Analyzing & Presenting Performance Improvement (PI) Data.
-
Upload
prudence-lewis -
Category
Documents
-
view
225 -
download
3
Transcript of Analyzing & Presenting Performance Improvement (PI) Data.
![Page 1: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/1.jpg)
Analyzing & Presenting Performance Improvement (PI) Data
![Page 2: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/2.jpg)
ObjectivesDemonstrate an exercise that uncovers how leaders make managerial decisions based upon data
Identify barriers to effective analysis and reporting of PI data
Share 2 data analysis/reporting educational tools targeted for staff
Provide sample PowerPoint slides for staff training re: data analysis and process variability
Discuss PI information needs of leadership
CSR ©2011
![Page 3: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/3.jpg)
1 2 3
![Page 4: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/4.jpg)
![Page 5: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/5.jpg)
CSR ©2011
![Page 6: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/6.jpg)
![Page 7: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/7.jpg)
![Page 8: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/8.jpg)
Why aggregate and analyze?Transform data into information
Identify current performance levels, patterns, or trends
Determine
Whether or not improvement is needed
If a strategy to stabilize or improve performance was effective
If design specifications met
Judge a particular process’s stability or a particular outcome’s predictability in relation to performance expectations
![Page 9: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/9.jpg)
Problem #1Lumping data together is usually not
enough! Aggregate #’s do not show any
“unusual” circumstances. If leaders take action based on data
assumptions without taking into account unusual circumstances – they can muck up a perfectly good process!
![Page 10: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/10.jpg)
Time to work each day
Minutes
Should I change the route to work each day?
![Page 11: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/11.jpg)
September’s Rates –Minutes to work
0
50
100
150
200
250
300
350
400
450
![Page 12: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/12.jpg)
Problem #2Before and after measures aren’t enough! Two aggregate measures taken before and
after a change do not in themselves demonstrate that a process has improved.
One needs to know the stability of the processes that produced these aggregate measures.
To determine process stability, it is necessary to look at data over time i.e., in a time series design.
![Page 13: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/13.jpg)
Staff Turnover
Intervention Begins 10/2009
![Page 14: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/14.jpg)
Staff Turnover – the same data using the # of staff over time!
Intervention Begins 10/2009
![Page 15: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/15.jpg)
Sheward & Deming Points
Variation exists in all we do Processes that exhibit common causes of variation
are predictable within statistical limits Processes often have both common and special
cause variation How we respond to special causes is different than
our response to common cause variation Attempting to improve processes that contain special
causes will increase variation and waste resources Once special causes have been “eliminated”, it is
appropriate to consider changing the process
![Page 16: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/16.jpg)
Common vs. Special Cause Variation
Common Cause Is inherent in the design of the process. Is due to regular, natural, or ordinary causes. Results in a stable process. The variation is predictable. Also known as random or unassignable causes.
Special Cause Is due to causes not inherent in a process. Results in an unstable process, because the variation is not
predictable. Also know as non-random or assignable causes. Might be described as a “signal” that the process has changed.
CSR ©2011
![Page 17: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/17.jpg)
Neither type of variation is “good” or “bad” in
itself!Common Cause Only tells you that a process is stable and predictable within
certain limits However, it may be functioning at an unacceptable level!
Special Cause Usually undesirable when you did not plan for it. Can also be a “signal” that a planned change was effective.
![Page 18: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/18.jpg)
When people do not understand variation
See trends where there are no trends Blame and give credit to others for things
over which they have little or no control Build barriers, decrease morale, and create
an atmosphere of fear Never be able to fully understand past
performance, make predictions about the future and make significant improvements in processes
![Page 19: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/19.jpg)
How Do We Analyze Variation?
Run charts and control charts are the tools used to determine whether variation is:Common cause, orSpecial cause
They tell us what the process is actually doing –Not what we would like it to do!
