Nina McGrath Group Safety Manager - FRMS, Cathay Pacific – on behalf of the IATA FRMS Task Force Identifying Common Safety Performance Indicators (SPIs) Fatigue Management Approaches Symposium 5-6 April 2016, Montréal, Canada
Identifying Common Safety Performance Indicators … › Meetings › fmas › Documents...Fatigue SPIs 4 Operational Survey (DCP) Study (DCP) (DCP – Data Collection Protocol) The
Nina McGrath Group Safety Manager - FRMS, Cathay Pacific – on behalf of the IATA FRMS Task Force
Identifying Common Safety Performance Indicators (SPIs)
Fatigue Management Approaches Symposium 5-6 April 2016, Montréal, Canada
The IATA FRMS Taskforce Consists of key IATA members with experience in FRM.
FRMS TF Objectives: – Provide a forum for exchange of fatigue management information.
– Assist Regulators.
– Develop new tools and support for Operators.
– Develop further guidance.
2
Presenter
Presentation Notes
The IATA FRMS Taskforce was formed in September 2011 and consists of key IATA members with experience in fatigue risk management. Objectives of the FRMS TF are to: Provide a forum for the communication and exchange of global fatigue management activities. Assist regulators with global alignment of FRMS. Develop new tools and support for operators who wish to implement an FRMS. Develop further guidance as knowledge and implementation continues.
Fatigue Safety Performance Indicators (SPIs): A Key Component of Proactive Fatigue Hazard Identification
• FRMS is data driven
• What data should operators: – Collect?
– Analyse?
– Monitor? 3
Presenter
Presentation Notes
One of the tools for operators who wish to implement an FRMS is the IATA FRMS TF paper ‘Fatigue Safety Performance Indicators (SPIs): A Key Component of Proactive Fatigue Hazard Identification’ which contains information on safety performance indicators (SPIs), metrics and data collection protocols. An FRMS is partially defined as a ‘data-driven means of continuously monitoring and managing fatigue related safety risks’. However many operators are unsure about what data they should be collecting to identify and monitor fatigue related safety risks. This document details operational, survey and study metrics that can be collected to to identify fatigue hazards in the operation for risk assessment, mitigation and monitoring.
Fatigue SPIs
4
Operational
Survey (DCP)
Study (DCP)
(DCP – Data Collection Protocol)
Presenter
Presentation Notes
The paper contains information for operators at any stage of FRMS implementation and development. Starting with operational objective SPIs, every operator here today could generate Roster metrics based on data in the rostering software. Those with a fatigue report in place can generate Fatigue reporting metrics, based on data from fatigue reports from crew members. Those with a fatigue model can generate Fatigue Model Metrics based on fatigue modelling software outputs from modelling a flight, pattern, roster or rosters. For those operators wishing to conduct a subjective survey, the paper contains information on data collection protocols and Subjective Fatigue Survey Metrics which can be generated from data gathered from surveying crew members about fatigue. For operators conducting a fatigue study, the paper contains information on data collection protocols and the objective and subjective metrics which can be generated. We will now look at each of these in more detail.
Fatigue SPIs – Operational • Objective Metrics
– Roster Metrics
– Fatigue Reporting Metrics
– Fatigue Model Metrics
5
Presenter
Presentation Notes
Operational Objective Metrics Roster metrics generated based on data in the rostering software. Examples of roster metrics include: • Number of extended flight duty periods for a specific pairing • Number of minimum rest periods per month as a percentage of all rest periods • Percentage of sectors on which controlled rest was used • Number of unfit for duty due to fatigue per roster as a percentage of all duties Fatigue reporting metrics, are generated based on data from fatigue reports submitted by crew members. Examples of fatigue reporting metrics include: Number of FRFs submitted, expressed as a percentage of crew sectors Breakdown of FRFs by reason for submission (e.g., adverse event, unfit for duty due to fatigue, controlled rest, general fatigue concern) Breakdown of FRFs by sector Fatigue Model Metrics are generated based on fatigue modelling software outputs for a flight, pattern, roster or rosters. The specific metrics available will depend on the fatigue model in use, but all models involve setting a threshold and focusing on operations predicted to be outside the threshold. Potential metrics include: • Percentage of all duties outside the threshold • Percentage of critical phases of flight outside the threshold e.g., take off, approach, and landing • Ranking of sectors by predicted fatigue outside the threshold • Identifying common factors for sectors with predicted fatigue outside the threshold
Fatigue SPIs - Survey • Subjective Metrics
– Fatigue Survey Metrics
– Typical Surveys
6
Presenter
Presentation Notes
Subjective Survey Metrics Fatigue Survey Metrics are gathered by surveying crew members about fatigue. Typical fatigue-related surveys include: • Demographic questions (What is your age, gender?) • Questions about perceived fatigue (e.g., How fatigued do you feel after a day at work? Do you see co-workers that are fatigued?) • Fatigue awareness questions (e.g., What techniques are most effective for managing fatigue?) • Population – specific questions (e.g., Where do you live and work? How long is your daily commute?) • Questions about habit and lifestyle (e.g., How many hours do you typically sleep each night?) • Questions about perceived problems (e.g., At what time of day are you typically most challenged by fatigue?)
