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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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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. |
format | Online Article Text |
id | pubmed-8809228 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
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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