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SAFTE/FAST Evidence-based Aviation Fatigue Risk Management

SAFTE/FAST Evidence-based Aviation Fatigue Risk Management

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SAFTE/FAST Evidence-based Aviation Fatigue Risk Management

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  1. SAFTE/FASTEvidence-based Aviation Fatigue Risk Management Steven R. Hursh, Ph.D. President, IBR and Professor, Johns Hopkins University School of Medicine September 1, 2011

  2. Major Fatigue Factors • 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.

  3. An Objective Fatigue Metric • No Blood Test for fatigue, yet • The conditions that lead to fatigue are well known. • A fatigue model simulates the specific conditions and determines if fatigue could be present. • The model can estimate the level of degradation in performance and provide an estimate of schedule induced fatigue risk.

  4. SAFTE • The Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) Model is based on 12 years of fatigue modeling experience. • Validated against laboratory and simulator measures of fatigue. • Validated and calibrated to predict accident risk by the Department of Transportation. • Peer reviewed and found to have the least error of any available fatigue model. • Accepted by the US DOD (Air Force, Army, Navy, Marines) as the common warfighter fatigue model.

  5. SAFTE Model Components

  6. Least Error for Conditions of Sleep Restriction 2002 Seattle Fatigue & Performance Modeling Workshop, of all models tested against laboratory measurements: • SAFTE had least error predicting objective vigilance performance. • SAFTE had least error predicting subjective ratings of fatigue. • Advances since 2002 have further validated the sleep and performance assumptions and prediction of accident risk and severity. Von Dongen, Aviation, Space, and Environmental Medicine, March, 2004, vol. 75, no. 3, section II

  7. Accuracy of Predicting Sleep Pattern and Duration

  8. Economic Risk and Effectiveness Railroad Accident Relative Risk No Fatigue High Fatigue

  9. Validated Fatigue Modeling Tools SAFTE/FAST Fatigue Science has the exclusive license from the US Army to commercialize SAFTE model.

  10. Practical Software for Implementation • Fatigue Avoidance Scheduling Tool (FAST) • Fatigue assessment tool using the SAFTE model • Developed for the US Air Force and the US Army • DOT / FRA sponsored work has lead to enhancements for transportation applications • FAST Features • Sleep estimation algorithm • Graphical analysis tools • Dashboard of fatigue factors • Data based of all effectiveness scores

  11. FAST Aviation Sleep Estimation • Accurate estimation of sleep is critical: • Measure: actigraphy or log books • Estimate: algorithm to simulate sleep behavior • Aviation specific estimates that can be refined with actrigraphic measurement. • Considers time zone changes and is valid for any city pair.

  12. Sleep Estimator Tailored to Aviation Environment:

  13. FAST Aviation Specific AutoSleep • Mimics typical sleep patterns • Tailored to workgroup and schedule demands • Considers total duty period and commuting • Naps prior to anticipated late starts • Considers time zones • Slits sleep when appropriate • Automatically inserts in-flight sleep for augmented crews. User defined parameters: • amount of augmentation and • quality of sleep environment • Adjustable settings can be saved to file

  14. FRM Steering Committee Involves all stakeholders at each stage: management, labor, aided by science Fatigue Risk Management System Continuous Improvement Process Measure Define the situation Schedule evaluation Actigraph recordings Monitor Assess operational indicators Individual self-evaluation Feedback to process Model & Analyze Model the fatigue problem Analyze sources and Fatigue factors SAFTE/FAST Enablers Employee training Medical screening Economic analysis Technology aids Manage Collaborate for solutions Obtain commitment to solve problem • Modify/Mitigate • Shared Responsibility • Operating practices • Labor agreements • Individual “life style”

  15. FAST Aviation Fatigue Assessment Process Airline Specific Schedule Database XML format City-pairs/Trips or 30-day Bids Translation Tools available for any scheduling system Standard FAST schedule is created by FAST Aviation Modeler FAST Aviation Modeler FAST Aviation Manager FAST Analyzer Individual Schedules • Examine schedules • Effectiveness Graph • Fatigue Factors • “What-If” Drills • Individual reports • Aviation AutoSleep • SAFTE Model • Output results to folder • Links to Manager • Sorts by Criterion • Displays results • Links to Analyzer • Fleet level reports Modular Process for Speed and Flexibility

  16. FAST Aviation Modeler • Model the schedules: • Set AutoSleep parameters if necessary • Name the Output folder a unique name • Choose either a City Pairs file or a Bid Schedules file for modeling Airline Schedule Database Processed Schedules Modeling Results

  17. FAST Aviation Manager • Sort by: • Flight Time Below Criterion Level (FTBCL) • Critical TBCL (30 min associated with takeoff/landing) • Average Effectiveness • Minimum Effectiveness overall • Minimum Effectiveness during critical periods • Maximum Workload (high workload score in schedule) • Median Workload (central score across schedule) • Save table to text file Click on any line in Aviation Manager and schedule opens in FAST for detailed analysis.