![Page 20: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/20.jpg)
Bar Graphs
A preliminary exploration of datamay be time-
ordered
Are a common graphical display format
Can be difficult for trend detection
20
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
0
200
400
600
800
1000
1200
1400
2009
2010
![Page 21: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/21.jpg)
Same bar graph data displayed in a simple Line Graph
Offers a preliminary view of time ordered data
Stock market trends are viewed in line graphs
Common mistake is to see trends where they statistically don’t exist
21
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN
400
500
600
700
800
900
1000
1100
1200
1300LINE GRAPH
![Page 22: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/22.jpg)
Run Chart
Used to detect common cause vs. special cause variation
Easy to construct and evaluate
Less sensitive than control charts for identifying extreme data points as a special cause
22
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN
400
500
600
700
800
900
1000
1100
1200
1300RUN CHART: Monthly Calls Received
1999 2000
MEDIAN
"COMMON CAUSE VARIATION"
![Page 23: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/23.jpg)
Run Chart = Line Graph + Center Line*
23
*The center line in a run chart is typically the median point for the data, but some use the process average or mean as the center line
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN
400
500
600
700
800
900
1000
1100
1200
1300
RUN CHART: Monthly Requests for Services
1999 2000
MEDIAN
"COMMON CAUSE VARIATION"
![Page 24: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/24.jpg)
Run Chart Terminology
RUNDefined as one or more consecutive data
points occurring on the same side of the center line
TRENDDefined as an unusually long series of data
points steadily increasing or decreasing
24
![Page 25: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/25.jpg)
EASY Run Chart Tests for Special Causes
TREND of 6 consecutive data points steadily increasing or decreasing
RUN of 8 consecutive data points on one side of the center line (median or mean)
OUTLIER POINTS – use your judgment whether to expend resources to investigate further to understand cause and determine if improvement is needed
25CSR ©2011
![Page 26: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/26.jpg)
1
10
Feb March April May June July Aug Sept Oct Nov Dec
2Median
Test #1 TREND of 6 consecutive data points steadily increasing or decreasing
Sequential Run of only 5 –
Common Cause Variation
![Page 27: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/27.jpg)
1
10
Feb March April May June July Aug Sept Oct Nov Dec
2Median
Test #2 RUN of 8 consecutive data points on one side of the center
line (median or mean)
Run of 9 –
Special Cause Variation
![Page 28: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/28.jpg)
median
Test #3OUTLIER POINTS – use your
judgment whether to expend resources to investigate further
to understand cause and determine if improvement is
needed
Is May’s result a special cause????
![Page 29: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/29.jpg)
Improvement Strategies: After making a run or control chart, what’s next?
29
The type of variation determines your approach:
Special cause variation?If negative, eliminate it.If positive, emulate it.But don’t change the process!
Common cause variation?If process is functioning at an unacceptable level, change the process!Don’t “tamper” with individual data points!
![Page 30: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/30.jpg)
How will you know your intervention is a success?
30
A Special cause in the desired direction will signal that the old process is changed for the better.
A Special cause in the wrong direction will indicate that your intervention was counterproductive.
Continued common cause variation will indicate that your intervention did not help – but did not hurt either.CSR ©2011
![Page 31: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/31.jpg)
Improvement Strategy
31
Time 1 Time 2 Time 3 Time 4
Conduct Initial
Investigation
Standardize
The
Process
Introduce Improvement - 1
Introduce Improvement - 2
![Page 32: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/32.jpg)
Targeting Your MessageHospital boards should hold accountable and require full and complete explanations from management when safety and quality performance levels differ significantly from national benchmarks or fall below expectations, with specific attention devoted to the organization’s plan for improvement (e.g., its development, performance expectations, and the basis on which expectations are established).
Hospital Governing Boards and Quality of Care: A Call to Responsibility. Washington, DC: National Quality Forum; 2004.
![Page 33: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/33.jpg)
Leadership should….• Create alignment between organizational strategy, measures, and improvement projects• Unify Quality Improvement Plans, Strategic Plans, and Financial Plans within the organization• Ensure that the daily work of employees is organized to support deployment of strategies and improvement projects chosen because of their direct impact on system-level measures or direct support of strategic objectives. Leaders should then implement, monitor, and revise the strategy as needed if the desired changes are not occurring.
Botwinick L, Bisognano M, Haraden C. Leadership Guide to Patient Safety. IHI Innovation Series white paper. Cambridge, Massachusetts: Institute for Healthcare Improvement; 2006. (Available on www.IHI.org)
![Page 34: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/34.jpg)
![Page 35: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/35.jpg)
![Page 36: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/36.jpg)
![Page 37: Analyzing & Presenting Performance Improvement (PI) Data.](https://reader035.fdocuments.in/reader035/viewer/2022081516/56649f515503460f94c754b0/html5/thumbnails/37.jpg)
Use of Lean/6 SigmaPotential Topics to Report:• Voice of the customer, suppliers and process workers• Key critical customer requirements• Outputs which are “Critical to Quality” (CTQ)• Rating of relationship between the process steps [inputs] to the customer requirements• Current Process Controls Prevention• Current Process Controls Detection• FMEAs – findings about severity, occurrence and detection• Description of standard work – current and future states (i.e., value stream map)