The centre effect and statistical process control
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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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Liv RI – Rank 31

Chester – Rank 35


What are the aims for comparing centre outcomes
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)


Why use spc
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


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


Examples of spc
Examples of SPC

  • Cross sectional

    • Funnel plots

  • Longitudinal

    • Control charts

    • CUSUM, EWMA, SPRT etc..

  • Hybrid

    • Funnel plots



Cross sectional plots
Cross sectional plots

  • Specificity

  • False positive rate/Type 1 error

  • 3SD = 0.27%

  • 2SD = 5%


Longitudinal plots
Longitudinal plots

  • Type 1 error

  • 25 data points

  • 3SD = 6.5%

  • 2SD = 27.7%


Longitudinal plots interpretation
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


Spc and the ukrr
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



Cross sectional vs longitudinal

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


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


Conclusions
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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