The centre effect and statistical process control
This presentation is the property of its rightful owner.
Sponsored Links
1 / 29

The ‘Centre Effect’ and Statistical Process Control PowerPoint PPT Presentation


  • 50 Views
  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

The ‘Centre Effect’ and Statistical Process Control

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


The centre effect and statistical process control

The ‘Centre Effect’ and Statistical Process Control

Alex Hodsman


The centre effect and statistical process control

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


The centre effect and statistical process control

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


Principles of spc

Principles of SPC


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


Phosphate distributions

Phosphate distributions


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


The centre effect and statistical process control

  • 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


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


  • Login