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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Detalles Bibliográficos
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
Descripción
Sumario: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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