COMPONENTS OF AN EFFECTIVE FATIGUE RISK MANAGEMENT SYSTEM

Steven R. Hursh, Ph.D.President AND Chief Scientist

Institutes for Behavior Resources, Inc.Baltimore, MD USA

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Institutes for Behavior Resources, Inc.

WHO WE ARE– Independent, nonprofit research, services, and consulting

OUR MISSION– Apply science to the improvement of human and social challenges.

WHAT WE DO:Support evidence-based safety-related policy

– Fatigue, performance and safety

Fatigue impairs performance

Fatigue:–a complex state characterized by

reduced alertness, mental, physical performance

– impairs performance, progressive and often without awareness

– is internally-driven; resulting from interaction of sleep and circadianeffects

retrieved 1/5/2015 http://www.washingtonpost.com/blogs/dr-gridlock/wp/2014/03/26/driver-fatigue-an-issue-in-chicago-subway-derailment/

Adapted from Durmer and Dinges, 2005

Fatigue and Sleep Loss• Impaired executive functioning (decreased

activity within prefrontal cortex and thalamus)

• Involuntary microsleeps

• Unstable attention-based performance (lapses of attention and impulsive actions)

• Cognitive slowing in self-paced tasks; time pressure tasks have increased cognitive errors.

• Response time slows

• Short-term recall and working memory performance decline

• Reduced learning (acquisition)

• Performance requiring divergent thinking deteriorates

• Response suppression errors increase

• Increased likelihood of response perseveration on ineffective solutions

• Increased compensatory effort required to remain effective

• Performance deteriorates as task duration increases

• Increased neglect of activities judged to be nonessential (loss of situational awareness)

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Sleep/24 hours < 8 hours>16 hours continuous wakefulness

Sleep Disorders

Sleep and Circadian Fatigue Risk Factors

CIRCADIAN

- Early morning nadir in performance - Nighttime propensity for sleep- Reduced effectiveness of daytime sleep- Peak in sleep inertia at night

SLEEP HISTORY- 8 hours of sleep /24 hours reduces risk- Transient sleep inertia upon awakening

CONTINUOUS HOURS AWAKE

- Risk of impairment increases > 16 hours awake

SLEEP DEBT- Cumulative effect of chronic sleep restriction- Accumulation of sleep debt in disrupted sleep

WORKLOAD

Fatigue is Insidious

• No practical real-time fatigue detector

• Most underestimate how fatigued they are and how impaired they are by fatigue

• We don’t adapt to working fatigued

Fatigue must be managed: limit the factors that cause fatigue

to sustain alertness and performance

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Fatigue Risk Pyramid

Job Performance ChangesSubjective Awareness

Employee sleep habits, traits, & conditions

Work demands, schedules, and sleep opportunities

Fatigue RelatedErrors

Accidents& Incidents

Based on James Reason, “Managing the Risks of Organizational Accidents”, Figure 1.6, Stages in the development and investigation of an organizational accident.

Level 1 Defense

Level 2 Defense

Level 3 Defense

Fatigue Risk Management System (FRMS)

• A system in which fatigue can be managed

• Derived from SMS

• Continuous Improvement

• Based on science and data

• Customized to the workplace

• Combines several approaches

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Fatigue Risk Management Logic

• Apply a scientific and data driven approach to minimize fatigue risk.

• Eliminate false negatives and false positives created by imperfect rules.

True DistributionAcceptable Risk

True DistributionExcessive Risk

False PositiveFalse Negative

Rules “Not Legal”“Legal”

FRMS

Fatigue Risk Management System

Involves all stakeholdersat each stage:

management, labor, aided by science

EnablersEmployee trainingMedical screeningEconomic analysis

Technology aids

MeasureDefine the situationSchedule evaluationActigraph recordings

Model & AnalyzeModel the fatigue problem

Analyze sources and Fatigue factors

ManageCollaborate for solutionsObtain commitment to

solve problem

Modify/MitigateShared Responsibility

• Operating practices• Labor agreements• Individual “life style”

MonitorAssess operational indicators

Individual self-evaluationFeedback to process

6.0%

6.4%

8.0%

9.8%

12.0%

13.0%

13.0%

20.5%

41.0%

42.0%

88.0%

0% 25% 50% 75% 100%

OutcomeMeasures

& Modeling

Mitigations are Proportional to the RiskEvolutionary, Incremental ImprovementResponsive to Changing Circumstances

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Continuous Improvement Process

FRMS Goals

• avoid situations that pose excessive fatigue, even when permitted • identify certain prohibited situations that do not pose

excessive risk and could be worked under controlled circumstances.

