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. 2024 Jun;30(2):559-570.
doi: 10.1080/10803548.2024.2325301. Epub 2024 Apr 5.

Establishment-level safety analytics: a scoping review

Anne M Foreman  1 , Jonathan E Friedel  2 , Maira E Ezerins  3 , Riggs Matthews  4 , Royale E Nicholson  4 , Logan Wellersdick  4 , Shawn Bergman  4 , Yalcin Açıkgöz  4 , Timothy D Ludwig  4 , Oliver Wirth  1
Affiliations

Establishment-level safety analytics: a scoping review

Anne M Foreman et al. Int J Occup Saf Ergon. 2024 Jun.

Abstract

The use of data analytics has seen widespread application in fields such as medicine and supply chain management, but their application in occupational safety has only recently become more common. The purpose of this scoping review was to summarize studies that employed analytics within establishments to reveal insights about work-related injuries or fatalities. Over 300 articles were reviewed to survey the objectives, scope and methods used in this emerging field. We conclude that the promise of analytics for providing actionable insights to address occupational safety concerns is still in its infancy. Our review shows that most articles were focused on method development and validation, including studies that tested novel methods or compared the utility of multiple methods. Many of the studies cited various challenges in overcoming barriers caused by inadequate or inefficient technical infrastructures and unsupportive data cultures that threaten the accuracy and quality of insights revealed by the analytics.

Keywords: data analytics; data mining; injuries; occupational safety.

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Conflict of interest statement

Disclosure statement

No potential conflict of interest was reported by the authors.

Figures

Figure 1.
Figure 1.
Number of Web of Science search results from 2007 to 2022 for the search ‘safety analytics’ in the article title, abstract and key words.
Figure 2.
Figure 2.
Flow diagram for the scoping review study selection process. The n denotes the number of articles under consideration in each step of the process.
Figure 3.
Figure 3.
Frequency counts: (A) year of publication; (B) country where the research was conducted; (C) sector in which the research was conducted; (D) industry in which the research was conducted if the sector was manufacturing.
Figure 4.
Figure 4.
Most frequently analyzed variables for (A) employee characteristics and (B) incident details in the included studies.
Figure 5.
Figure 5.
Summary data for different aspects of data preprocessing: (A) methods employed for variable selection; (B) missing data; (C) text-mining preprocessing; (D) software used for the included studies. Note: TF-IDF = term frequency-inverse document frequency.
Figure 6.
Figure 6.
Techniques employed by studies that used only one technique (in contrast to the studies that compared two or more techniques).
See this image and copyright information in PMC

References

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