Reducing Surgical Site Infections (SSIs) Learning Session 1

Reducing Surgical Site Infections(SSIs)

Learning Session 1Western Cape, July 2015

To reduce Surgical Site Infections (SSIs)using a ‘care bundle’ and a

Quality Improvement (QI) approach

Aim of the SSI collaborative

WILL

IDEAS EXECUTION

Improving the system

Where are SSIs occurring in your hospital and in which patients?

Identify the department(s) with the highest SSI rates and/or SSIs with the mostserious consequence

SSIs - how big is the problem?

Map the departments, wards and theatres manage these patients

Map the units that deal with these patients

• In which department/s did you start the project?

• Select one department in which to start your project

• What are your reasons? high priority best chance of success

Scoping the size of the challenge at your hospital

Evidence based intervention

• IHI (US) 100,000 lives campaign • Canadian Safer Healthcare Now• Scotland NHS Patient Safety Alliance ….and other successes around the world

Interventions were made into bundles

• What is a bundle and how does it work?A grouping of best practices that individually improve care,

but when applied together result in substantially greater improvement.

The science behind the bundle is so well established that it should be considered standard of care.

Bundle elements are dichotomous and compliance can be measured: yes/no answers.

Bundles shun the piecemeal application of proven therapies in favor of an “all elements” approach.

Fry D. Surgical Infections. Volume 12, Number 6, 2011

50 Ways To Cause Surgical Site Infections

The 5 elements of the SSI bundle

• Antiseptic skin preparation• Antibiotic prophylaxis• Hair removal• Glucose control• Normothermia

What can be achieved when the bundles are implemented reliably?

Reliable care:every element of the bundle

to every patientevery time!

2.10

1.52

2.13

1.93

1.30

1.94

1.30

1.09

1.80

1.59

1.38

1.16 1.21

1.57

0.95

1.37

0.93

1.38

59%

74% 75%78%

74% 73%

78% 77%79% 78%

81%84% 85%

83% 83% 83%85%

87%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-

0.50

1.00

1.50

2.00

2.50

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Overall Surgical Site Infections - Bundle Compliance and SSI RateMar 09 - Aug 10

Series2

Series1

HAI : SSI RATE

12

Surgical Site InfectionsBenchmarking: What is possible?

National Surgical Infection Prevention Project 56 participating hospitals Quality Improvement Approach

Measures

• The 4 BCA bundle elements plus an additional element

Results

After 11 months: • Overall surgical infection rate

fell 27% (p=0.0005)• from 2.28% the first 3 months

(215 infections/ 9435 cases)• to 1.65% in the last 3

reporting months (158 infections/ 9584 cases)

Baseline : 26% incidence of SSI in patients undergoing elective colorectal resection

Implemented 3 of the 4 BCA bundle elements

Implemented a multidisciplinary wound management protocol using SIP as guideline

Outcome: SSI incidence fell from 25.6% to 15.9% (p≤0.05)

39% improvement

Baseline : 10.9% incidence of SSI in children undergoing heart surgery

Implemented a comprehensive Infection Control Program

Included 2 of the 4 BCA elements plusAntiseptic protocol ( Chlorhexidine bath and skin prep)

Environmental elements

Outcome: 1.92% incidence in post-intervention group (82% reduction)

In summary

• 3 studies• Each implementing a combination of

bundle elements• 27-82% reduction in % of SSI’s.

• Set your aim according to the needs of the patients best practice

• At this point you don’t need to know

how how to get there

Setting a s-t-r-e-t-c-h Aim

Setting a s-t-r-e-t-c-h Aim

In …………hospital

we aim to reduce SSIs

By/to……(how much) by ……..(when)

in the ……….. department

WILL

IDEAS EXECUTION

The Aim

• Tells us where we are heading

• Helps build WILL• Keeps the project focused

The SSI bundle

• Chlorhexidine shower/bath preoperatively

• IV antibiotic prophylaxis before skin incision (<1 hour) • Correct drug, dose, redosing• <24 hours

• Hair removal• Preferably, none• Clippers not razors• Depilation?

• Avoiding hyperglycemia in cardiac surgery

• Normothermia in colorectal surgery

Reducing SSIs Experience from the field

Hospital presentations – Mowbray Maternity Hospital- Paarl Hospital- Tygerberg Hospital

Going beyond the bundle

• Additional elements that have supplemented the bundle elements in SA public health projects

FAQ’s – panel discussion

PanelProf Marc MendelsonProf Ivan JoubertSr Linda van der Westhuizen (Mediclinic)

Chaired by Yolanda Walsh (Mediclinic)

Reducing SSIs Experience from the field

Sharing challenges and successes in implementing the bundle – open session

Key concepts in systems improvement

“Every system is perfectly designed to achieve the

result it gets”

12/2008

Improvement alwaysrequires change. . . .

