How effective are Fatigue Risk Management Systems (FRMS)? A review
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How effective are Fatigue Risk Management Systems (FRMS)? A review

Filetype[PDF-729.33 KB]


  • English

  • Details:

    • Alternative Title:
      Accid Anal Prev
    • Description:
      Objective:

      Fatigue Risk Management Systems (FRMS) are a data-driven set of management practices for identifying and managing fatigue-related safety risks. This approach also considers sleep and work time, and is based on ongoing risk assessment and monitoring. This narrative review addresses the effectiveness of FRMS, as well as barriers and enablers in the implementation of FRMS. Furthermore, this review draws on the literature to provide evidence-based policy guidance regarding FRMS implementation.

      Methods:

      Seven databases were drawn on to identify relevant peer-reviewed literature. Relevant grey literature was also reviewed based on the authors’ experience in the area. In total, 2129 records were screened based on the search strategy, with 231 included in the final review.

      Results:

      Few studies provide an evidence-base for the effectiveness of FRMS as a whole. However, FRMS components (e.g., bio-mathematical models, self-report measures, performance monitoring) have improved key safety and fatigue metrics. This suggests FRMS as a whole are likely to have positive safety outcomes. Key enablers of successful implementation of FRMS include organisational and worker commitment, workplace culture, and training.

      Conclusions:

      While FRMS are likely to be effective, in organisations where safety cultures are insufficiently mature and resources are less available, these systems may be challenging to implement successfully. We propose regulatory bodies consider a hybrid model of FRMS, where organisations could choose to align with tight hours of work (compliance) controls. Alternatively, where organisational flexibility is desired, a risk-based approach to fatigue management could be implemented.

    • Pubmed ID:
      34756484
    • Pubmed Central ID:
      PMC8806333
    • Document Type:
    • Collection(s):
    • Main Document Checksum:
    • File Type:

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