Fatigue SPIs - Study
7
Objective Metrics • Performance Metrics
‒ Psychomotor Vigilance Test (PVT)
• Sleep Metrics ‒ Actigraphy
Subjective Metrics • Alertness/Sleepiness Metrics
‒ Samn-Perelli
‒ Karolinska Sleepiness Scale
• Sleep/Wake Diary Metrics
Presenter
Presentation Notes
When conducting fatigue studies both objective and subjective data can be collected. Objective Performance Metrics a measurement of crew performance at a given moment in time. E.g. The Psychomotor Vigilance Test (PVT). Sleep Metrics a measurement of crew sleep/wake over a specified pattern. E.g. using actigraphy; a wristwatch-like device which measures movement to establish when sleep occurs. Statistics that can be obtained include: • Sleep and wakefulness duration, timing and quality. • In-flight sleep: in-flight rest schedule (duration and timing of breaks), and environment • Self-rated sleepiness • Crew performance: average response time for all trials on PVT Subjective Alertness/Sleepiness Metrics gathered from crew reporting their fatigue, sleepiness or alertness at a given moment in time. For example, individuals are asked to rate their alertness/sleepiness at sign-on/check-in/report for duty, TOC, before and after each sleep period, TOD and sign-off/check-out/end of duty. Validated tools for assessing subjective alertness/sleepiness include: The Karolinska Sleepiness Scale (KSS) The Samn-Perelli scale Sleep/Wake Diary Metrics are gathered from crew recording their sleep/wake over a specified pattern. This could include crew ratings of: sleepiness and alertness before and after sleep periods, sleep quality, and the sources of any sleep disturbances.
Data Collection Protocols • Subjective Survey
• PDA Study
• Top of Descent (TOD) Study
• Daily Diary Study
8
Presenter
Presentation Notes
For operators who wish to conduct a survey or a study to proactively identify fatigue-related safety risks, the paper contains not only the objective and subjective metrics but also data collection protocols for various types of survey or study. This includes: Subjective Survey: As using a standardised fatigue survey allows comparison of results between/across operations and with other surveys. PDA Study: PDA stands for Psychomotor Vigilance Test, diary and actigraph. The “gold standard” for Fatigue Hazard Identification studies is one which combines subjective data capture with objective measures of sleep and performance. The use of subjective estimates of alertness and sleepiness in combination with objective performance measurement (PVT) and sleep measurement (actigraphy) is a validated methodology for assessing potential fatigue risk in aviation operations. Top of Descent (TOD) Study: In a TOD study subjects rate their fatigue at TOD. Additional data collected on a TOD study could include rating fatigue at start of duty to enable fatigue acceleration/deceleration calculations (i.e. difference between start of duty and TOD fatigue ratings). Daily Diary Study: Diary studies are generally designed to collect information about work schedules, sleep schedules, and alertness and fatigue ratings. Daily diary studies can provide insights into the actual work habits and sleep schedules of individuals, and can provide a basis for modifying schedules, providing training, or implementing other interventions. Information on in-flight rest and sleep periods can also be collected with the daily diary.
IATA FRMS TF - Publications For more information please refer to ‘Fatigue Safety Performance Indicators (SPIs): A Key Component of Proactive Fatigue Hazard Identification’
Other IATA FRMS TF Publications: • FRMS White Paper
• CASA Biomathematical Fatigue Models Guidance Document Summary
• Common Protocol for Minimum Data Collection Variables in Aviation Operations 9
Presenter
Presentation Notes
For further information please refer to ‘Fatigue Safety Performance Indicators (SPIs): A Key Component of Proactive Fatigue Hazard Identification’ The FRMS TF has published three additional papers: The FRMS White Paper CASA Biomathematical Fatigue Models Guidance Document Summary Common Protocol for Minimum Data Collection Variables in Aviation Operations
• Fatigue SPIs: A Key Component of Proactive Fatigue Hazard Identification: http://www.iata.org/whatwedo/ops-infra/Documents/fatigue-spis_a-key-component-of-proactive-fatigue-hazard-identification.pdf
• CASA Biomathematical Fatigue Models Guidance Document Summary: http://www.iata.org/whatwedo/ops-infra/Documents/condensed-version-of-CASA-biomathematical-models-doc.pdf
• Common Protocol for Minimum Data Collection Variables in Aviation Operations: http://www.iata.org/whatwedo/ops-infra/Documents/Common-Protocol-for-Minimum-Data-Collection-Variables-in-Aviation-Ops.pdf