. . . .but not all change is an Improvement!

The change • is inappropriate to problem• was not implemented as expected• worked but it caused an upset in another part of the system

The three reasons that a change doesn’t result in an improvement

What are we trying toaccomplish?

How will we know that achange is an improvement?

What change can we make thatwill result in improvement?

Model for ImprovementAim

Change

Measures

‘Quality Improvement is data driven change’

Use the data to tell the improvement story

Measurement

2.10

1.52

2.13

1.93

1.30

1.94

1.30

1.09

1.80

1.59

1.38

1.16 1.21

1.57

0.95

1.37

0.93

1.38

59%

74% 75%78%

74% 73%

78% 77%79% 78%

81%84% 85%

83% 83% 83%85%

87%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-

0.50

1.00

1.50

2.00

2.50

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Jan

-00

Overall Surgical Site Infections - Bundle Compliance and SSI RateMar 09 - Aug 10

Series2

Series1

HAI : SSI RATE

32

0.0

5.0

10.0

15.0 20

11JU

N

JUL

AU

G

SE

P

OC

T

NO

V

DE

C

2012

JAN

FE

B

MR

C

AP

R

MA

Y

JUN

JUL

AU

G

SE

PT

OC

T

NO

V

DE

C

2013

JAN

FE

B

MR

C

AP

R

MA

Y

JUN

JUL

Ward C: septic surgical cases/1000 surgical cases

Mediclinic Highveld 2013

‘Despite all our efforts aggregated data for surgical cases did not show significant improvement in SSI’s yet process measures were improving

and there was a sense that improvement was happening! However, by retrospectively stratifying the data into the three surgical sub-groups, we demonstrated a significant reduction in SSIs in general surgery cases but not in orthopedic or obstetric cases’.

0

2

4

6

8

Surgeon1 Surgeon2 Surgeon3

#SSIsoverasixmonthperiodbysurgeon

Another hospital SSIs per surgeon over a six month period

Small multiples: comparing units or hospitals

Data source:

Measurement

Are we getting closer to our target? – SSI rates

Outcome measure

Measurement

Did we use the whole bundle in every patient every time? - % Compliance with the bundle

Process measure (Bundle compliance)

Measurement

Was the change an improvement?- Demonstrate significant change

Measuring the impact of a change

• Define the indicators - % - rate- days between infections- cases between infections

• Define the elements of each indicator- numerator- denominator

Defining the measures

• System to collect and collate the data• Display and discuss the data

- graphs- meetings

• Respond to the data- analysis - test a change

Measurement system

• Welsh Safety Cross• Line graphs and Run Chart Rules• ‘Days between infection’ graphs

Displaying the data

Safety CalendarWelsh 1000 lives campaign

I

Developed by Annette Bartley

Welsh safety Calendar

Month 1

Month 2

Month 3

No incident

More than 1 incident

New incident

KEY

Numerator only

44

Welsh Patient Safety Project

Christian Barnard Hospital

Tracking a single indicator over time

https://www.youtube.com/watch?v=YQd1QoMHYwU&feature=youtu.be

http://youtu.be/8e38RCU8-uA

Comment on the performance of these two (same size) clinics

0

2

4

6

8

10

J F M A M J J A S O N D

#ANCclients

ClinicA:NumberofANCclientstes ngHIV+/m

0

2

4

6

8

10

J F M A M J J A S O N D#ANCcllients

ClinicB:NumberofANCclientstes ngHIV+/m

Variation

0

2

4

6

8

10

J F M A M J J A S O N D

#ANCclients

ClinicA:NumberofANCclientstes ngHIV+/m

Ups and downs in the data are normal – so how do we know how the staff are doing?

Variation• If we know how a stable system is performing

─Range─Median

• We can predict where the next point will fall

0

1

2

3

4

5

6

7

8

J F M A M J J A S O N D

FacilityA:Numberofstrokepa entsadmi ed/m

Line graphs

• Is the system performing at an adequate level?

• Is the system stable? (are the points falling within the expected range?)

• Is something happening to the system that we are not aware of?

• Was the change we introduced an improvement?

