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Turning Data into Tools to Save Lives

Frank Myers BA, MA, CIC

Infection Preventionist III

University of California, San Diego Healthcare System

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• Data are unactionable

• Data are unrevealing

• Data are stagnant

Data are useless

• Changes minds

• Changes behavior

• Is actionable

• Is fluid

Information

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• You need to understand

– What others think

– What motivates behavior change for your audience

The key is changing data into information

• HIV seroprevalence study – (double blinded testing of all syphilis specimens)

• Designed for planning for future AIDS needs in the state

• Study of high and low risk populations– IDU (drug screen)

– STD patients

– Woman giving birth (heel stick for genetic diseases)

An Old Story(1980’s)

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• 34% of all patients attending one STD clinic

were HIV +

• A combination of MSM, IDU, and high risk

heterosexual activity

STD clinic data

• More than 1 out of 3 people were HIV+ at the clinic – And by extension the waiting room

• Most people would return to the clinic within 2 years

• I was scared and wanted them to stop putting themselves at risk

• I wanted to motivate a behavior change– They thought their behavior was resulting in only a

trip to the STD clinic

Data to information

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• 34% is not a number people understand

• 1 out of 3 is (sort of)

Speaking the Language

• 1 out of every 3 people in this STD clinic

waiting room likely has HIV. Look around. Is

that one you? If not what can you do to

prevent yourself from being in this waiting

room again and being at risk again?

– Returns to the STD clinic went down significantly.

– STDs in the ZIP code it served went down

significantly.

Motivation

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• Bias from who completes the form

• If you comply with completing the form you

are more likely to comply with safe CLIP

Who has found anything useful in the CLIP data?

• What percent of central lines inserted in your

ICU are femoral?

– 0-1%

– 2-5%

– 6-8%

– 9-11%

– 11% or more

Vote Time!

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CLIP 8.2% of lines inserted in California adult ICUs are femoral lines

• The source of all knowledge is comparative!

Congratulations you turned data into information

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Flow of ProcessSSI Outlier identified in

analysis BEFORE Infection

Control Committee (ICC)

meeting

Data goes to Infection

Control Committee (ICC)

Data given to

Medical Director

Investigation into causes for

high SSI rate is done before

next ICC meeting

Data discussed at ICC and

decision to investigate further

reached

Non-standardized

investigations done using

ICC hypothesis

Data and identified issues

shared with surgical group

If theories are incorrect,

investigation results go

back to ICC (?)

Flow of New Process

SSI outlier identified in

analysis before ICC meeting

Analysis done of likely causes

for high rate

Data go to Medical DirectorData go to ICC along with

issues ruled out as causes

Data and identified issues

shared with surgical group

Discussion at ICC about data,

identified issues

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� Identify a surgical site outlier�Use SIR and p value assuming an alpha 0.05

� Pick a least a 6 months to a year of data

�Run line listing report to capture all variables which is a interest to you and the providers

Extracting Line Listing from NSHN

Selecting your variables from NSHN

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Sorting the Variables

� Use the filter icon in Excel to help you sort your variables

� Filter by infections / No infections

1) Gender

2) Avg. AGE

3) BMI Avg.

4) Scope

5) Average Surgery Time

6) ASA Score

7) Wound Class

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Sample of a Line Listing

Line listing from NHSN formula used =(3*60)+K42

patID dob gender proceedProc Date

Proc Code

asaBMI_ val

proc Duration

Hr

Combo

of

hrs/mins

ProcDuratio

nMin

Anes. emerg scopeSw

Class

trauma

Age At

Procinfection

12345678 1/1/00 F 12345678 1/1/00 colo 3 24.5 3 222 42 3 N Y 2 N 32 Y

� Once the line listing is complete

� Sort out your variables

� Infection – No infections

� Female – Males

� Scope – Not Scope

� Avg Procedure time

� BMI average

� ASA score

Sorting the Line Listing

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Identifying the variable that can be an value

when analyzing SSI.

EXAMPLES: INFECTION NO INFECTIONS

GENDER Females (10) Males (9) = 19 Females (103) Males (117) =220

Avg. AGE 60 54

AVG SURG.TIME 4.7 hours 3.6 hours

SCOPE 9 Were scoped 129 Not scoped

BMI Average 27.4 25

ASA Score 2.7 2.9

� 88 % of COLO patients in 8 months had no infections

Built in NHSN test

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http://www.openepi.com/SMR/SMR.htm

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Statistical Test

The following test can help to identify different variables, which can help to recognize

what is significantly expected or not expected.

� Whitney-Mann Test (ordinal data)

� Fisher Exact Test

� T- test independent Sample Test (add this website)

� Website: http://www.real-statistics.com

Whitney-Mann Test

� Whitney- Mann U Test: is a nonparametric test of the null hypothesis that two

populations are the same against an alternative hypothesis, especially that a

particular population tends to have larger values than the other.