  18. If fatigue is present, what do you do about it? • Modeling tools must do more than give you a fatigue score: • It must estimate fatigue risk • It must show detail of each schedule • It must calculate fatigue factors • It must suggest conditions that lead to fatigue so mitigations can be implemented by an FRMS

  19. Detailed Analysis Results • Creates detailed database that shows: • All duty periods and estimated sleep intervals • Effectiveness in each half hour of each duty period • Effectiveness at each half hour of the clock • Distribution of duty time in effectiveness categories • Allows results be sorted based on user defined categories • Individual ID reports with effectiveness at the 1 min resolution

  20. FAST Aviation Analyzer San Francisco to Sydney Pairing Dashboard with Fatigue Factors Sleep Timing based on both physiological and social cues 14.5 hr flight Pre-flight nap In-flight sleep Pre-flight nap Schedules in Aviation Manager link to FAST for detailed analysis.

  21. Flags are fatigue indicators Value at point in schedule Criteria Dashboard Information Content based on fatigue analysis workshop hosted by NTSB and conducted by Drs. Mark Rosekind & David Dinges, funded by FRA Office of Safety. • Sleep (last 24 hrs) • Chronic Sleep Debt • Hours Awake • Time of Day • Out of Phase • Performance Values • Effectiveness (vigilance) • Mean Cognitive • Lapse Index • Reaction Time • Reservoir

  22. Schedule Files for Evaluation • Translated the spread sheets using Access database into the required XML file structure. • Batch processed through FAST Aviation • Used FAST Manager to rank order Pairings and Rosters • Used output spread sheet to rank order segments

  23. Fatigue Metrics • Typically, FAST is used to assess the “tail of the distribution” – how much critical duty time is spent at low effectiveness. • For this exercise, we were asked to rank order all segments and schedules, not just the extreme cases. • We rank ordered segments by minimum effectiveness at critical times of flight – take-offs and landings. • We rank ordered pairings and rosters by minimum effectivenessand “critical time below criterion” which is more useful for entire schedules.

  24. SAFTE/FAST - Segment Analysis

  25. SAFTE/FAST - Roster Analysis

  26. Minimum Critical Time Effectiveness Segments

  27. Short Haul PairingA 10040 LAX to ATL Segment Early Start Daytime Rest Night flight

  28. LH Pairing - C 10027 Anchorage-LAX Segment 86% reservoir 5 hrs

  29. LH Pairing - C 10014 – Narita Segment Narita

  30. Short Haul Pairing - A 10002 Santa Cruz, Bolivia – Santiago, Chile 1.5 hr Nap

  31. A 10002, continued Santa Cruz, Bolivia – Santiago, ChilePossible Mitigation 3 hr Nap

  32. Long Haul Pairing - C 10014 Honolulu to Salt Lake 93% Res 3:15 Base Narita Honolulu Salt Lake

  33. C 10014 Honolulu to Salt LakeAltered Sleep Pattern Altered Sleep Pattern

  34. Long Haul PairingC 10033 Kuala Lumpur Based Pilot

  35. Greatest Time Below Criterion Rosters

  36. Long Haul Roster - D 10029 11 Day - Closer Examination

  37. D 10029 Detroit– Narita- Guam Segment

  38. Long Haul RosterD 10029 Guam – Narita Segment

  39. Short Haul Roster - B 10049 92 Segments in 57 Days25th Ranking Workload of 56

  40. B 10049Early Start on 12/19

  41. B 10049 Multiple Segments at Nighton 12/21 starting 1730 to 0510

  42. B 10049Multiple Segments at Night

  43. Long Haul Roster - D 10024 Salt Lake-Anchorage-Minn

  44. D 10024 Six Day SeriesSalt Lake-Anchorage-Minn

  45. D 10024 Salt Lake-Anchorage-MinnExplanation Two Early Starts (0700 and 0500) Two Consecutive Night Flights (2235 and 0020) Daytime Recovery 6 hrs 5 hrs

  46. Workload Factor • According to the NTSB: “One factor that contributes to self-reported pilot fatigue, especially in short-haul flight operations, is the number of legs flown in a duty period.”* • The highest workload in a flight occurs at take-off and landing; increasing segments multiplies these high stress periods. • FAST Aviation is the first fatigue assessment tool to provide an automated method to assess this source of fatigue. *NTSB Safety Recommendation, A-09-61 through -66, August 7, 2009

  47. Sample Workload Pattern Maximum Workload increases with segments Workload dissipates over time Median

  48. B 10007Top Ranking Workload58 Segments in 12 Days

  49. Advantages of Modeling Approach • Validated model with history of outstanding performance under independent review. • Explicit Sleep Estimator (AutoSleep) tailored to habits and policies of the airline. • Aviation specific drivers of fatigue. • Cognitive fatigue • Workload related fatigue • Analysis tools that lead to specific fatigue factors and mitigation approaches. • Modular design can be tailored to customers needs.

  50. Summary