The Effective Display of Health & Safety Data to

Achieve Desired Decision Making

Robert Emery, DrPH, CHP, CIH, CBSP, CSP, CHMM, CPP, ARMVice President for Safety, Health, Environment & Risk Management

The University of Texas Health Science Center at HoustonAssociate Professor of Occupational Health

The University of Texas School of Public Health

Why Training on Data Presentation ?

• An interesting dilemma:

– Almost all programs thrive on data

– Virtually every important decision is based on data to some extent

– Formal training in the area of compelling data presentations is rare for many professionals

– The ability to compellingly display data is the key to desired decision making

Why Training on Data Presentation (cont.)?

• The safety profession is particularly awash in bad examples of data presentations!

• We’ve all endured them at some point in our careers!

• Commentary: This may be the reason for repeated encounters with stakeholders do not understand what safety does.

Achieving Safety & Security Data Display Excellence

• The presentation of complex ideas and concepts in ways that are– Clear– Precise– Efficient

• How do we go about achieving this?

Go to The Experts On Information Display

• Tukey, JW, Exploratory Data Analysis, Reading, MA 1977

• Tukey, PA, Tukey, JW Summarization: smoothing; supplemented views, in Vic Barnett ed. Interpreting Multivariate Data, Chichester, England, 1982

• Tufte, ER, The Visual Display of Quantitative Information, Cheshire, CT, 2001

• Tufte, ER, Envisioning Information, Cheshire, CT, 1990

• Williams, R The Non-Designers Book: Design and Typographic Principles for the Visual Novice. Berkley, CA, 1994

• Tufte, ER, Visual Explanations, Cheshire, CT, 1997

Recommendations

• Don’t blindly rely on the automatic graphic formatting provided by Excel or Powerpoint!

• Encourage the eye to compare different data

• Representations of numbers should be directly proportional to their numerical quantities

• Use clear, detailed, and thorough labeling

Recommendations (cont.)

• Display the variation of data, not a variation of design

• Maximize the data to ink ratio – put most of the ink to work telling about the data!

• When possible, use horizontal graphics: 50% wider than tall is usually best

Compelling Remark by Tufte

• “Visual reasoning occurs more effectively when relevant information is shown adjacent in the space within our eye-span”

• “This is especially true for statistical data where the fundamental analytical act is to make comparisons”

• The key point: “compared to what?”

Four UTHSCH “Make Over” Examples

• Data we accumulated and displayed on:– Nuisance Fire Alarms– Workers compensation experience modifiers– First reports of injury– Corridor clearance

• But first, 2 quick notes:– The forum to be used:

• The “big screen” versus the “small screen”?• In what setting are most important decisions made?

– Like fashion, there are likely no right answers – individual tastes apply, but some universal rules will become apparent

Results of the Great UTHSC-H Nuisance Fire Alarm Challenge

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Contractor Smoke/Fire

Spontaneous Maintenance

Results of the Great UTHSC-H Nuisance Fire Alarm Challenge

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Contractor Smoke/Fire Spontaneous Maintenance

Results of the Great UTHSC-H Nuisance Fire Alarm Challenge

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Contractor Smoke/Fire Spontaneous Maintenance

Results of the Great UTHSC-H Nuisance Fire Alarm Challenge

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Contractor Smoke/Fire Spontaneous Maintenance

Results of the Great UTHSC-H Nuisance Fire Alarm Challenge

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Contractor Smoke/Fire

Spontaneous Maintenance

Results of the Great UTHSC-H Nuisance Fire Alarm Challenge

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Contractor Smoke/Fire

Spontaneous Maintenance

Results of the Great UTHSC-H Nuisance Fire Alarm Challenge

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Results of the Great UTHSC-H Nuisance Fire Alarm Challenge (FY04)

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Caused by UTHSCH Facilities work

Caused by detector malfunction or dust accumulation

Caused by actual smoke or fire

Caused by outside contractor work

Fiscal Year 04

Results of the Great UTHSC-H Nuisance Fire Alarm Challenge

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Contractor Smoke/Fire

Spontaneous Maintenance

Employee Worker’s Comp Experience Modifier

compared to other UT health components, FY 98-FY 04

0

0.2

0.4

0.6

0.8

1

98 99 2000 2001 2002 2003 2004

UT-Tyler UTMB UT-SA MDA UT-H UT-SW

Rate of "1" industry average, representing $1 premium per $100

WCI Premium Adjustment for UTS Health Components(discount premium rating as compared to a baseline of 1)

