1st Edition

Statistical Methods for Healthcare Performance Monitoring

By Alex Bottle, Paul Aylin Copyright 2017
    292 Pages 12 B/W Illustrations
    by CRC Press

    291 Pages 12 B/W Illustrations
    by CRC Press

    Healthcare is important to everyone, yet large variations in its quality have been well documented both between and within many countries. With demand and expenditure rising, it’s more crucial than ever to know how well the healthcare system and all its components – from staff member to regional network – are performing. This requires data, which inevitably differ in form and quality. It also requires statistical methods, the output of which needs to be presented so that it can be understood by whoever needs it to make decisions.

    Statistical Methods for Healthcare Performance Monitoring covers measuring quality, types of data, risk adjustment, defining good and bad performance, statistical monitoring, presenting the results to different audiences and evaluating the monitoring system itself. Using examples from around the world, it brings all the issues and perspectives together in a largely non-technical way for clinicians, managers and methodologists.

    Statistical Methods for Healthcare Performance Monitoring is aimed at statisticians and researchers who need to know how to measure and compare performance, health service regulators, health service managers with responsibilities for monitoring performance, and quality improvement scientists, including those involved in clinical audits.

    The need for performance monitoring
    Measuring and monitoring quality
    The need for this book
    Who is this book for and how should it be used?
    Common abbreviations used in the book

    Origins and examples of monitoring systems
    Healthcare scandals
    Examples of monitoring schemes
    Goals of monitoring

    Choosing the unit of analysis and reporting
    Issues principally concerning the analysis
    Issues more relevant to reporting: attributing performance to a given unit in a system

    What to measure: choosing and defining indicators
    How can we define quality?
    Common indicator taxonomies
    The particular challenges of measuring patient safety
    The particular challenges of multimorbidity
    Measuring the health of the population and quality of the whole healthcare system
    Efficiency and value
    Features of an ideal indicator
    Steps in construction and common issues in definition
    Validation of indicators
    Some strategies for choosing among candidates
    Time to go: when to withdraw indicators

    Sources of data
    How to assess data quality
    Administrative data
    Clinical registry data
    The accuracy of administrative and clinical databases compared
    Indicent reports and other ways to capture safety events
    Other sources
    Other issues concering data sources

    Risk-adjustment principles and methods
    Risk adjustment and risk prediction
    When and why should we adjust for risk?
    Alteratives to risk adjustment
    What factors should be adjust for?
    Selecting an initial set of candidate variables
    Dealing with missing and extreme values
    Timing of the risk factor measurement
    Building the model

    Output the observed and model-predicted outcomes
    Ratios versus differences
    Deriving SMRs from standardisation and logistic regression
    Other fixed effects approaches to generate an SMR
    Random effects based SMRs
    Marginal versus multilevel models
    Which is the "best" modelling approach overall?
    Further reading on producing risk-adjusted outcomes by unit

    Composite measures
    Some examples
    Steps in the construction
    Some real examples
    Pros and cons of composites

    Setting performance thresholds and defining outliers
    Defining acceptable performance
    Bayesian methods for comparing providers
    Statistical process control and funnel plots
    Multiple testing
    Ways of assessing variation between units
    How much variation is "acceptable"?
    The impact on outlier status of using fixed versus random effects to derive SMRs
    How reliably can we detect poor performance?
    Some resources for quality improvement methods

    Making comparisons across national borders
    Examples of multinational patient-level databases
    Interpreting apparent differences in performance between countries

    Presenting the results to stakeholders
    Main ways of presenting comparative performance data
    Effect on behaviour of the choice of format when providing performance data
    Importance of the method of presentation
    Examples of giving performance information to units
    Examples of giving performance information to the public

    Evaluating the monitoring system
    Study design and statistical approaches to evalutating a monitoring system
    Economic evaluation methods

    Concluding thoughts
    Simple versus complex
    Specific versus general
    The future


    Appendix: glossary of main statistical terms used


    Dr. Alex Bottle is a Senior Lecturer at Imperial College London, UK

    Dr. Paul Aylin is a Professor at Imperial College London, UK

    "… Overall, the book provides an interesting and easily accessible overview on health care performance monitoring and the statistical methods associated with it. Each chapter has a short overview at the beginning and sometimes a conclusion at the end, so it can also serve as a reference book. According to the authors, this book is not primarily aimed at statisticians, but all who want to compare and measure health care performance. The level of statistics obtained from an undergraduate nursing or medical degree is enough to follow. Furthermore, the authors marked several chapters and subchapters more heavy on statistics that can be skipped without missing the big picture. The rich examples make the book enjoyable to read and it has my unconditional recommendation to all interested in the topic."
    —Christoph F. Kurz, Helmholtz Zentrum Munich, in Biometrics, March 2018

    "Bottle and Aylin offer readers a practical approach to performance measurement, and the statistical tools to get it right. This topic may seem dry and arcane until you realize that these methods are what patients and policymakers depend on to tell a good hospital from a dangerous one, and a superb physician from a quack. In fact, the authors learned their trade investigating some of the most famous cases of medical scandals in the world, including a hospital whose errors killed dozens of babies, and a murderous physician who killed scores of patients. So, the lessons in this book matter. Highly recommended."
    Robert M. Wachter, MD, Professor and Chair, Department of Medicine, University of California, San Francisco

    "Improving healthcare and ensuring patient safety relies on timely, valid and understandable information. Healthcare systems are awash with data but this seldom translates into useful information. The great value of this book lies in the combination of statistical sophistication with an understanding of the healthcare context and a practical concern for improving the care of patients. Alex Bottle and Paul Aylin have done us a great service by sharing their extensive expertise and showing us how healthcare can be effectively monitored and improved."
    —Charles Vincent, Professor of Psychology, University of Oxford, and Emeritus Professor Clinical Safety Research, Imperial College London

    "This book is the most thorough, comprehensive and practical review of hospital performance monitoring available to my knowledge. ¿ Although a statistical manual, it is not overly technical with very few formulae, and covers the ground in a logical way. It is replete with examples and I particularly like the tabulations of pros and cons of different methods and approaches and summaries of current controversies.
    It discusses real-world issues which affect policy makers, practitioners and researchers alike and will be of value to all. I wish I'd had this book when I was working in this area. I can strongly recommend to anyone wishing to embark on the complexities of performance monitoring, and everyone who is already engaged in this area."
    Julian Flowers, Head of Public Health Data Science, Public Health England