variation, run charts and control charts powerpoint

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1 Colorado 5M WebEx Colorado 5M WebEx Variation, Run Variation, Run Charts, and Control Charts, and Control Charts Charts Beth A. Katzenberg, EdM, MBA, CPHQ Beth A. Katzenberg, EdM, MBA, CPHQ Director, Corporate Quality & Director, Corporate Quality & Compliance Compliance Colorado Foundation for Medical Colorado Foundation for Medical Care Care

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Page 1: Variation, Run Charts and Control Charts PowerPoint

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Colorado 5M WebExColorado 5M WebExVariation, Run Charts, Variation, Run Charts, and Control Chartsand Control Charts

Beth A. Katzenberg, EdM, MBA, CPHQBeth A. Katzenberg, EdM, MBA, CPHQDirector, Corporate Quality & ComplianceDirector, Corporate Quality & ComplianceColorado Foundation for Medical CareColorado Foundation for Medical Care

Page 2: Variation, Run Charts and Control Charts PowerPoint

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Types of variationTypes of variation

Common causeCommon cause Always presentAlways present Inherent in processInherent in process Can predict Can predict

performance with a performance with a range of variationrange of variation

Cannot tell what Cannot tell what specifically causes specifically causes variationvariation

Special causeSpecial cause Abnormal, Abnormal,

unexpectedunexpected Due to causes not Due to causes not

inherent in processinherent in process Can be identified Can be identified

(e.g., change in (e.g., change in shift, weather, shift, weather, process)process)

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You must understand the You must understand the type of variation that is type of variation that is

occurring as this will occurring as this will determine how you determine how you

address the problem.address the problem.

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VariationVariationType of Type of

variationvariation Appropriate action to takeAppropriate action to take

Common Common causecause(predictable, (predictable, stable, in control, stable, in control, inherent in inherent in process)process)

Change the processChange the process Do not react to individualDo not react to individual differences or try to explain differences or try to explain differences between high differences between high and and low numbers low numbers

Special causeSpecial cause(unpredictable, (unpredictable, unstable, out of unstable, out of control)control)

Identify and study special Identify and study special causecause If negative, minimize or If negative, minimize or preventprevent If positive, build into processIf positive, build into process

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PitfallsPitfalls

If only If only common cause variationcommon cause variation and and treat as special cause (tampering), treat as special cause (tampering), leads to greater variation, mistakes, leads to greater variation, mistakes, defectsdefects

If If common cause and special causecommon cause and special cause, , and change the process, leads to and change the process, leads to wasted resources because the wasted resources because the change won’t workchange won’t work

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Tools to identify Tools to identify variationvariation

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Run chartsRun charts

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Run chartRun chartRun Chart

1.07 - 12.07

0

10

20

30

40

50

1.07 2.07 3.07 4.07 5.07 6.07 7.07 8.07 9.07 10.07 11.07 12.07

Time Frame(Month.Year)

Nu

mb

er

Median

Graph of data over timeGraph of data over time

Track performanceTrack performance

Display & identify variationDisplay & identify variation

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Run chart analysis: Run chart analysis: Common cause variation onlyCommon cause variation only

0

1

2

3

4

5

6

7

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Time

Common cause variation around the Common cause variation around the median:median: Only common cause variation present.Only common cause variation present. Output may or may not meet customer/ Output may or may not meet customer/ patient requirementspatient requirements

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Run chart analysis: RunsRun chart analysis: Runs

Run = one or more consecutive data Run = one or more consecutive data points on the same side of the medianpoints on the same side of the median

Excludes data points on the medianExcludes data points on the median

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

11 runs

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Expected number of runsExpected number of runs# data pts not

on median

Smallest run

count

Largest run

count

# data pts not

on median

Smallest run

count

Largest run

count

10 3 8 26 9 1811 3 9 27 9 1912 3 10 28 10 1913 4 10 29 10 2014 4 11 30 11 2015 4 12 31 11 2116 5 12 32 11 2217 5 13 33 11 2218 6 13 34 12 2319 6 14 35 13 2320 6 15 36 13 2421 7 15 37 13 2522 7 16 38 14 2523 8 16 39 14 2624 8 17 40 15 2625 9 17 41 16 26

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High probability High probability of special cause variation:of special cause variation:

Too few runsToo few runsToo many runsToo many runs

= 0.05)(

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Run chart analysis: Run Run chart analysis: Run lengthlength

0

2

4

6

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Time

Special cause—run length:Special cause—run length:

<20 data points<20 data points (not on median): A run of (not on median): A run of 77 data points on one side of the median data points on one side of the median (either above or below) (either above or below)

20+ data points20+ data points (not on median): A run of (not on median): A run of 88 data points on one side of the median data points on one side of the median

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Run chart analysis: TrendsRun chart analysis: Trends

0

2

4

6

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Time

Special cause—Special cause—trends:trends: Consecutive points all Consecutive points all going up or all going going up or all going down. May cross the down. May cross the median. Ignore 2+ median. Ignore 2+ consecutive points that consecutive points that are the same.are the same.

