1. Performance indicators: good, bad, and ugly The report of the Royal Statistical Society working party on Performance Monitoring in the Public Services http://www.rss.org.uk/PDF/PerformanceMonitoring.pdf

2. “Performance monitoring done well is broadly productive for those concerned. Done badly, it can be very costly and not merely ineffective but harmful and indeed destructive.” Value of Performance Monitoring  Public sector PM plays three roles:  Research  Management  Democratic

3. Methodology … … adopt a rigorous approach  Data sources:  Sample surveys should be designed, conducted and analysed in accordance with statistical theory and best practice  Administrative data should be fully auditable  Concepts, questions, etc:  should be comparable and harmonised where possible

4. Methodology … … adopt a rigorous approach  Indicators and targets:  Precise: accurate enough to show reliably when change has occurred  Clear: defining all key concepts used  Unambiguous  Consistent over time  Clear: documenting fully any changes to definitions or methods

5. Targets … … seek practitioner input  Motivational but irrational targets may demoralise  Ambitious but achievable targets require:  A good understanding of the practicalities of delivery on the ground … … based on consultation with practitioners  A good understanding of the data

6. Targets … … avoid extreme value targets  0% or 100% targets can lead to perverse outcomes, demoralisation, and lead to disproportionate resources being used  An example from the report:  “No patient shall wait in A&E for more than 4 hours”  This becomes irrelevant as soon as one patient does wait more than 4 hours  A&E staff may have very sound reasons for making a small number of people wait longer

7. Targets … … monitor for perverse outcomes  Targets can lead to practitioners playing the system rather than improving performance to meet poorly conceived targets  An example from the report:  An indicator for prisons is the number of “serious” assaults on prisoners  “Serious” = proven prisoner-on-prisoner assault  The indicator would improve if prisons reduced their investigations into alleged assaults

8. Do not ignore … … uncertainty or variability  Single numbers provide simple answers to complex questions  Natural variability, outliers, recording errors, statistical error (i.e. confidence intervals around sample estimates) all need to be considered  Uncertainty and variability need to be clearly presented An example taken from the from the work of David Spiegelhalter …

9. Cannot be 95% confident about any hospital being in top quarter or bottom quarter POST/RSS meeting on Performance Monitoring in the Public Services (March 2004) … ranks for 51 hospitals with 95% intervals: mortality after fractured hip

10. Do not ignore … … the distribution  Performance Indicators are one number  Single number summaries of data can be misleading  An example from the report:  “Number of patients waiting more than 4 hours”  The whole distribution needs viewing to understand the indicator  Example: has progress been achieved by getting most people seen in 3 hours 59 minutes but some not for 10 hours?

11. Do not confuse statistical significance for practical importance  Statistical significance  depends on sample size: very small differences can be statistically significant for very large samples  measures a property of the statistics not the practical importance of any relationship observed  A difference can be statistically significant but not of practical importance

12. Consider not setting a target until data are well understood  The statistical properties of an indicator will be much better understood after one or two rounds of analysis  It may therefore be sensible to wait before setting a target

13. Document everything  Others should be able to replicate procedures  Establish a ‘PM Protocol’, including:  Objectives  Definitions  Information about context  Survey methods / information about data  Risks of perverse outcomes  How the data will be analysed  Components of variation  Ethical, legal and confidentiality issues  How, when and where data will be published