Designing Accountable Health Care Algorithms: Lessons from Covid-19 Contact Tracing

AI THEME ISSUE: How can health care organizations ensure that there is accountability of algorithms for accuracy, bias, and the wide range of unintended consequences when deployed in real-world settings? A machine-learning system for Covid-19 contact tracing serves as a model to scope out, develop,...

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Autores principales: Lu, Lisa, D’Agostino, Alexis, Rudman, Sarah L., Ouyang, Derek, Ho, Daniel E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Massachusetts Medical Society 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9576145/
http://dx.doi.org/10.1056/CAT.21.0382
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author Lu, Lisa
D’Agostino, Alexis
Rudman, Sarah L.
Ouyang, Derek
Ho, Daniel E.
author_facet Lu, Lisa
D’Agostino, Alexis
Rudman, Sarah L.
Ouyang, Derek
Ho, Daniel E.
author_sort Lu, Lisa
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description AI THEME ISSUE: How can health care organizations ensure that there is accountability of algorithms for accuracy, bias, and the wide range of unintended consequences when deployed in real-world settings? A machine-learning system for Covid-19 contact tracing serves as a model to scope out, develop, interrogate, and assess an algorithmic solution that produces improvements in care, mitigates risk, and enables evaluation by many stakeholders.
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spelling pubmed-95761452022-10-20 Designing Accountable Health Care Algorithms: Lessons from Covid-19 Contact Tracing Lu, Lisa D’Agostino, Alexis Rudman, Sarah L. Ouyang, Derek Ho, Daniel E. NEJM Catal Innov Care Deliv Article AI THEME ISSUE: How can health care organizations ensure that there is accountability of algorithms for accuracy, bias, and the wide range of unintended consequences when deployed in real-world settings? A machine-learning system for Covid-19 contact tracing serves as a model to scope out, develop, interrogate, and assess an algorithmic solution that produces improvements in care, mitigates risk, and enables evaluation by many stakeholders. Massachusetts Medical Society 2022-03-16 /pmc/articles/PMC9576145/ http://dx.doi.org/10.1056/CAT.21.0382 Text en Copyright © 2022 Massachusetts Medical Society. This article is made available via the PMC Open Access Subset for unrestricted re-use, except commercial resale, and analyses in any form or by any means with acknowledgment of the original source. PMC is granted a license to make this article available via PMC and Europe PMC, subject to existing copyright protections.
spellingShingle Article
Lu, Lisa
D’Agostino, Alexis
Rudman, Sarah L.
Ouyang, Derek
Ho, Daniel E.
Designing Accountable Health Care Algorithms: Lessons from Covid-19 Contact Tracing
title Designing Accountable Health Care Algorithms: Lessons from Covid-19 Contact Tracing
title_full Designing Accountable Health Care Algorithms: Lessons from Covid-19 Contact Tracing
title_fullStr Designing Accountable Health Care Algorithms: Lessons from Covid-19 Contact Tracing
title_full_unstemmed Designing Accountable Health Care Algorithms: Lessons from Covid-19 Contact Tracing
title_short Designing Accountable Health Care Algorithms: Lessons from Covid-19 Contact Tracing
title_sort designing accountable health care algorithms: lessons from covid-19 contact tracing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9576145/
http://dx.doi.org/10.1056/CAT.21.0382
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