000			05359nam a22002417a 4500
005			20230719174903.0
008			230719b |||||||| |||| 00| 0 eng d
020			_a9781032242835
082			_a362.10727 _bBOT
100			_aBottle, Alex _912714
245			_aStatistical methods for healthcare performance monitoring
260			_bCRC Press _aBoca Raton _c2021
300			_axxi, 269 p.
365			_aGBP _b44.99
504			_aIntroduction 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 Acknowledgments Origins and examples of monitoring systems Origins 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 Conclusion 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 Surverys Other sources Other issues concering data sources Conclusion 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 Challenges Interpreting apparent differences in performance between countries Conclusion 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 Metadata 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
520			_aHealthcare 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.
650			_aMedical care--Evaluation _913159
650			_aMedical care--Quality control _913160
650			_aMedical care--Safety measures _913169
650			_aMedical statistics _913094
700			_aAylin, Paul _913170
942			_cBK _2ddc
999			_c5419 _d5419