Resilience, Safety and Health: Reflections About Covid-19’ Assistance

Prognostic scales may help to optimize the use of hospital resources, which may be of prime interest in the context of a fast spreading pandemics. Nonetheless, such tools are underdeveloped in the context of COVID-19. In the present article we asked whether accurate prognostic scales could be developed to optimize the use of hospital resources. We retrospectively studied 467 files of hospitalized patients after COVID-19. The odds ratios for 16 different biomarkers were calculated, those that were significantly associated were screened by a Pearson’s correlation, and such index was used to establish the mathematical function for each marker. The scales to predict the need for hospitalization, intensive-care requirement and mortality had enhanced sensitivities (0.91 CI 0.87–0.94; 0.96 CI 0.94–0.98; 0.96 CI 0.94–0.98; all with p < 0.0001) and specificities (0.74 CI 0.62–0.83; 0.92 CI 0.87–0.96 and 0.91 CI 0.86–0.94; all with p < 0.0001). Interestingly, when a different population was assayed, these parameters did not change considerably. These results show a novel approach to establish the mathematical function of a marker in the development of highly sensitive prognostic tools, which in this case, may aid in the optimization of hospital resources. An online version of the three algorithms can be found at: http://benepachuca.no-ip.org/covid/index.php

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Resilience Potentials for Health and Safety Management in Cobot Applications Using the Resilience Analysis Grid

Adriaensen

Bernabei

Costantino

et al. 2023

Journal of Manufacturing Science and Engineering

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The increasing usage of cobot applications reshapes work environments and working conditions, requiring specific advancements in organizational practices for health and safety. Enterprises should shift from a technocentric risk management approach to considering cobots application as socio-technical systems, where resilience engineering is beneficial. This study presents an instantiation of the Resilience Analysis Grid (RAG) in cobot applications with the aim of measuring resilience potentials in terms of the four cornerstones of resilience engineering (respond, learn, monitor, anticipate). The assessment has been provided via a questionnaire to fifteen companies that make use of cobot applications. Results revealed that companies mainly focus on the risk assessment of cobot applications with a traditional view of machine-centric safety, paying less attention to assessing contexts and process variables. This observation seems to arise mainly due to the lack of formally available safety methods or limited guidance from technical standards. Additionally, traditional industrial approaches to risk management lack monitoring of several risks that are essential for managing resilience, defined as the adaptive capacity of people, organizations, and human-machine systems. In addition, companies strongly rely on data from the cobot manufacturer for their safety assessment. The Resilience Analysis Grid was confirmed as a valuable assessment tool for the participating companies to identify improvement areas and assess health and safety from a resilience engineering perspective.

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Resilience, Safety and Health: Reflections About Covid-19’ Assistance

Cited by 3 publications

References 14 publications

The Resilience Assessment Grid in Day-To-Day Work

The Resilience Assessment Grid in Day-To-Day Work

A novel scale based on biomarkers associated with COVID-19 severity can predict the need for hospitalization and intensive care, as well as enhanced probabilities for mortality

Resilience Potentials for Health and Safety Management in Cobot Applications Using the Resilience Analysis Grid

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