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企业层面的职业安全分析:挑战与机遇

Establishment-level occupational safety analytics: Challenges and opportunities.

作者信息

Foreman Anne M, Friedel Jonathan E, Ludwig Timothy D, Ezerins Maira E, Açikgöz Yalçin, Bergman Shawn M, Wirth Oliver

机构信息

National Institute for Occupational Safety and Health, USA.

Georgia Southern University, USA.

出版信息

Int J Ind Ergon. 2023 Mar;94. doi: 10.1016/j.ergon.2023.103428.

Abstract

In occupational safety and health, big data and analytics show promise for the prediction and prevention of workplace injuries. Advances in computing power and analytical methods have allowed companies to reveal insights from the "big" data that previously would have gone undetected. Despite the promise, occupational safety has lagged behind other industries, such as supply chain management and healthcare, in terms of exploiting the potential of analytics and much of the data collected by organizations goes unanalyzed. The purpose of the present paper is to argue for the broader application of establishment-level safety analytics. This is accomplished by defining the terms, describing previous research, outlining the necessary components required, and describing knowledge gaps and future directions. The knowledge gaps and future directions for research in establishment-level analytics are categorized into readiness for analytics, analytics methods, technology integration, data culture, and impact of analytics.

摘要

在职业安全与健康领域,大数据和分析技术在预测和预防工作场所伤害方面展现出了前景。计算能力和分析方法的进步使公司能够从以前未被发现的“大”数据中挖掘出见解。尽管前景广阔,但在利用分析潜力方面,职业安全领域落后于供应链管理和医疗保健等其他行业,组织收集的许多数据都未得到分析。本文的目的是主张在企业层面更广泛地应用安全分析。这通过定义术语、描述先前的研究、概述所需的必要组成部分以及描述知识差距和未来方向来实现。企业层面分析研究的知识差距和未来方向分为分析准备情况、分析方法、技术集成、数据文化以及分析的影响。

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本文引用的文献

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Comparison of methods for auto-coding causation of injury narratives.损伤描述因果关系自动编码方法的比较
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