Radiation Safety Measures and Metrics

That Matter!

Robert Emery, DrPH, CHP, CIH, CSP, RBP, CHMM, CPP, ARMAssistant Vice 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

Objectives

• Part 1:– Identify and classify the different types of measures

accrued by radiation safety programs– Differentiate between program measures and metrics– Discuss how these measures may be used

• Part 2:– Examine the science and art of effective data displays– Identify the basic characteristics of effective data

displays– Review actual before and after “make overs” of actual

programmatic data displays

Why Training on Measures?

• An interesting dilemma:

– Radiation safety programs thrive on data

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

– Formal training in the area of compelling data presentations is somewhat rare for radiation safety professionals

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

Why Training on Data Presentation (cont.)?

• The radiation safety profession is 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 management who do not understand what their radiation safety programs do.

Radiation Safety Program Measures

• Step 1. Actual field measurements• Radiation exposure levels, rates• Radiation dose levels, rates• Amounts of radioactivity• Other aspects – distance, mass, area

• Step 2. Programmatic measures• Indicators of workload

• Number of principle investigators• Number of authorized labs• Lab inspections

• Indicators of program outcomes• Regulatory inspection outcomes?• Actual doses received? In excess of ALARA limits?

• Note – what is the applicability of this information to the annual Radiation Protection Program review?

Radiation Safety Program Measures

• Step 3: Programmatic metrics

• Comparing data to major organizational drivers, such as

• Institutional extramural research expenditures?• Patient revenues?• Institutional square footage?

• Example of the power of metrics: What does the license/registration cost versus what is it worth?

Radiation Safety Program Measures

• Step 4: Actually presenting or communicating the data to others

• Some key questions:

• To whom might we be presenting your data to?

• Will these different stakeholders understand or comprehend what you’re trying to say?

• How long do you typically have to tell your story?

Examples

• Four examples of safety data displays (3 radiation-related, the fourth more generic)

• 1. Communicating to room occupants their possible radiation exposures

• 2. Communicating to the radiation safety committee and upper management the capacity of our broad scope license

• 3. Communicating to upper management general radiation safety trends

• 4. Multiple examples of attempts to communicate effects of routine surveillance program

Example 1: Area Radiation Levels

Figure 1. Recorded radiation doses in mrem/yr on inside walls of vault room as compared to regulatory limits, as recorded by area dosimeters in place for calendar year 2004

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Background radiation dose level 360 mrem/yr

Occupational dose limit 5,000 mrem/yr, beyond 360 mrem/yr background dose level

General public limit 100 mrem/yr beyond background dose level of 360 mrem/yr

Example 2: Broad Scope License Capacity

Fig. 1 Summary of UTHSCH Broad Scope Radioactive Material License Possession Limits, Collective Sublicensee Possession Limits and Actual On-hand Collective Inventory(a)

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H-3 N-13 C-14 Na-22 P-32 P-33 S-35 Cl-36 Ca-45 Cr-51 I-125 Ce-141 Ra-226

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Collective sublincensee possesion limit

Actual amount on-hand

(a) Data for August 2002

Example 3: 10 Year Prospectus

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Example 4In 2004, an institution initiated a comprehensive lab safety routine surveillance program. Prior to 2004, the safety program was in a reactive mode, with no regularly scheduled routine inspections being performed.

In 2004, the institution possessed 269 labs. Of these, 175 were inspected during the year. Of the 175 labs inspected, 95 did not exhibit any items of non compliance, whereas 80 labs were found to have at least one item of non-compliance that needed to be addressed

In 2005 the institution added 33 labs, bringing the total to 302 labs on campus. In 2005, all of the labs were inspected, with 280 exhibiting no items of non-compliance and 22 exhibiting at least one item of non-compliance

How would you communicate this information so that resources will be provided to continue this very worthwhile effort?

How Do We Achieve Data Display Excellence?

• The goal is to present 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

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

Sample Recommendations

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

• Strive to make large data sets coherent

• Encourage the eye to compare different data

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

• Use clear, detailed, and thorough labeling

Sample 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 Tufte Remark

• 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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Results of the Great UTHSC-H Nuisance Fire Alarm Challenge

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Caused by UTHSCH Facilities workCaused by detector malfunction or dust accumulationCaused by actual smoke or fireCaused by outside contractor work

Fiscal Year 04

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Employee Worker’s Comp Experience Modifier

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

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Worker’s Compensation Insurance Premium Adjustment for UTS Health Components Fiscal Years 2002 to 2007

(discount premium rating as compared to a baseline of 1, three year rolling average adjusts rates for subsequent year)

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3 year period upon which premium is calculated

Losses – PersonnelReported Injuries by Population

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Number of UTHSC-H First Reports of Injury, by Population Type (total population 8,832; employee population 4,425; student population 3,587; resident population 820)

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Total (n = 450)

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Residents (n = 105)Students (n = 85)

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2 - M at t hew Keck

3 - Gamalial T or r es

4 - Leon Br own

5 - Dit a Gear y

6 - Selome Ayele

7 - Julie Br oussar d

Pen t house - Jason Bible

T o t a l O c c l u d e d F e e t

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

(building floor indicated at origin of each line)

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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)

37.5062.50305080October 2005

43.4856.52303969September 2005

27.0372.97205474August 2005

35.7164.29305484July 2005

48.7251.28384078June 2005

43.6256.38415394May 2005

%With Lab Violations

%Without Lab Violations

# With Lab Violations

#Without Lab Violations

Total PI’s

•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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Figure 3. Receipt of Radioactive Material

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Fig. 3. Receipts of Radioactive Materials

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Fig. 3. Receipts of Radioactive Materials

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OSHA LAB STANDARD & EPA COMPLIANCE

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labs audited Total # of labs # in compliance

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

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

269 to 302.

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Wastes Generated from Laboratory OperationsWaste Generated from Administrative DepartmentsWaste Generated from Renovation ProjectsTotal Waste Generatation

Figure 1: Laboratory Waste verses Total Waste Generated

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Figure 1: Hazardous Waste Generation in Pounds by Type of Institutional Activity

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Figure 1: Laboratory Waste verses Total Waste Generated

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Figure 2: Annual Hazardous Waste Disposal Cost by Type of Institutional Activity

Total cost

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Cost of waste from administrative departments

Cost of waste from renovation projects

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EH&S Staffing Trends

EHS StaffBudget AugmentsBudget Cuts

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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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ANNUAL SPH FACULTY ACTIVITIES PEER REVIEW RESULTS FOR ROBERT EMERY 15% FACULTY APPOINTMENT

YEAR 1999 2000 2001 2002 2003 2004

TEACHING 8.86 8.33 8.16 8.25 8.5 8.85

RESEARCH 8.56 9.33 8.85 8.65 8.44 6.75

SERVICE 8.78 8.33 9.06 9.28 9.2 8.6

OVERALL 8.62 8.5 8.78 8.63 8.8 8.09

1 2 3 4 5 6 7 8Note:Emery ranked as Assistant Professor 1999-2000, promoted to Associate Professor in 2002.

Outstanding

Excellent

Good

Acceptable

Annual SPH Faculty Activities Peer Review Results for Emery(15% Faculty Appointment)

Teaching

6

7

8

9

10

1999 2000 2001 2002 2003 2004 2005

Service

6

7

8

9

10

1999 2000 2001 2002 2003 2004 2005

Overall

6

7

8

9

10

1999 2000 2001 2002 2003 2004 2005

Research

6

7

8

9

10

1999 2000 2001 2002 2003 2004 2005

Outstanding

Excellent

Acceptable

Good

Asst Professor Assoc Professor