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A qualitative study of big data and the opioid epidemic: recommendations for data governance
BACKGROUND: The opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significa...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7576981/ https://www.ncbi.nlm.nih.gov/pubmed/33087123 http://dx.doi.org/10.1186/s12910-020-00544-9 |
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author | Evans, Elizabeth A. Delorme, Elizabeth Cyr, Karl Goldstein, Daniel M. |
author_facet | Evans, Elizabeth A. Delorme, Elizabeth Cyr, Karl Goldstein, Daniel M. |
author_sort | Evans, Elizabeth A. |
collection | PubMed |
description | BACKGROUND: The opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms. OBJECTIVES: We aimed to identify concerns and recommendations regarding how to use big data on opioid use in ethical ways. METHODS: We conducted focus groups and interviews in 2019 with 39 big data stakeholders (gatekeepers, researchers, patient advocates) who had interest in or knowledge of the Public Health Data Warehouse maintained by the Massachusetts Department of Public Health. RESULTS: Concerns regarding big data on opioid use are rooted in potential privacy infringements due to linkage of previously distinct data systems, increased profiling and surveillance capabilities, limitless lifespan, and lack of explicit informed consent. Also problematic is the inability of affected groups to control how big data are used, the potential of big data to increase stigmatization and discrimination of those affected despite data anonymization, and uses that ignore or perpetuate biases. Participants support big data processes that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. Recommendations for ethical big data governance offer ways to narrow the big data divide (e.g., prioritize health equity, set off-limits topics/methods, recognize blind spots), enact shared data governance (e.g., establish community advisory boards), cultivate public trust and earn social license for big data uses (e.g., institute safeguards and other stewardship responsibilities, engage the public, communicate the greater good), and refocus ethical approaches. CONCLUSIONS: Using big data to address the opioid epidemic poses ethical concerns which, if unaddressed, may undermine its benefits. Findings can inform guidelines on how to conduct ethical big data governance and in ways that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. |
format | Online Article Text |
id | pubmed-7576981 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75769812020-10-22 A qualitative study of big data and the opioid epidemic: recommendations for data governance Evans, Elizabeth A. Delorme, Elizabeth Cyr, Karl Goldstein, Daniel M. BMC Med Ethics Research Article BACKGROUND: The opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms. OBJECTIVES: We aimed to identify concerns and recommendations regarding how to use big data on opioid use in ethical ways. METHODS: We conducted focus groups and interviews in 2019 with 39 big data stakeholders (gatekeepers, researchers, patient advocates) who had interest in or knowledge of the Public Health Data Warehouse maintained by the Massachusetts Department of Public Health. RESULTS: Concerns regarding big data on opioid use are rooted in potential privacy infringements due to linkage of previously distinct data systems, increased profiling and surveillance capabilities, limitless lifespan, and lack of explicit informed consent. Also problematic is the inability of affected groups to control how big data are used, the potential of big data to increase stigmatization and discrimination of those affected despite data anonymization, and uses that ignore or perpetuate biases. Participants support big data processes that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. Recommendations for ethical big data governance offer ways to narrow the big data divide (e.g., prioritize health equity, set off-limits topics/methods, recognize blind spots), enact shared data governance (e.g., establish community advisory boards), cultivate public trust and earn social license for big data uses (e.g., institute safeguards and other stewardship responsibilities, engage the public, communicate the greater good), and refocus ethical approaches. CONCLUSIONS: Using big data to address the opioid epidemic poses ethical concerns which, if unaddressed, may undermine its benefits. Findings can inform guidelines on how to conduct ethical big data governance and in ways that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. BioMed Central 2020-10-21 /pmc/articles/PMC7576981/ /pubmed/33087123 http://dx.doi.org/10.1186/s12910-020-00544-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Evans, Elizabeth A. Delorme, Elizabeth Cyr, Karl Goldstein, Daniel M. A qualitative study of big data and the opioid epidemic: recommendations for data governance |
title | A qualitative study of big data and the opioid epidemic: recommendations for data governance |
title_full | A qualitative study of big data and the opioid epidemic: recommendations for data governance |
title_fullStr | A qualitative study of big data and the opioid epidemic: recommendations for data governance |
title_full_unstemmed | A qualitative study of big data and the opioid epidemic: recommendations for data governance |
title_short | A qualitative study of big data and the opioid epidemic: recommendations for data governance |
title_sort | qualitative study of big data and the opioid epidemic: recommendations for data governance |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7576981/ https://www.ncbi.nlm.nih.gov/pubmed/33087123 http://dx.doi.org/10.1186/s12910-020-00544-9 |
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