Total # data points on Total # data points on chartchart

# Consecutive points all # Consecutive points all increasing or decreasingincreasing or decreasing

5 to 85 to 8 44

9 to 209 to 20 55

21 to 10021 to 100 66

101 or more101 or more 77 (Pyzdek, 2003)

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Run chart analysis: FreaksRun chart analysis: Freaks

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Time

Freaks:Freaks: The presence of more than one The presence of more than one or two dramatic spikes suggests the or two dramatic spikes suggests the process is out of control.process is out of control.

Run charts not as sensitive in identifying, Run charts not as sensitive in identifying, thus may fail to detect.thus may fail to detect.

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Run chart analysis: CyclingRun chart analysis: Cycling

Cycling:Cycling: A zigzag or saw-tooth pattern A zigzag or saw-tooth pattern with 14+ points in a row alternating up with 14+ points in a row alternating up or down. or down.

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

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Run charts tipsRun charts tips

How many data points?How many data points?15-20 minimum is preferable15-20 minimum is preferable

Median = 50%/50% split pointMedian = 50%/50% split pointPrecisely half of the data set will be Precisely half of the data set will be

above the median and half below itabove the median and half below it

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Control chartsControl charts

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Control chartControl chart

Time

Qu

alit

y C

ha

rac

teri

sti

c

Low

High

UCL

An indication of a special cause

LCL

X

Run chart with control limitsRun chart with control limits

Determines type of variationDetermines type of variation

Is process stable? Predictable?Is process stable? Predictable?

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Dividing control chart into Dividing control chart into zoneszones

Zone AZone A

Zone BZone B

Zone CZone C

Zone CZone C

Zone BZone B

Zone AZone A

Each zone is 1

sigma wide

UCL

LCL

X

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Identifying special causesIdentifying special causes

Apply independently to each side of the Apply independently to each side of the center line:center line:1 point outside the 3 sigma limit1 point outside the 3 sigma limit2 out of 3 consecutive points in zone A or beyond2 out of 3 consecutive points in zone A or beyond4 out of 5 consecutive points in zone B or beyond4 out of 5 consecutive points in zone B or beyond<20 total data points:<20 total data points: 7 consecutive points in 7 consecutive points in

zone C or beyond on one side of center line zone C or beyond on one side of center line 20+ total data points:20+ total data points: 8 consecutive points in 8 consecutive points in

zone C or beyond on one side of center linezone C or beyond on one side of center line(continued)(continued)

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Identifying special causes, Identifying special causes, cont.cont.

Apply this test to entire chart:Apply this test to entire chart:<21 total data points:<21 total data points: 6 or more points 6 or more points

in a row steadily increasing or decreasingin a row steadily increasing or decreasing21+ total data points:21+ total data points: 7 or more points 7 or more points

in a row steadily increasing or decreasingin a row steadily increasing or decreasing14 consecutive points alternating up and 14 consecutive points alternating up and

down in saw-tooth patterndown in saw-tooth pattern15 consecutive points in zone C (above 15 consecutive points in zone C (above

and below center line)and below center line)

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Deciding which control chart to Deciding which control chart to useuse

Decide on type of data

Continuous (Variables, measurement) data(Values on continuous scale; e.g., time, temperatures, cost)

Attributes (count, discrete) data

(Values in discrete categories; e.g., % waste, # falls, # errors,

% incomplete charts)

More than one observation per

sub-group?NoYes

Fewer than 10 observations

per sub-group?

Are there equal areas of opportunity?

Are the subgroup

sizes equal?

Can both occurrences and non-occurrences

be counted?

