Loading in 5 sec....

Analysis and presentation of quality indicators PowerPoint Presentation

Analysis and presentation of quality indicators

- 96 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' Analysis and presentation of quality indicators ' - jedidiah-braylon

**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

Analysis and presentation of QIs

- Principles of statistical process control
- Comparison among providers
- Continuous monitoring over time

Analysis and presentation of QIs

- Principles of statistical process control
- Common cause variation
- Special cause variation
- Control limits

- Comparison among providers
- Continuous monitoring over time

Principles of statistical process control

- Common cause variation
- Variation cannot be eliminated
- Some variation is inherent to any process
- This is termed “common cause variation”
- To reduce common cause variation we need to change the process

They are not identical…

…but they are all my signature

We could reject some as low quality…

…but they are still my signature!

This iscommon cause variation

Principles of statistical process control

- Special cause variation
- Some variation is the result of external factors acting on a process
- This is termed “special cause variation”
- To reduce special cause variation we need to identify the source and eliminate it

Now we have a sixth signature…

…it’s a good try, but I think you can tell which one is the forgery!

This isspecial cause variation

Control limits

- Statistical process control is all about making allowance for common cause variation to detect special cause variation
- To do this we place control limits around a process
- Control limits represent the acceptable range of common cause variation

Control limits

- Typically control limits of 2 and 3 SDs represent “alert” and “alarm”
- If a system is in control:
- 95.4% of values within 2 SDs
- 99.7% of values within 3 SDs

Analysis and presentation of QIs

- Principles of statistical process control
- Comparison among providers
- League tables
- Caterpillar plots
- Funnel plots
- Over-dispersion

- Continuous monitoring over time

Comparison among providers

- I’ll assume we have a binary event (e.g. death) and an associated risk estimate (e.g. predicted risk of death)
- Most common QI is:observed events / expected events
- (for mortality this is the standardised mortality ratio)
- How should we compare this QI among providers (e.g. critical care units)?

League tables

- Journalists love them
- High impact
- Everyone wants to know who is firstand last

Seven deadliest hospitals identified in damning Dr Foster reportDaily Telegraph, 29 November 2009

Twelve NHS trusts slammedThe Sun, 29 November 2009

Patient safety at ScarboroughHospital ‘second worst in country’Scarborough Evening News, 29 November 2009

League tables

- Journalists love them
- High impact
- Everyone wants to know who is firstand last

- Statisticians hate them
- Overemphasise unimportant differences
- Even if there is no true difference, someone will be first and someone last
- No account of role of chance (common cause variation)

Marshall & Spiegelhalter, BMJ 1998

- League table of 52 IVF clinics ranked on live birth rate
- Monte Carlo simulation to put 95% CI on ranks

Marshall & Spiegelhalter, BMJ 1998

Marshall & Spiegelhalter, BMJ 1998

- King’s College Hospital – sixth from bottom – is the only one that can reliably be placed in the bottom 25%

Marshall & Spiegelhalter, BMJ 1998

- BMI Chiltern Hospital – seventh from bottom – may not even be in the bottom 50%

Marshall & Spiegelhalter, BMJ 1998

*

*

*

*

*

- Five clinics can confidently be placed in the top quarter

Marshall & Spiegelhalter, BMJ 1998

- Southmead General – ranked sixth from top – may not be in the top 50%

Caterpillar plots (or forest plots)

- Plot of QIs with CIs in rank order
- Still a league table really
- But at least acknowledges variation by including CIs

Caterpillar plot – ANZICS

- SMRs by APACHE III-J for 106 adult ICUs in Australia and New Zealand, 2004(Cook et al. Crit Care Resusc 2008)

Funnel plots

- Larger sample = greater precision
- If you plot QI against sample size, you expect to see a funnel shape
- We can plot funnel shaped control limits

Funnel plot – ANZICS

- SMRs by APACHE III-J for 106 adult ICUs in Australia and New Zealand, 2004(Cook et al. Crit Care Resusc 2008)

