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Workplace health surveillance and COVID-19: algorithmic health discrimination and cancer survivors

PURPOSE: This article examines ways COVID-19 health surveillance and algorithmic decision-making (“ADM”) are creating and exacerbating workplace inequalities that impact post-treatment cancer survivors. Cancer survivors’ ability to exercise their right to work often is limited by prejudice and healt...

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Autores principales: Harpur, Paul, Hyseni, Fitore, Blanck, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8809228/
https://www.ncbi.nlm.nih.gov/pubmed/35107794
http://dx.doi.org/10.1007/s11764-021-01144-1
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author Harpur, Paul
Hyseni, Fitore
Blanck, Peter
author_facet Harpur, Paul
Hyseni, Fitore
Blanck, Peter
author_sort Harpur, Paul
collection PubMed
description PURPOSE: This article examines ways COVID-19 health surveillance and algorithmic decision-making (“ADM”) are creating and exacerbating workplace inequalities that impact post-treatment cancer survivors. Cancer survivors’ ability to exercise their right to work often is limited by prejudice and health concerns. While cancer survivors can ostensibly elect not to disclose to their employers when they are receiving treatments or if they have a history of treatment, the use of ADM increases the chances that employers will learn of their situation regardless of their preferences. Moreover, absent significant change, inequalities may persist or even expand. METHODS: We analyze how COVID-19 health surveillance is creating an unprecedented amount of health data on all people. These data are increasingly collected and used by employers as part of COVID-19 regulatory interventions. RESULTS: The increase in data, combined with the health and economic crisis, means algorithm-driven health inequalities will be experienced by a larger percentage of the population. Post-treatment cancer survivors, as for people with disabilities generally, are at greater risk of experiencing negative outcomes from algorithmic health discrimination. CONCLUSIONS: Updated and revised workplace policy and practice requirements, as well as collaboration across impacted groups, are critical in helping to control the inequalities that flow from the interaction between COVID-19, ADM, and the experience of cancer survivorship in the workplace. IMPLICATIONS FOR CANCER SURVIVORS: The interaction among COVID-19, health surveillance, and ADM increases exposure to algorithmic health discrimination in the workplace.
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spelling pubmed-88092282022-02-02 Workplace health surveillance and COVID-19: algorithmic health discrimination and cancer survivors Harpur, Paul Hyseni, Fitore Blanck, Peter J Cancer Surviv Policy Papers PURPOSE: This article examines ways COVID-19 health surveillance and algorithmic decision-making (“ADM”) are creating and exacerbating workplace inequalities that impact post-treatment cancer survivors. Cancer survivors’ ability to exercise their right to work often is limited by prejudice and health concerns. While cancer survivors can ostensibly elect not to disclose to their employers when they are receiving treatments or if they have a history of treatment, the use of ADM increases the chances that employers will learn of their situation regardless of their preferences. Moreover, absent significant change, inequalities may persist or even expand. METHODS: We analyze how COVID-19 health surveillance is creating an unprecedented amount of health data on all people. These data are increasingly collected and used by employers as part of COVID-19 regulatory interventions. RESULTS: The increase in data, combined with the health and economic crisis, means algorithm-driven health inequalities will be experienced by a larger percentage of the population. Post-treatment cancer survivors, as for people with disabilities generally, are at greater risk of experiencing negative outcomes from algorithmic health discrimination. CONCLUSIONS: Updated and revised workplace policy and practice requirements, as well as collaboration across impacted groups, are critical in helping to control the inequalities that flow from the interaction between COVID-19, ADM, and the experience of cancer survivorship in the workplace. IMPLICATIONS FOR CANCER SURVIVORS: The interaction among COVID-19, health surveillance, and ADM increases exposure to algorithmic health discrimination in the workplace. Springer US 2022-02-02 2022 /pmc/articles/PMC8809228/ /pubmed/35107794 http://dx.doi.org/10.1007/s11764-021-01144-1 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Policy Papers
Harpur, Paul
Hyseni, Fitore
Blanck, Peter
Workplace health surveillance and COVID-19: algorithmic health discrimination and cancer survivors
title Workplace health surveillance and COVID-19: algorithmic health discrimination and cancer survivors
title_full Workplace health surveillance and COVID-19: algorithmic health discrimination and cancer survivors
title_fullStr Workplace health surveillance and COVID-19: algorithmic health discrimination and cancer survivors
title_full_unstemmed Workplace health surveillance and COVID-19: algorithmic health discrimination and cancer survivors
title_short Workplace health surveillance and COVID-19: algorithmic health discrimination and cancer survivors
title_sort workplace health surveillance and covid-19: algorithmic health discrimination and cancer survivors
topic Policy Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8809228/
https://www.ncbi.nlm.nih.gov/pubmed/35107794
http://dx.doi.org/10.1007/s11764-021-01144-1
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