NoYes

X -R chart X -S chart XmR chart

NoYes

No NoYes Yes

c-chartp-chartnp-chart u-chart

Average & range chart

Average & standard deviation

(sigma) chart

Individual & moving range

chart

Source: Carey, R. C. and Lloyd, R. C. Measuring Quality Improvement in Healthcare: A Guide to Statistical Process Control Applications, 1995

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Types of dataTypes of dataCount/attributeCount/attribute Measurement/Measurement/

continuouscontinuousCount observations or Count observations or incidents falling into incidents falling into categoriescategories

Whole numbers onlyWhole numbers onlyCannot be converted Cannot be converted to measurementto measurement

Take on values on a Take on values on a continuous scalecontinuous scaleWhole numbers and Whole numbers and decimals decimals Can be converted to Can be converted to countcount

Yes/noYes/no Dead/aliveDead/alive Infected/not infectedInfected/not infected On time/lateOn time/late

Time in minutes or Time in minutes or hourshours Weight in gramsWeight in grams Length of stayLength of stay Blood sugar levelsBlood sugar levels

% c-sections% c-sections % incomplete charts% incomplete charts # pt falls# pt falls # medication errors# medication errors

CostsCosts TemperatureTemperature

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Control chart example 1Control chart example 1

CBC Turn Around Time

40

50

60

70

80

90

100

110

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Day (Not Counting Weekends)

CB

C T

urn

Aro

un

d T

ime

(M

inu

tes)

UCL = 114.6

LCL = 51.9

X = 83.3

Common cause variation only

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Control chart example 2Control chart example 2

Luggage Reported Missing on Flights into Center CityMarch 7 through April 10

0

2

4

6

8

10

12

Day

# P

iece

s M

issi

ng UCL = 9.5

LCL = None

X = 3.2

new hiresnowstorm

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Control chart example 3Control chart example 3

Net Operating Margin for Hospital A 1/05-9/06

-4

-2

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Month

Per

cen

t

UCL = 12.1

X = 4.6

LCL = -2.9

(From: Carey, R. G. & Lloyd, R. C. Measuring Quality Improvement in Healthcare

Common cause variation only; can predict will stay within control limits, if no changes

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Control chart example 4Control chart example 4

Net Operating Margin for Hospital B1/92-9/93

-4

-2

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Month

Per

cen

t

UCL = 9.25

X = 4.60

LCL = -.04

(From: Carey, R. G. & Lloyd, R. C. Measuring Quality Improvement in Healthcare

Out of control, unpredictable

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Just because a process is under Just because a process is under control (common cause variation control (common cause variation only), it does not mean that the only), it does not mean that the

process is meeting expectations. process is meeting expectations.

It just means that the process is It just means that the process is predictable and you are getting predictable and you are getting

consistent performance.consistent performance.

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Control charts tipsControl charts tips

Control limits are not specifications limits Control limits are not specifications limits (specification limits related to customer (specification limits related to customer requirements)requirements)

After removing special causes and After removing special causes and recalculating chart, continue to plot new data recalculating chart, continue to plot new data on this chart, without recalculating control on this chart, without recalculating control limits.limits.Recalculate control limits only when a Recalculate control limits only when a

permanent, desired change has occurred in the permanent, desired change has occurred in the process and only using data process and only using data afterafter the change the change occurredoccurred

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Share the dataShare the data

Team meetingsTeam meetingsPost in break-roomsPost in break-roomsNewslettersNewslettersIntranetIntranet

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Examples of SoftwareExamples of Software

QI Macros QI Macros www.qimacros.comwww.qimacros.comStatSoft StatSoft www.statsoft.comwww.statsoft.comMinitab Minitab www.minitab.comwww.minitab.com

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ReferencesReferences

Carey, R.G. & Lloyd, R.C. Carey, R.G. & Lloyd, R.C. Measuring Quality Improvement in Healthcare: A Measuring Quality Improvement in Healthcare: A Guide to Statistical Process Control Applications, Guide to Statistical Process Control Applications, Quality Resources, 1995.Quality Resources, 1995.

Pyzdek, R. Pyzdek, R. The Six Sigma Handbook: A Complete Guide for Green Belts, Black The Six Sigma Handbook: A Complete Guide for Green Belts, Black Belts, and Managers at All Levels, Belts, and Managers at All Levels, 2003.2003.

The Six Sigma Memory Jogger II, The Six Sigma Memory Jogger II, GOAL/QPC, 2002.GOAL/QPC, 2002.

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Beth Katzenberg, EdM, MBA, CPHQBeth Katzenberg, EdM, MBA, CPHQDirector, Corporate quality & complianceDirector, Corporate quality & compliance

Colorado Foundation for Medical CareColorado Foundation for Medical [email protected]@cfmc.org