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FRMS Methods to Better Manage Fatigue

• Duty limits and rest policies

• Fatigue reports and root cause analysis

• Fatigue training

• Sleep disorder screening and treatment

• Fatigue related events and root cause analysis

• Policies for non-punitive “fatigue declaration”

• Studies of actual sleep and performance

• Proactive biomathematical modeling of schedules

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Building Blocks of an FRMS

Organization & Personnel

Publicity & Training

Policies & Procedures

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Implementation of FRMS

Data, Tools, & Methods

Explicit Fatigue Modeling: Identify Hazards & Audit Results

SAFTE Model Components

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• Time of Day: between midnight and 0600 hrs.

• Recent Sleep: less than eight hours in last 24 hrs.

• Continuous Hours Awake: more than 17 hours since last major sleep period.

• Cumulative Sleep Debt: more than eight hours accumulation since last full night of sleep (includes disrupted sleep).

• Time on Task/Work Load: continuous work time without a break or intensity of work demands.

• All five factors interact simultaneously in non-linear relationships

• The model can estimate the level of degradation in performance and provide an estimate of schedule induced fatigue risk.

Identify Explicit and Verifiable Fatigue Factors

Fatigue Avoidance Scheduling Tool (FAST) based on Sleep, Activity, Fatigue, and Task Effectiveness

(SAFTE) model

Dashboard shows effectiveness and fatigue

contributors at cursor point

Predicted effectiveness score (performance)

throughout each day of the schedule

Sleep and work times

Calibration Values for SAFTE-FAST

Continuous Hours of

Wakefulness

Effectiveness

(% of Baseline)

Reaction Time

(% increase from

Baseline)

Lapse Likelihood

(relative to 1 for well

rested)

Equivalent

BAC2

16 90 +11% 1.5 NA

18 80 +25% 3.1 < 0.05

18.5 77 +30% 3.7 0.05

19 75 +33% 4.0 > 0.05

21 70 +43% 5.2 0.08

403 651 +54% 6.5 > 0.08

A level of 65 is considered the floor for aviation operations without mitigations by the US AF, equivalent to 40 hrs of continuous wakefulness. Below 77, US AF applies countermeasures to improve performance.

The effects of fatigue may be compared to the effects of blood alcohol concentration to illustrate the severity of fatigue. Fatigue as predicted by SAFTE-FAST and the effects of alcohol are similar in reaction time but very different on other dimensions of performance.

Increase is non-linear because of circadian interactions.

Arnedt, J.T., Wilde, G.J., Munt, P.W., MacLean, A.W. “How do prolonged wakefulness and alcohol compare in the decrements they produce on a simulated driving task?” Accid Anal Prev., 2001 May;33(3):337-44.Dawson, D., Reid, K., 1997. “Fatigue, alcohol and performance impairment.” Nature 388, 23.

INPUT OUTPUT METRIC

Dispatcher IDTM ; Fatigue Audit InterDyne (FAID), InterDyne Pty Ltd.

- Work start and end times- Task risk (Low High)

Shift Fatigue Score(Standard Very High)

Circadian Alertness Simulator (CAS) TM,

Circadian ®- Work start and end times

-Sleep times (optional)Alertness Score

(as a function of time)

Fatigue Avoidance Scheduling Tool (FAST ®), Fatigue Science LLC & IBR Inc

- Work start and end times- Sleep times (optional)

- Sleep Environment Quality

Effectiveness Score(as a function of time)

System for Aircrew Fatigue (SAFE) -Work Periods -Fatigue Levels

Fatigue Calculator, MB Solutions Pty Ltd.

