Dear customers, please be informed that our shopping cart will be unavailable between August 21 and September 1, 2014, as we will be making some changes to serve you better. To minimise any possible delivery disruption, we encourage you to make your purchases before August 21. We appreciate your understanding and apologise for any inconvenience.

Print this page Share

The Health Care Data Guide: Learning from Data for Improvement

ISBN: 978-0-470-90258-5
480 pages
October 2011, ©2011, Jossey-Bass
The Health Care Data Guide: Learning from Data for Improvement (0470902582) cover image

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:

See More
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 Charts with a Changing Center Line 253

Shewhart Charts with a Sloping Center Line 253

Shewhart Charts with Seasonal Effects 255

Transformation of Data with Shewhart Charts 258

Shewhart Charts for Autocorrelated Data 264

Shewhart Charts for Attribute Data with Large Subgroup Sizes

(Over-Dispersion) 269

Prime Charts (pand U) 269

Comparison Charts 274

Confidence Intervals and Confi dence Limits 275

Shewhart Charts for Case-Mix Adjustment 278

Chapter 9 Drilling Down into Aggregate Data for Improvement 281

What Are Aggregate Data? 281

What Is the Challenge Presented by Aggregate Data? 282

Introduction to the Drill Down Pathway 285

Stratification 287

Sequencing 288

Rational Subgrouping 288

An Illustration of the Drill Down Pathway: Adverse Drug Events (ADES) 289

Drill Down Pathway Step One 290

Drill Down Pathway Step Two 290

Drill Down Pathway Step Three 292

Drill Down Pathway Step Four 297

Drill Down Pathway Step Five 302

Drill Down Pathway Step Six 304

Part III Applications of Shewhart Charts in Health Care 307

Chapter 10 Learning from Individual Patient Data 309

Examples of Shewhart Charts for Individual Patients 310

Example 1: Temperature Readings for a Hospitalized Patient 311

Example 2: Bone Density for a Patient Diagnosed with Osteoporosis 313

Example 3: PSA Screening for Prostate Cancer 314

Example 4: Shewhart Charts for Continuous Monitoring of Patients 316

Example 5: Asthma Patient Use of Shewhart Charts 318

Example 6: Monitoring Weight 318

Example 7: Monitoring Blood Sugar Control for Patients with Diabetes 320

Example 8: Monitoring Patient Measures in the Hospital 321

Example 9: Using Shewhart Charts in Pain Management 322

Chapter 11 Learning from Patient Feedback to Improve Care 325

Patient Surveys 326

Summarizing Patient Feedback Data 329

Presentation of Patient Satisfaction Data 336

Using Patient Feedback for Improvement 337

The Plan-Do-Study-Act Cycles (PDSA) Cycle for Testing and

Implementing Changes 338

Using Patient Satisfaction Data in Planning for Improvement 344

Special Issues with Patient Feedback Data 346

Are There Challenges When Summarizing and Using Patient Satisfaction

Survey Data? 346

Does Survey Scale Matter? 347

Chapter 12 Using Shewhart Charts in Health Care Leadership 349

A Health Care Organization’s Vector of Measures 349

Developing a Vector of Measures 350

Displaying and Learning from a Vector of Measures 351

“So How Do We Best Display a Vector of Measures?” 358

Administrative Issues with Vector of Measures 361

Some Examples of Other Vectors of Measures 362

Emergency Department: 363

Primary Care Center 364

Health Authority 364

Large Urban Hospital 366

Part IV Case Studies 369

Chapter 13 Case Studies Using Shewhart Charts 371

Case Study A: Improving Access to a Specialty Care Clinic 372

Case Study B: Radiology Improvement Projects 381

Case Study C: Reducing Post-CABG Infections 388

Case Study D: Drilling Down into Percentage of C-Sections 399

Case Study E: Accidental Puncture/Laceration Rate 409

Case Study F: Reducing Hospital Readmissions 418

Case Study G: Variation in Financial Data 425

Index 435

Shewhart Chart Selection Guide 446

See More

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.

See More
Instructors Resources
Wiley Instructor Companion Site
See More
See Less
Buy Both and Save 25%!

The Health Care Data Guide: Learning from Data for Improvement (US $90.00)

-and- Pursuing the Triple Aim: Seven Innovators Show the Way to Better Care, Better Health, and Lower Costs (US $48.00)

Total List Price: US $138.00
Discounted Price: US $103.50 (Save: US $34.50)

Buy Both
Cannot be combined with any other offers. Learn more.
Back to Top