Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Evans, Elizabeth A., Delorme, Elizabeth, Cyr, Karl, Goldstein, Daniel M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
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
_version_ 1783598121051226112
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
work_keys_str_mv AT evanselizabetha aqualitativestudyofbigdataandtheopioidepidemicrecommendationsfordatagovernance
AT delormeelizabeth aqualitativestudyofbigdataandtheopioidepidemicrecommendationsfordatagovernance
AT cyrkarl aqualitativestudyofbigdataandtheopioidepidemicrecommendationsfordatagovernance
AT goldsteindanielm aqualitativestudyofbigdataandtheopioidepidemicrecommendationsfordatagovernance
AT evanselizabetha qualitativestudyofbigdataandtheopioidepidemicrecommendationsfordatagovernance
AT delormeelizabeth qualitativestudyofbigdataandtheopioidepidemicrecommendationsfordatagovernance
AT cyrkarl qualitativestudyofbigdataandtheopioidepidemicrecommendationsfordatagovernance
AT goldsteindanielm qualitativestudyofbigdataandtheopioidepidemicrecommendationsfordatagovernance