Example: ASA Score ( infection & No Infection)

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Mann-Whitney U Test

RAW DATA

ASA Infections ASA No Infection

3 3 3 3 3 3 2 3

2 3 3 4 3 3 2 3

2 3 3 4 2 3 2 3

3 3 3 3 3 2 3 2

3 3 3 3 2

2 3 2 2

3 3 3 1

3 4 3 3

3 3 2 3

3 3 5 3

3 4 2 3

3 3 3 2

3 3 2 2

3 3 2 2

3 2 2 2

1 3 2 3

3 2 2 3

3 3 3 3

4 3 3 3

2 3 3 3

4 4 4

Test via normal distribution

ASA infection ASA No Infection

count 17 64

median 3 3

rank sum 51 163

U 1190 3005

α 0.05

tails 1

U 1190

mean 544

variance 7434.7

Example: ASA Score ( infection & No Infection)

Whitney-Mann Test

� Website: http://www.real-statistics.com

KEY = CC 1

Mann-Whitney U Test CO 2

Raw Data D 3

U 4

Wound Class Infections Wound Class No Infection

1 1 1 1 2

1 2 1 2 1

2 1 1 1 3

2 1 1 1 2

2 1 2 1 1

2 3 1 2 1

2 1 1 1 2

2 2 2 2 1

2 1 4 4 1

1 1 2 3 1

1 1 1 1

1 1 2 1

2 2 2 1

3 1 2 1

1 1 1 2

2 2 1 1

1 1 1 1

1 1 1 2

2 2 1 1

1 3 1 1

1 1 3 3

3 3 3 3

Whitney-Mann Test

Example: Wound Class

(infection & No Infection)

Test via normal distribution

Wound Class

infection

Wound Class

No Infection

count 10 88

median 2 1

rank sum 16 138

U 919 4658

α 0.05

tails 1

U 919

mean 440

variance 7260

std dev 85.20563362

z-score 5.621694009

U-crit 299.3492045

p-value 0.999999991

sig no

r 0.567878651

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Fisher Exact Test

�Fisher Exact Test : is a statistical significance test used in the

analysis of contingency tables. Although in practice it is

employed when sample sizes are small, it is valid for all

sample sizes.

� 86% of those patients were not scoped

� http://research.microsoft.com/en us/um/redmond/projects/mscompbio/fisherexacttest/

Fisher Exact Test

Scope No Scope

Infection a= 10 b= 9

No Infection c= 88 d=131

Total 98 140

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Fisher Exact Test

Observed Values Expected Values

emergency no emergency Total emergency non emergency Total

infection 0 19 infection 0 19 0

non INFECTIONS 0 219 NON-EMERGENCY 0 219 0

Total 0 238 Total 0 238 0

α 0.05

df 1

χ2 #DIV/0!

p-value #DIV/0!

χ2-crit 3.841458821

sig #DIV/0!

Example: Emergency (infection & No Infection)

T-Test-Two Independent Sample Test

� T-Test- two independent sample test: A t-test helps you compare whether two

groups have different average values (for example, whether men and women have

different average heights).

Example: Age & BMI (infection/ no infections)

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AGE SUMMARY Hyp Mean Diff 0

Groups Count Mean Variance Cohen d

Infection 10 61.8 134.8

No Infection 219 53.4 298.7

Pooled 292.2 0.49183

T TEST: Equal Variances Alpha 0.05

std err t-stat df p-value t-crit lower upper sig effect r

One Tail 5.527607 1.520967 227 0.06483 1.651594 no 0.10044

Two Tail 5.527607 1.520967 227 0.12966 1.97047 -2.48468 19.29928785 no 0.10044

T Test: Two Independent Samples for

AGE

T Test: Two Independent Samples for

BMI

BMI SUMMARY

Hyp Mean

Diff 0

Groups Count Mean Variance Cohen d

Infection 12 27.4 118.3

No Infection 147 24.9 37.0

Pooled 159 42.68252 0.381046

T TEST: Equal Variances Alpha 0.05

std err t-stat df p-value t-crit lower upper sig effect r

One Tail 1.961437 1.269195 157 0.103125 1.654617 no 0.100777

Two Tail 1.961437 1.269195 157 0.20625 1.975189 -1.38476 6.363654 no 0.100777

� Jan 2014 we started to capture BMI status and since then 12 patients had infections.

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New Process

� Turn around time is days after Infection Control Committee

(as analysis is done before the meeting)

� The new process will help to rule out any problems (examples)

– Surgeon performance

– Operative arena or discharge issues

– Time to onset and organisms

– Failure of the risk model to account for patient severity (gender, age risk

score)

Challenges Remaining

• Moving the data from ICC to other

departments to motivate change

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Program Evaluations should

• Include patient data

– How many patients acquired infections

– Fewer or Less than last year

• Adjusted for volumes

• Adjusted for severity

– A quick thought point

• Include Financial Data

• Include additional or less mortality

The Source of all Knowledge is Comparative

• Compare to other facilities

– Our patients are always sicker

– The other hospital always lies

• Compare to ourselves

– Changes in our demographics and services

• Adjustable

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To look at our accomplishments (or failures) from last year

• We need last year’s CLABSI rate and this years

CLABSI rate and line days (same for CAUTI)

• Last year’s SIR and this year’s total observed

infections and total expected infections

• Last year’s HO C. difficile rate and this year’s HO C.

difficile rate and patient days.

