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The ‘Centre Effect’ and Statistical Process Control

The ‘Centre Effect’ and Statistical Process Control. Alex Hodsman. Liv RI – Rank 31. Chester – Rank 35. What are the aims for comparing centre outcomes?. Identify ‘meaningful’ differences between centres Identify improvement/deterioration Multiple simultaneous comparisons

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The ‘Centre Effect’ and Statistical Process Control

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  1. The ‘Centre Effect’ and Statistical Process Control Alex Hodsman

  2. Liv RI – Rank 31 Chester – Rank 35

  3. What are the aims for comparing centre outcomes? • Identify ‘meaningful’ differences between centres • Identify improvement/deterioration • Multiple simultaneous comparisons • Make fair comparisons • (Identify modifiable clinical processes)

  4. Why use SPC? • Inter centre variability in outcome measures • Chance • Data quality • Definitions • Case mix • Quality of care • Organisational structure • Processes of care • Intra centre variability in outcome measures

  5. SPC • Method of monitoring, controlling improving a process through statistical analysis • Key principles • Variability in all systems • Differentiate ‘special cause’ from ‘normal random’ variation • Identify and improve processes to reduce special cause variation

  6. Examples of SPC • Cross sectional • Funnel plots • Longitudinal • Control charts • CUSUM, EWMA, SPRT etc.. • Hybrid • Funnel plots

  7. Principles of SPC

  8. Cross sectional plots • Specificity • False positive rate/Type 1 error • 3SD = 0.27% • 2SD = 5%

  9. Longitudinal plots • Type 1 error • 25 data points • 3SD = 6.5% • 2SD = 27.7%

  10. Longitudinal plots - Interpretation • Shewhart’s original rule • > 3SDs from the process average • Numerous additional rules • Patterns/Trends in the data • E.g. 7 points in the same direction • Enhance sensitivity • Probability calculations

  11. SPC and the UKRR 2004 Report Funnel plot of age adjusted 1 year after 90 days survival, 2002-2005 cohort 2006 Report Funnel plot of % with serum phosphate<1.8mmol/L:HD

  12. Phosphate distributions

  13. Cross sectional Inter centre variability Good for looking at stable unit characteristics Data, Case mix, Organisational structure Longitudinal Intra centre variability Good for looking at less stable unit characteristics Data, Processes of care Cross sectional vs. Longitudinal

  14. Data collection • Define specification of audit measure Funnel plot to compare all centres • Individual control chart for each centre • Updated quarterly • P chart - % achieving audit measure • XMR chart for mean • XMR chart for SD • ? Also include a measure of process capability Identify and analyse outliers Check data against local audit data Data correct Data incorrect • Investigate causes • Case mix • Quality (organisational structure) • Investigate causes • Quality (processes of care) Refer to control chart to identify time of UKRR fault

  15. Conclusions • Methodical diagnostic approach to performance • Takes chance out of the equation • Focus resources • Statistics are complex but the output is user friendly • Limited ability to compare centres longitudinally i.e. rate of change

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