Statistical methods for healthcare performance monitoring

Cover
Half Title
Title Page
Copyright page
Table of Contents
List of Illustrations
List of Tables
Preface
Authors
1: Introduction
	1.1 The Need for Performance Monitoring
	1.2 Measuring and Monitoring Quality
	1.3 The Need for This Book
	1.4 Who Is This Book For and How Should It Be Used?
	Common Abbreviations Used in the Book
	Acknowledgment
2: Origins and Examples of Monitoring Systems
	Aims of This Chapter
	2.1 Origins
	2.2 Healthcare Scandals
		2.2.1 Responses to the Scandals
	2.3 Examples of Monitoring Schemes
	2.4 Goals of Monitoring
		2.4.1 Accountability
		2.4.2 Regulation and Accreditation
		2.4.3 Patient Choice
		2.4.4 Openness and Transparency
		2.4.5 Quality Improvement
		2.4.6 Prevent Harm and Unsafe Care
		2.4.7 Professionalism
		2.4.8 Informed Consent
3: Choosing the Unit of Analysis and Reporting
	Aims of This Chapter
	3.1 Issues Principally Concerning the Analysis
		3.1.1 Clustering (*)
		3.1.2 Episode Treatment Groups
	3.2 Issues More Relevant to Reporting: Attributing Performance to a Given Unit in a System
4: What to Measure: Choosing and Defining Indicators
	Aims of This Chapter
	4.1 How Can We Define Quality?
	4.2 Common Indicator Taxonomies
	4.3 Particular Challenges of Measuring Patient Safety
	4.4 Particular Challenges of Multimorbidity
	4.5 Measuring the Health of the Population and Quality of the Whole Healthcare System
		4.5.1 The WHO Annual World Health Statistics Report
	4.6 Efficiency and Value
		4.6.1 Data Envelopment Analysis and Stochastic Frontier Analysis (*)
	4.7 Features of an Ideal Indicator
	4.8 Steps in Construction and Common Issues in Definition
	4.9 Validation of Indicators
	4.10 Some Strategies for Choosing among Candidates
	4.11 Time to Go: When to Withdraw Indicators
	4.12 Conclusion
5: Sources of Data
	Aims of This Chapter
	5.1 How to Assess Data Quality
	5.2 Administrative Data
		5.2.1 Coding Systems for Administrative Data
		5.2.2 Use of Administrative Databases to Flag Patient Safety Events
	5.3 Clinical Registry Data
	5.4 Accuracy of Administrative and Clinical Databases Compared
	5.5 Incident Reports and Other Ways to Capture Safety Events
	5.6 Surveys
	5.7 Other Sources
	5.8 Other Issues Concerning Data Sources
	5.9 Conclusion
6: Risk-Adjustment Principles and Methods
	Aims of This Chapter
	6.1 Risk Adjustment and Risk Prediction
	6.2 When and Why Should We Adjust for Risk?
	6.3 Alternatives to Risk Adjustment
	6.4 What Factors Should We Adjust For?
		6.4.1 Factors Not under the Control of the Provider
		6.4.2 Proxies Such as Age and Socioeconomic Status
		6.4.3 Comorbidity
		6.4.4 Disease Severity
	6.5 Selecting an Initial Set of Candidate Variables
	6.6 Dealing with Missing and Extreme Values
	6.7 Timing of the Risk Factor Measurement
	6.8 Building the Model
		6.8.1 Choosing the Final Set of Variables from the Initial Set of Candidates
		6.8.2 Decide How Each Variable Should Be Entered into the Model
		6.8.3 Decide on the Statistical Method for Modelling (*)
		6.8.4 Assess the Fit of the Model (*)
			6.8.4.1 Adjusted R2
			6.8.4.2 Area under the Receiver Operating Characteristic Curve: c Statistic
			6.8.4.3 The Hosmer–Lemeshow Statistic for Calibration
		6.8.5 Which Is More Important, Discrimination or Calibration?
		6.8.6 What Can Be Done If the Model Fit or Performance Is Unacceptable?