Tracking data over time

‘dice’ exercise:

• Throw the die 13 times• Record the consecutive scores in the data

table• Plot the data points• Add a median, project the median point

forward with a dotted line

Adding a median

0

1

2

3

4

5

6

7

J F M A M J J A S O N D J F M A M J J A S O N D

Number thrown/monthAdd a title

If your system is stable, you can predict how it will perform

0

1

2

3

4

5

6

7

J F M A M J J A S O N D J F M A M J J A S O N D

Number thrown/month

median

range

Introducing a change to the system

• Add 12 more data points but this time add a change:

For each consecutive data point throw the die 3 times and record the highest score of the three

• Before you start record your prediction about what will happen to your system

Introducing a change- annotate your graph and plot the new data

points

0

1

2

3

4

5

6

7

J F M A M J J A S O N D J F M A M J J A S O N D

Number thrown/month

Change started

Add a title

Make a change in the system

• Was the change an improvement?• Run Chart Rules

Run Chart Rules

• Was the change statistically relevant? (iereal)

• ie did the system change when we introduced the intervention?

Rules For Determining Probability Based Signals of Change

Rule 1 (Shift) : Six or more consecutive POINTS either all above or all below the median. Skip values on the median and continue counting points. Values on the median DO NOT make or break a shift.

Median=10Median=11

Rule 1

0

5

10

15

20

25

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

as

ure

or

Ch

ara

cte

ris

tic

Median 10

Rule 2 (Trend): Five points all going up or all going down. If the value of two or more successive points is the same, ignore one of the points when counting; like values do not make or break a trend.

Rule 2

0

5

10

15

20

25

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

Me

as

ure

or

Ch

ara

cte

ris

tic

Median=11Median 11

Rules For Determining Probability Based Signals of Change

Rules For Determining Probability Based Signals of Change

Rule 3 (Too many or too few runs) To Determine The Number of Runs Above and Below the Median:

A run is a series of points in a row on one side of the median. Some points fall right on the median, which makes it hard to decide which run these points belong to.

So, an easy way to determine the number of runs is to count the number of times the data line crosses the median and add one.

Statistically significant change signaled by too few or too many runs.

Rule 3

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Me

as

ure

or

Ch

ara

ce

ris

tic

Median 11.4

Data line crosses onceToo few runs: total 2 runs

10 Data points not on medianData line crosses onceToo few runs: total 2 runs

DG Fig 3.22

Rule 3: # of RunsTable for Checking for Too Many or Too Few Runs on a Run Chart

Total number of data

points on the run chart

that do not fall on the

median

Lower limit for the number of runs(< than this number of runs is “too few”)

Upper limit for the number of runs(> than this number of runs is “too many”)

10 3 9

11 3 10

12 3 11

13 4 11

14 4 12

15 5 12

16 5 13

17 5 13

18 6 14

19 6 15

20 6 16

21 7 16

22 7 17

23 7 17

24 8 18

25 8 18Table is based on about a 5% risk of failing the run test for random patterns of data.Adapted from Swed, Feda S. and Eisenhart, C. (1943). “Tables for Testing Randomness of Grouping in a Sequenceof Alternatives. Annals of Mathematical Statistics. Vol. XIV, pp.66 and 87, Tables II and III.

RULE 4:AstronomicalFor detecting unusually large or small numbers:

Data that is Blatantly Obvious different valueEveryone studying the chart agrees that it is unusual

Remember:Every data set will have a high and a low - this does not mean the

high or low are astronomical

Rule 4

0

5

10

15

20

25

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

Mea

sure

men

t or

Cha

ract

eris

tic

Rules For Determining Probability Based Signals of Change

0

2000

4000

6000

8000

10000

Jan-13

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Jan-14

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Jan-15

Feb

Mar

StateBpublic:PregnantwomenCounseled,testedandreceivedresults(CTRR)

Astronomical Point

Run Chart: Rules for Identifying Statistically Significant Change

Astronomical Point: a obviously, even blatantly different valueNote: Every set of data will have a highest and lowest data point. This does not mean the high or low are astronomical

Median

Shift: 6 points in row on same side of the median Note: A point exactly on the centerline does not cancel or count towards a shift

Median

Median

Trend: 5 points in row headed in same directionNote: Ties between two consecutive points

don’t cancel or add to a trend

Rule 3

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Mea

sure

or

Cha

race

rist

ic

Median 11.4

Data line crosses onceToo few runs: total 2 runs

Runs: too few or too many runs

Rule 1 Rule 2

Rule 4Rule 3

Provost and Murray

Run Charts

• 12-18 data points to understand the variation in a stable system (baseline)

• Baseline median performance• Continuous data – over a number of years• Run chart rules allow you to determine if

your median performance has changed• Annotate the line graph to see where

changes were made

Apply the Run Chart Rules to your graph to see the change was an

improvement

0

2

4

6

8

10

12

14

J FMAM J J A S ON D J FMAM J J A S ON D

FacilityA:Numberofstrokepa entsadmi ed

Change made

Was the change an improvement?

