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

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.

Figures, Tables, and Exhibits xiPreface xxvThe Authors xxixPart I Using Data for Improvement 1Chapter 1 Improvement Methodology 3Fundamental Questions for Improvement 4What Are We Trying to Accomplish? 5How Will We Know That a Change Is an Improvement? 6What Changes Can We Make That Will Result in Improvement? 7The PDSA Cycle for Improvement 8Tools and Methods to Support the Model for Improvement 11Analysis of Data from PDSA Cycles 18Chapter 2 Using Data for Improvement 25What Does the Concept of Data Mean? 25How Are Data Used? 26Types of Data 32The Importance of Operational Defi nitions 37Data for Different Types of Studies 40Use of Sampling 42What About Sample Size? 45Stratifi cation of Data 49What About Risk or Case-Mix Adjustment? 51Transforming Data 52Analysis and Presentation of Data 58Using a Family of Measures 61Chapter 3 Understanding Variation Using Run Charts 67Introduction 67What Is a Run Chart? 67Use of a Run Chart 68Constructing a Run Chart 69Examples of Run Charts for Improvement Projects 70Probability-Based Tests to Aid in Interpreting Run Charts 76Special Issues in Using Run Charts 85Stratification with Run Charts 99Using the Cumulative Sum Statistic with Run Charts 101Chapter 4 Learning from Variation in Data 107The Concept of Variation 107Depicting Variation 110Introduction to Shewhart Charts 113Interpretation of a Shewhart Chart 116Establishing and Revising Limits for Shewhart Charts 121When Do We Revise Limits? 124Stratifi cation with Shewhart Charts 126Rational Subgrouping 128Shewhart Charts with Targets, Goals, or Other Specifi cations 131Special Cause: Is It Good or Bad? 133Other Tools for Learning from Variation 136Chapter 5 Understanding Variation Using Shewhart Charts 149Selecting the Type of Shewhart Chart 149Shewhart Charts for Continuous Data 152I Charts 152Examples of Shewhart Charts for Individual Measurements 155Rational Ordering with an Individual Chart 158Effect of the Distribution of the Measurements 158Example of Individual Chart for Deviations from a Target 159X - and S Shewhart Charts 160Shewhart Charts for Attribute Data 163The P Chart for Classifi cation Data 166C and U Charts for Counts of Nonconformities 173Process Capability 184Process Capability from an I Chart 186Capability of a Process from X- and S Chart (or R chart) 187Capability of a Process from Attribute Control Charts 188Capability from a P Chart 188Capability from a C or U Chart 189Appendix 5.1 Calculating Shewhart Limits 192I Chart 192X - and S Charts 193X - and S Control Chart Calculation Form 195P Chart 197P Chart Calculation Form: Constant Subgroup Size 197P Chart Calculation Form: Variable Subgroup Size 198C Chart 199U Chart 200Chapter 6 Shewhart Chart Savvy: Dealing with Some Issues 201Designing Effective Shewhart Charts 201Tip 1: Type of Data and Subgroup Size 201Tip 2: Rounding Data 202Tip 3: Formatting Charts 202Typical Problems with Software for Calculating Shewhart Charts 207Characteristics to Consider When Purchasing SPC Software 211Some Cautions When Using I Charts 211Part II Advanced Theory and Methods with Data 217Chapter 7 More Shewhart-Type Charts 219Other Shewhart-Type Charts 220NP Chart 221X - and Range (R) Chart 221Median Chart 224Shewhart Charts for Rare Events 226G Chart for Opportunities Between Rare Events 228T Chart for Time Between Rare Events 229Some Alternatives to Shewhart-Type Charts 231Moving Average Chart 233Cumulative Sum (CUSUM) Chart 236Exponentially Weighted Moving Average (EWMA) 242Standardized Shewhart Charts 244Multivariate Shewhart-Type Charts 245Chapter 8 Special Uses for Shewhart Charts 253Shewhart Charts with a Changing Center Line 253Shewhart Charts with a Sloping Center Line 253Shewhart Charts with Seasonal Effects 255Transformation of Data with Shewhart Charts 258Shewhart Charts for Autocorrelated Data 264Shewhart Charts for Attribute Data with Large Subgroup Sizes(Over-Dispersion) 269Prime Charts (pand U) 269Comparison Charts 274Confidence Intervals and Confi dence Limits 275Shewhart Charts for Case-Mix Adjustment 278Chapter 9 Drilling Down into Aggregate Data for Improvement 281What Are Aggregate Data? 281What Is the Challenge Presented by Aggregate Data? 282Introduction to the Drill Down Pathway 285Stratification 287Sequencing 288Rational Subgrouping 288An Illustration of the Drill Down Pathway: Adverse Drug Events (ADES) 289Drill Down Pathway Step One 290Drill Down Pathway Step Two 290Drill Down Pathway Step Three 292Drill Down Pathway Step Four 297Drill Down Pathway Step Five 302Drill Down Pathway Step Six 304Part III Applications of Shewhart Charts in Health Care 307Chapter 10 Learning from Individual Patient Data 309Examples of Shewhart Charts for Individual Patients 310Example 1: Temperature Readings for a Hospitalized Patient 311Example 2: Bone Density for a Patient Diagnosed with Osteoporosis 313Example 3: PSA Screening for Prostate Cancer 314Example 4: Shewhart Charts for Continuous Monitoring of Patients 316Example 5: Asthma Patient Use of Shewhart Charts 318Example 6: Monitoring Weight 318Example 7: Monitoring Blood Sugar Control for Patients with Diabetes 320Example 8: Monitoring Patient Measures in the Hospital 321Example 9: Using Shewhart Charts in Pain Management 322Chapter 11 Learning from Patient Feedback to Improve Care 325Patient Surveys 326Summarizing Patient Feedback Data 329Presentation of Patient Satisfaction Data 336Using Patient Feedback for Improvement 337The Plan-Do-Study-Act Cycles (PDSA) Cycle for Testing andImplementing Changes 338Using Patient Satisfaction Data in Planning for Improvement 344Special Issues with Patient Feedback Data 346Are There Challenges When Summarizing and Using Patient SatisfactionSurvey Data? 346Does Survey Scale Matter? 347Chapter 12 Using Shewhart Charts in Health Care Leadership 349A Health Care Organization's Vector of Measures 349Developing a Vector of Measures 350Displaying and Learning from a Vector of Measures 351"So How Do We Best Display a Vector of Measures?" 358Administrative Issues with Vector of Measures 361Some Examples of Other Vectors of Measures 362Emergency Department: 363Primary Care Center 364Health Authority 364Large Urban Hospital 366Part IV Case Studies 369Chapter 13 Case Studies Using Shewhart Charts 371Case Study A: Improving Access to a Specialty Care Clinic 372Case Study B: Radiology Improvement Projects 381Case Study C: Reducing Post-CABG Infections 388Case Study D: Drilling Down into Percentage of C-Sections 399Case Study E: Accidental Puncture/Laceration Rate 409Case Study F: Reducing Hospital Readmissions 418Case Study G: Variation in Financial Data 425Index 435Shewhart Chart Selection Guide 446