0

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1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Fiscal year

UT Health Center Tyler (0.40)UT Medical Branch Galveston (0.38)

UT HSC San Antonio (0.27)UT Southwestern Dallas (0.24)

UT HSC Houston (0.17)UT MD Anderson Cancer Center (0.14)

Losses – PersonnelReported Injuries by Population

0

200

400

600

800

98 99 00 01 02 03 04

Employee Resident Student

690 694 715 675623 608 511

Number of First Reports of Injury, by Population Type

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800

FY98 FY99 FY00 FY01 FY02 FY03 FY04 FY05 FY06 FY07

Total (n = 513)

Employees (n = 284)

Residents (n = 140)

Students (n = 89)

Medical School Building Hallway Occlusion (2004)

0 100 200 300 400

Basement (under construction) - Pete Martinez

Ground - Pete Mart inez

1 - J ason LeBlanc

2 - Matthew Keck

3 - Gamalial Torres

4 - Leon Brown

5 - Dita Geary

6 - Selome Ayele

7 - J ulie Broussard

Penthouse - J ason Bible

T otal Oc c l uded Feet

Feb-04

Apr -04

May-04

J un-04

J ul -04

Aug-04

Sep-04

Oct-04

Dec-04

J an-05

Feb-05

MSB Corridor Blockage in Cumulative Occluded Linear Feet, by Month and Floor

(building floor indicated at origin of each line)

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1st

G

Important Caveats• Although the techniques displayed here are

powerful, there are some downsides to this approach– Time involved to create assemble data and create non-

standard graphs may not mesh with work demands– Relentless tinkering and artistic judgment

• Suggested sources for regular observations to develop an intuitive feel for the process– Suggested consistent source of good examples:

• Wall Street Journal

– Suggested consistent source of not-so-good examples: • USA Today “char-toons”

Summary

• The ability to display data compellingly is the key to desired decision making

• Always anticipate “compared to what?”

• Maximize the data-to-ink ratio – e.g. eliminate the unnecessary

• Think about what it is you’re trying to say

• Show to others unfamiliar with the topic without speaking – does this tell the story we’re trying to tell?

Your Questions at This Point?

Now Let’s Look at Some Other Examples

COLLABORATIVE LABORATORY INSPECTION PROGRAM (CLIP)

Total PI’s #Without Lab Violations

# With Lab Violations

%Without Lab Violations

%With Lab Violations

May 2005 94 53 41 56.38 43.62

June 2005 78 40 38 51.28 48.72

July 2005 84 54 30 64.29 35.71

August 2005 74 54 20 72.97 27.03

September 2005 69 39 30 56.52 43.48

October 2005 80 50 30 62.50 37.50

•During October 2005, 80 Principle Investigators for a total of 316 laboratory rooms were inspected•A total of 30 CLIP inspections were performed

PI Inspections:

Comprehensive Laboratory Inspection Program (CLIP) Activities and Outcomes, 2005

Month in Number of Principle Inspections InspectionsYear 2005 Investigators Inspected Without Violations With Violations

May 94 53 (56 %) 41 (44%)

June 78 40 (51%) 38 (49%)

July 84 54 (64%) 30 (36%)

August 74 54 (73%) 20 (27%)

September 69 39 (56%) 30 (44%)

October 80 50 (62%) 30 (38%)

2005 Collaborative Laboratory Inspection Program (CLIP) Inspection Activities and Compliance Findings

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2005 Collaborative Laboratory Inspection Program (CLIP) Inspection Activities and Compliance Findings

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Months within Calendar Year 2005

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Number with violations

Figure 3. Receipt of Radioactive Material

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Fig. 3. Receipts of Radioactive Materials

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Number of medical use radioactive material receipts

Number of non-medical use radioactive material receipts

Fig. 3. Receipts of Radioactive Materials

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FY 00 FY 01 FY 02 FY 03 FY 04 FY 05 FY 06

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Number of medical use radioactive material receipts

Number of non-medical use radioactive material receipts

OSHA LAB STANDARD & EPA COMPLIANCE

0255075

100125150175200225250275300325350

2004 2005

labs audited Total # of labs # in compliance

Results of University EH&S Lab Inspection Program, 2003 to 2005

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Calendar Year

Num

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Number of labs inspected and no violations detected

Number of labs inspected and one or more violation detected

Number of labs existing but not inspected

Note: 33 labs added to campus in 2005, increasing total from

269 to 302.