		6.8.7 Convert Regression Coefficients into a Risk Score If Desired
7: Output the Observed and Model-Predicted Outcomes (*)
	Aims of This Chapter
	7.1 Ratios versus Differences
	7.2 Deriving SMRs from Standardisation and Logistic Regression
	7.3 Other Fixed Effects Approaches to Generate an SMR
	7.4 Random Effects–Based SMRs (*)
	7.5 Marginal versus Multilevel Models (*)
	7.6 Which Is the “Best” Modelling Approach Overall? (*)
	7.7 Further Reading on Producing Risk-Adjusted Outcomes by Unit
8: Composite Measures
	Aims of This Chapter
	8.1 Some Examples
	8.2 Steps in the Construction
		8.2.1 Specify the Scope and Purpose
		8.2.2 Choose the Unit
		8.2.3 Select the Data and Deal with Missing Values
		8.2.4 Choose the Indicators and Run Descriptive Analyses
		8.2.5 Normalise the Metrics
		8.2.6 Assign Weights and Aggregate the Component Indicators
		8.2.7 Run Sensitivity Analyses
		8.2.8 Present the Results
	8.3 Some Real Examples
		8.3.1 AHRQ’s Patient Safety Indicator Composite
		8.3.2 Leapfrog Group Patient Safety Composite
	8.4 Pros and Cons of Composites
	8.5 Alternatives to the Use of Composites
9: Setting Performance Thresholds and Defining Outliers
	Aims of This Chapter
	9.1 Defining Acceptable Performance
		9.1.1 Targets
		9.1.2 Historical Benchmarks
		9.1.3 Referring to Inter-Unit Variation
	9.2 Bayesian Methods for Comparing Providers
	9.3 Statistical Process Control and Funnel Plots
	9.4 Multiple Testing (*)
		9.4.1 Multivariate Statistical Process Control Methods (*)
		9.4.2 Further Reading on SPC
	9.5 Ways of Assessing Variation between Units
	9.6 How Much Variation Is “Acceptable”?
	9.7 Impact on Outlier Status of Using Fixed versus Random Effects to Derive SMRs
	9.8 How Reliably Can We Detect Poor Performance?
	9.9 Some Resources for Quality Improvement Methods
10: Making Comparisons across National Borders
	Aims of This Chapter
	10.1 Examples of Multinational Patient-Level Databases
	10.2 Challenges
		10.2.1 Worked Example of Combining Administrative Databases from Multiple Countries: Stroke Mortality
		10.2.2 Clustering within Countries
		10.2.3 Countries with Unusual Data or Apparent Performance
	10.3 Interpreting Apparent Differences in Performance between Countries
	10.4 Conclusion
11: Presenting the Results to Stakeholders
	Aims of This Chapter
	11.1 The Main Ways of Presenting Comparative Performance Data
	11.2 Effect on Behaviour of the Choice of Format When Providing Performance Data
	11.3 The Importance of the Method of Presentation
		11.3.1 Presenting Performance Data to Managers and Clinicians
		11.3.2 Presenting Results to the Public
	11.4 Examples of Giving Performance Information to Units
	11.5 Examples of Giving Performance Information to the Public
	11.6 Metadata
12: Evaluating the Monitoring System
	Aims of This Chapter
	12.1 Study Design and Statistical Approaches to Evaluating a Monitoring System
		12.1.1 Interrupted Time-Series Design and Analysis (*)
		12.1.2 Adjusting for Confounding (*)
		12.1.3 Difference-in-Differences
		12.1.4 Instrumental Variable Analysis
		12.1.5 Regression Discontinuity Designs
	12.2 Economic Evaluation Methods
13: Concluding Thoughts
	13.1 Simple versus Complex
	13.2 Specific versus General
	13.3 The Future
Appendix A: Glossary of Main Statistical Terms Used
References
Index