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'Leading by Science' through Covid-19: the NHS Data Store & Automated Decision-Making.

Authors :
Mourby MJ
Source :
International journal of population data science [Int J Popul Data Sci] 2021 Apr 07; Vol. 5 (4), pp. 1099. Date of Electronic Publication: 2021 Apr 07.
Publication Year :
2021

Abstract

The UK government announced in March 2020 that it would create an NHS Covid-19 'Data Store' from information routinely collected as part of the health service. This 'Store' would use a number of sources of population data to provide a 'single source of truth' about the spread of the coronavirus in England. The initiative illustrates the difficulty of relying on automated processing when making healthcare decisions under the General Data Protection Regulation (GDPR). The end-product of the store, a number of 'dashboards' for decision-makers, was intended to include models and simulations developed through artificial intelligence. Decisions made on the basis of these dashboards would be significant, even (it was suggested) to the point of diverting patients and critical resources between hospitals based on their predictions. How these models will be developed, and externally validated, remains unclear. This is an issue if they are intended to be used for decisions which will affect patients so directly and acutely. We have (by default) a right under the GDPR not to be subject to significant decisions based solely on automated decision-making. It is not obvious, at present, whether resource allocation within the NHS could take place in reliance on this automated modelling. The recent A Level debacle illustrates, in the context of education, the risks of basing life-changing decisions on the national application of a single equation. It is worth considering the potential consequences for the health service if the NHS Data Store is used for resource planning as part of the Covid-19 response.<br />Competing Interests: Conflicts of interest: I am not aware of any conflicts of interest relating to the content of this piece.

Details

Language :
English
ISSN :
2399-4908
Volume :
5
Issue :
4
Database :
MEDLINE
Journal :
International journal of population data science
Publication Type :
Academic Journal
Accession number :
34164583
Full Text :
https://doi.org/10.23889/ijpds.v5i4.1402