-Work Schedule -Fatigue Risk Likelihood

Common Alertness Prediction Interface (CAPI); CrewAlert, Jeppesen LLC

-Work Schedule -Alertness based fatigue risk

Fatigue Modeling of Select Events

• Simulates conditions given (work, sleep) schedule information

• Based on science: bio-mathematical models of the physiology of fatigue

• Estimate effectiveness or performance based on the factors that contribute to fatigue

INPUT(scheduling, sleep

information)

OUTPUT(fatigue metric)

DECISION

PROCEED

REEVALUATE(with mitigations)

Systematic Modeling Approaches

ProspectiveInput planned work schedules:

assess fatigue & set mitigations on specific schedules set barriers in the scheduling process to reduce safety risk.

RetrospectiveInput schedules as worked :

Periodic review of actual work schedules Use modeling for root cause analysis of fatigue reports

Modeling to Find Customize Solutions

• Analysis of rail & bus operators schedule data

• 93% of operators’ work hours do not create fatigue

• Only 6-7% of operators exceed the criterion

• Most fatigue risk associated with night work and significant work time in period

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100%

64%

43%

22%

11%

6%3%2%1%0%0%

0%

20%

40%

60%

80%

100%

120%

100+95908580757065605550

Cu

mu

lati

ve P

erce

nt o

f W

ork

Tim

e

Less than This Value of Effectiveness

Cumulative Work Time Effectiveness

Key Modeling Considerations

o FRM requires fatigue evaluation at every stage in scheduling process o Should be “universal”, pulling data at each stage and providing fatigue hazard

assessment against a single standard.

o Fatigue models should use realistic sleep assumptions o Sleep assumptions are just as important as the underlying physiological model.

o Models should realistically reflect how flight crew manage sleep across multiple time zones with irregular start times and variable commute times.

o The sleep pattern should be reported for evaluation against actual data.

o Fatigue models should give you more than a single score o Model ought to display and document the fatigue factors that cause the hazard

and display the verifiable benefits of mitigations.

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Key Modeling Considerations (cont.)

o The modeling process should be “transparent” to the usero Each assumption made, hazard identified, and mitigation required – should be

exposed so it can be audited and verified against an independent standard.

o Fatigue models should be implemented so the entire performance distribution is moved to the right – to higher levels of alertness o Not just elimination of outliers.

o Fatigue modeling should be part of a continuous improvement process o Process should include evaluation of performance at each stage and time point.

o Modeling should support all the other elements of the FRM.

o There is no “magic solution”

o Often judgment by the user should be applied appropriately.23

Dr. Steve Hursh

Institutes for Behavior Resources

2104 Maryland Avenue

Baltimore, MD 21218

(410) 752-6080

shursh@ibrinc.org

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Questions and Discussion:

FAST Specificity

- 1,400 FRA-reportable rail accidents: Human Factors; Non human factors

- 5 US based operators

- Modeled operator 30-day work history.

- Effectiveness at time of incident

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1.69

1.24

1.10 1.061.06

0.85

y = -0.0131x + 2.0683R² = 0.8587

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

30 40 50 60 70 80 90 100

Effectiveness Category

Human Factors Accidents

Ris

k

< 50 50-60 60-70 70-80 80-90 90-100+

Correlation Coefficient = -0.93, p < 0.01

Human Factors ≈ 400

y = -0.0017x + 1.1833R² = 0.0304

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

30 40 50 60 70 80 90 100

Effectiveness Category

Non-Human Factors Incidents

Ris

k

< 50 50-60 60-70 70-80 80-90 90-100+

NON Human Factors≈ 1000

Field Validation in Rail:Accident Cost Increases with Decreasing Estimated Performance

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$1,589,938

$771,686

$395,283

$1,266,853

$503,652

$259,692

y = 3E+06e-0.696x

R² = 0.9995

$0

$200,000

$400,000

$600,000

$800,000

$1,000,000

$1,200,000

$1,400,000

$1,600,000

$1,800,000

Less than or equal to 70 70 to 90 Greater than 90

Aver

age

Acci

dent

Cos

t ($)

Human Factors Average Cost - Damage and Casualties

Human Factors Average Casualty Cost

Average cost of all accidents = $488,681

(N=95)

(N=138)

(N=117)

4x

Hursh SR, Fanzone JF, Raslear TG. (2011). Analysis of the relationship between operator effectiveness measures and economic impacts of rail accidents (Report No. DOT/FRA/ORD-11/13). Washington, DC: U.S. Department of Transportation.

SAFTE Model Effectiveness Range