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More About This Title The Health Care Data Guide: Learning from Data for Improvement
English
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: www.josseybass.com/go/provost
English
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.
English
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 Definitions 37
Data for Different Types of Studies 40
Use of Sampling 42
What About Sample Size? 45
Stratification 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
Stratification with Shewhart Charts 126
Rational Subgrouping 128
Shewhart Charts with Targets, Goals, or Other Specifications 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
X̅ and S Shewhart Charts 160
Shewhart Charts for Attribute Data 163
The P Chart for Classification 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 X̅ 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
X̅ and S Charts 193
X̅ 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
X̅ 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 (p’ and U’) 269
Comparison Charts 274
Confidence Intervals and Confidence 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