Average Cost of Workers Compensation Claims By CausePeriod FY01 - FY06

$0.00$5,000.00

$10,000.00$15,000.00$20,000.00$25,000.00$30,000.00$35,000.00$40,000.00$45,000.00

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Type of Claim

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#

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Slips, trips, falls – inside

Cumulative trauma

Overextension, twisting

Slips, trips, falls – outside

Lifting/handling

Uncontrolled object

Average Cost of Workers Compensation Claims, by Cause, for Period FY01 - FY06

Average cost from total of 3 events

Average cost from total of 3 events

Average cost from total of 4 events

Average cost from total of 4 events

Average cost from total of 10

events

Average cost from total of 4

events

2005 Workers' Compensation by Injury Type

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Caught In

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2005 Total Number of Monthly Workers Compensation Claims inclusive of the three most frequent identifiable classes of injuries

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FallStrainCut, Puncture

Building Related Programs

-100

0

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1986 1996 1998 2003

Years

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Fire Ext. Systems

Fire Extinguishers

Fire Related Incidents

Asbestos Projects

  Fire Extinguisher Systems

Fire Extinguishers Fire Related Incidents

Asbestos Projects

         

1986 0 0 0 0

1996 203 19 91 55

1998 208 25 15 68

2003 437 46 -18 191

Growth in Occupational Safety Responsibilities 1986 to 2003

Building Fire Systems to be Serviced

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Years

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1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Years

Num

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Fiscal Year

EH&S Staffing Trends

EHS StaffBudget Augments

Budget Cuts

UCR EH&S Staff, Extramural Research Funding and Grant Awards

418

457

498484

559

529 523

583

622 619610

710

735

800816

$23$26

$28

$33$36

$40 $39$43

$45 $45

$51

$58

1518.5 19 19 20 19 19 18.5 17

1518

2022 20.5

17

$65

$143

$166

$87$82

$106

$123

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EH

S S

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s-Millio

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Number of Awards

Grants in Millions $

EHS Career Staff

Campus Sq. Footage & EHS Staffing

72,964

55,200

29,700

20,000

30,000

40,000

50,000

60,000

70,000

1990 2005 2010

Fiscal YearG

SF

in T

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s o

f Th

ou

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s

10.00

20.00

30.00

40.00

50.00

60.00

EH

S F

TE

EHS Staffing & Student Growth

8,006

15,666

20,140

-

5,000

10,000

15,000

20,000

1990 2005 2010

Fiscal Year

10

15

20

25

30

35

40

45

50

55

60

All Students

EHS Staff

UCR Campus Growth Indicators Compared to EH&S Staffing

Campus Gross Square Footage

0

20,000,000

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Years

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Extramural Research Funding

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Years

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EH&S Staffing

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25

1990 1995 2000 2005 2010

Years

Nu

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taff

Journal of Environmental Health, September 2006, page 49

Quat-Safe and Cotton Food Service Towel Quanternary Ammonium Chloride Solution

Concentration Compared Over Time*

Quat-Safe Solutions

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0 15 30 45 60 75

Time in minutes

ppm

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*Towels removed and rinsed at each interval

Offshore Benzene

Time Sample Taken (Hours) Sampling Result in ppm

0 0

2 0.25

4 0.3

6 0.4

8 0.1

10 0.002

12 0.3

14 0.2

16 0

18 0.25

20 0.3

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30 0.2

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40 0.1

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44 0.3

46 0.2

48 0

50 0.25

Example of oil spill worker benzene exposure monitoring data posted on OSHA website by BP industrial hygiene program prior to the availability of any independent OSHA sampling results (June 2010)

Gulf Worker Benzene Exposure

0 10 20 30 40 50 60 70 80

0.0

0.1

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1.0

1.1

1.2

OSHA Permissible ExposureLimit 1 ppm (maximum exposureallowed over 8 hour timeframe)

OSHA Action level 0.5 ppm(harmful level requiringprotection for workers )

Cristina Alvarez Graphic Assignment

Time Sample Taken (hours post leak event)

Sam

plin

g R

es

ult

(p

pm

)