 201

Designing Effective Shewhart Charts 201

Tip 1: Type of Data and Subgroup Size 201

Tip 2: Rounding Data 202

Tip 3: Formatting Charts 202

Typical Problems with Software for Calculating Shewhart Charts 207

Characteristics to Consider When Purchasing SPC Software 211

Some Cautions When Using I Charts 211

Part II Advanced Theory and Methods with Data 217

Chapter 7 More Shewhart-Type Charts 219

Other Shewhart-Type Charts 220

NP Chart 221

X - and Range (R) Chart 221

Median Chart 224

Shewhart Charts for Rare Events 226

G Chart for Opportunities Between Rare Events 228

T Chart for Time Between Rare Events 229

Some Alternatives to Shewhart-Type Charts 231

Moving Average Chart 233

Cumulative Sum (CUSUM) Chart 236

Exponentially Weighted Moving Average (EWMA) 242

Standardized Shewhart Charts 244

Multivariate Shewhart-Type Charts 245

Chapter 8 Special Uses for Shewhart Charts 253

Shewhart

Über Autor(innen)

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 active cohort of client organizations encompasses the spectrum of health care delivery.