• Last year’s MRSA hospital onset incidence

bacteremia rate and this year’s hospital onset MRSA

bacteremia rate and patient days. (same for VRE).

Get costs!

• Get a range, not one number– Pick the number in

• The middle?

• The lowest cost?

• The highest cost?

• Range?

• Know the difference between costs and charges

• We never save costs (WE AVOID THEM)

• http://www.cdc.gov/hai/pdfs/hai/scott_costpaper.pdf

• To update to 2014 healthcare dollars

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Want to get really fancy? Convert 2007 Healthcare dollars to 2013

healthcare dollars?• http://www.halfhill.com/inflation.html

When you hit “Options…”

You can select “U.S.

Medical Costs Inflation”

Put in 2007 for starting

year

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So $6,461 CLABSI cost in 2007 is what today?

The Patient Cost

• Find Attributable Mortality rates

– Attributable not overall mortality rates

• You may want to look at

– Attributable LOS

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Now the Math

CLABSI CAUTI Formula

• FY 2012/2013 CLABSI rate was 1.89 per 1000 line days

• This year our CLABSI rate was 1.19 (15/12571)

• If we had stayed at our old rate (1.89) we would have seen .00189 * 12571=23.76 infections. Round to 24.

• 24-15=9 So we prevented 9 infections by lowering our CLABSI rates!

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So now you just made nurses happy!

• 9 people they can see that

• But the CFO? You aren’t speaking their

language so get that attributable cost number

• $7288 * 9 = $65,592 in avoided costs or 90%

of what we pay an SD nurse a year

From Salary.com

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But the Medical Staff Committee Doesn’t Care about Dollars

• Attributable Mortality numbers ranged from

12% to 25%

• So 9 * 0.12= 1.08 or the lowered CLABSI rate

prevented 1 death

MRSA, VRE bacteremia and C diff

• Same basic formula just using patient days

rather than device days.

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SSIa little trickier

• Last year’s SIR was 1.27

• This year’s expected infections 38

• This year’s observed infections were 27

• So 1.27 * 38 =48.26 infections if the SIR had

stayed at 1.27 (rather than the 0.71 it is now)

• So 48-27 =21 SSI prevented by dropping our

rates

Infections & Costs AvoidedEstimated

• We had 21 fewer CLABSI in FY2013-20143 than we had in FY 2012-2013 (assuming FY2012-2013’s line days). These fewer infections saved UC San Diego Health Systems $542,829 and over 3 patients lives (based on published attributable mortality).

• We had 2 fewer C. difficile cases in the same time period than we had in the previous FY (assuming FY 2013-2014’s patient days). These additional infections saved UC San Diego Health Systems $16180

• We had 86 fewer CAUTI in the same time period than we had in the previous FY (assuming FY2013-2014 foleydays). These fewer infections helped UC San Diego Health System avoid an additional $71,552 in costs

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FY 2012-2013 IC Outcomes Compared to FY 2013-2014

• We had 8 more VRE bacteremias inFY2013-2014 than we had in FY 2012-2013 (assuming FY2013-2014 patient days).

• We had 7 more surgical site infections in the same time period than we had in the previous (assuming FY2013-2014 surgical volumes and acuity). These additional infections cost UC San Diego Health Systems an additional $206,101 in costs

• We had 1 additional MRSA bacteremia compared to the previous FY (assuming the FY 2013-2014 patient days) This additional case cost UC San Diego Health System an additional $3700 in costs

• Because attributable mortality rates are difficult to come by and estimated overall impact on patients lives could not be conducted.

The big picture

• For the fiscal year 93 fewer patients had

infections in the outcomes we evaluate.

• The organization avoided $420,760 in

unreimbursed treatment costs in avoiding

theses infections.

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Make the numbers “REAL”

• Example you have a daily census of 275 in

your facility and an average LOS of 3 days and

this year say a very small drop in your C. diff

rate resulting in 2 fewer healthcare associated

C diff cases for the year but still see 181 cases

for the year.

• 181 is an abstract number. I can’t think of 181

people.

But I can think of

• “We infect almost a patient every other day

with C diff”

• “On any given day in house we have two

patients here who we gave C diff during their

stay”

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Process Measures

• This same method can be used to show

reduction number of:

– IUSS

• Actionable what is being IUSS’ed

– Blood culture contaminates

• Results

– Other processes

Questions?

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