Funnel plot – ANZICS

- Note: use of normal distribution can result in negative confidence intervals – better methods exist

Funnel plot – ANZICS

- Note: as SMR is a ratio measure, we would advocate plotting on a log scale (i.e. SMR=2 and SMR=0.5 are equidistant from SMR=1)

Funnel plot – SICSAG

- SMRs by APACHE II for 25 adult ICUs in Scotland, 2009(SICSAG Audit of critical care in Scotland 2010)

Funnel plot – SICSAG

- Note: as the model is poorly calibrated, most units are “better than average” – the funnel has been centred on the average SMR not 1

Over-dispersion

- Variability more than expected by chance
- Suggests important factors that vary among providers are not being taken into account
- Too many providers classified as “abnormal” (i.e. outside the funnel)

Over-dispersion – hospital readmissions

(Spiegelhalter. Qual Saf Health Care 2005)

Over-dispersion – what to do…?

- Don’t use the indicator?
- Improve risk adjustment
- Adjust for it
- Estimate “over-dispersion factor” by “Winsorisation”

- Use random effects models
- Assumes each provider has their own true rate from a distribution

Example – over-dispersion factor

- SMRs by ICNARC model for 171 adult ICUs in England, Wales & N Ireland, 2009

Example – over-dispersion factors

- Over-dispersion factor estimated at 1.4
- Funnel widened

Analysis and presentation of QIs

- Principles of statistical process control
- Comparison among providers
- Continuous monitoring over time
- RAP chart
- EWMA
- VLAD
- R-SPRT
- CUSUM

Continuous monitoring over time

- Various approaches
- In general, they consist of…
- an indicator that is updated for each consecutive patient
- control limits

Example for continuous monitoring

- Queen Kate Hospital
- Fictitious critical care unit
- Random sample of 2000 records from the Case Mix Programme Database
- After 1000 records, outcomes changed so that an extra 6% of patients (selected at random) die
- Risk adjustment by the ICNARC (2009) model

RAP chart

- Risk-adjusted p chart
- Cohort divided into discrete blocks (e.g. 100 patients)
- Indicator is observed mortality
- Control limits are predicted mortality +/- 2 or 3 SDs
- Pro
- Displays observed and expected mortality

- Con
- Still in blocks, not sensitive

EWMA

- Exponentially weighted moving average
- Similar to RAP but uses all data up to the current timepoint
- Data weighted by a smoothing factor so that most recent data are given most weight

EWMA

- Pro
- Displays observed and expected mortality
- Estimates updated continuously not in arbitrary blocks

- Con
- Choice of smoothing factor is important – too little smoothing and plot is unreadable, too much and plot is insensitive to changes

VLAD

- Variable life adjusted display
- Cumulative observed minus expected deaths
- Pro
- Nice easy interpretation

- Con
- Control limits are complex to calculate curved functions

R-SPRT

- Resetting sequential probability ratio test
- Tests evidence for/against a specific hypothesis (e.g. odds of death are double that predicted by the model)
- Plot of log likelihood ratio
- If bottom line is reached (strong evidence against hypothesis) then line resets to zero

R-SPRT

- Pro
- Nice statistical properties
- Control limits are horizontal lines

- Con
- Choice of hypothesis to test is arbitrary – should we test for an OR of 2, 1.5,…?

CUSUM

- “Cumulative sum”
- Log likelihood ratio – same as R-SPRT
- “Absorbing barrier” at zero (i.e. never goes below zero)

CUSUM

- Pros/Cons as for the R-SPRT plus…
- Pro
- Does not allow credit to build up (as in R-SPRT) so alerts earlier
- Negative CUSUM (e.g. OR=0.5) can be plotted on the same axes

- Con
- Cannot detect evidence against hypothesis

Which method(s) to use…?

- Comparison among providers
- Funnel plot

- Continuous monitoring over time
- EWMA
- or R-SPRT
- or CUSUM
- (VLAD can be used as a display in conjunction with, e.g., CUSUM for monitoring)

Download Presentation

Connecting to Server..