• The change must alter the overall median performance of the system

Measuring HAI

The concept of ‘days between’ infections

For measure ‘rare’ events (occur < 10%)

Off the internet, Google pictures

Off the internet, Google pictures

Source:Google pictures

Days between events (infection)

Days Betweenevents(egInfection)

Sequence of events (eg Infection)

1st 2nd 3rd 4th 5th

5

10

15

20

25

Days between events

1st 2nd 3rd 4th 5th

5

10

15

20

25Days Betweenevents(egInfection)

Sequence of events (eg Infection)

Maternal deaths – Malawi

For the “NO Maternal Death” Campaign

a colorful, laminated A4 paper that said “Days without a Maternal Death: ______”.

were hung in every Labour Ward for all (providers, patients and guardians) to see and the number was filled in daily with a dry erase marker

Neonatal deaths – Malare Health Centre, 5’s Alive! Project, Ghana

I

Larger is better

IMeasuring rare events and time-between measures. James Benneyan IHI

Use the tools provided to display the dataas a Safety Calendar and a ‘Days Between

Infection’ run chart

July Aug Sep Oct

5/7 13/8 7/9 5/10

5/7 9/9 8/10

6/7 15/9 15/10

11/7 19/10

25/7 20/10

27/7 21/10

25/10

Date of infection

# Days since last infection

Days BetweenInfection

Sequence of Infections

Reporting on the extra-net

What are we trying toaccomplish?

How will we know that achange is an improvement?

What change can we make thatwill result in improvement?

Model for Improvement

Act Plan

Study Do

How to make a change

Closing the “Know-Do” gap

I

The Implementation Gap

PLAN

IMPLEMENT

FAIL

PROBLEM

EVIDENCE BASED SOLUTION

“traditional” attempts to change

I

DO

STUDY

ACTIMPLEMENT

SUCCEED/ SUSTAIN

Overcoming the Implementation Gap

GREAT IDEAS

SYSTEM ANALYSIS to identify barriers to carePROBLEM

PLAN

Rapid Cycle Change

What can we change that will result in an improvement?

PLAN

DO

STUDY

ACT

How will we know that a change is an improvement?

What are we trying to accomplish?

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

Actabandonadaptadopt

Plan

Study Do

PDSA – scientific method

whatwherewhowhenhow

The Plan-Do-Study-Act Cycle

Improvement Guide, Chapter 5, p. 97

P: Ask one doctor to use clippers instead of razor with 1 patient

D: Dr. M used clippers on 2 patients. Was pleased. Told staff not to put razor on his cart again!

S: Was some resistance as predicted. Lack of supplies unexpected barrier.

A: Clippers ordered. Another PDSA with 6 other surgeons planned

Improving many parts of the bundle/system at once.

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

part 3 part 4part 1 part 2

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

PLAN

DO

STUDY

ACT

Make it do-able

Work as a team• Small, representative improvement team• Meet regularly, have regular huddles

Start in one department• where you have the best chance of success

Start small building as you gain confidence• Get your measures in place• Test one bundle element

Driver DiagramYour theory of change

Surgical Site Infections reduced to ……days

or …. cases

between infectionby ……………..

AIMS PRIMARY DRIVERS

SECONDARY DRIVERS

INTERVENTIONS(Change Ideas)

MEASURES

Activated and empowered

healthcare team

SSI bundle implemented

Other system factors well managed

Leadership involved & supportive ??

??

??

??

??

??

??

??

??

??

Improvement team meets regularly

QI activities are planned

Antiseptic skin prep & chlorhexidine wash

Prophylactic antibiotics

Appropriate hair removal

??

??

Normoglycaemiamaintained

Normothermiamaintained

• % Preopantiseptic

• % Preopantibiotics

• % Hair removal• % Postop

glucose• % Postop temp

• Manager regularly attends meetings

• SSI rate or days between infection

• Run charts / crosses updated monthly

W Cape BCA SSI InitiativeDriver Diagram v0.1

Environment (theatre) is clean

Excellent hand hygiene

Excellent wound care

Dependably sterile supplies

Supplies available

Drugs available

Routine measurement

system

• Weekly case counts• Infection count/date

• Quarterly reports to management

SSI Process & Outcomes

Driver Diagram

AIM of this change:

PROBLEM :

WILL

IDEAS EXECUTION

Set to go