Open Access‘Leading by Science’ through Covid-19: the GDPR & Automated Decision-Making
Author(s) -
Miranda Mourby
Publication year - 2021
Publication title -
international journal of population data science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.602
H-Index - 7
ISSN - 2399-4908
DOI - 10.23889/ijpds.v5i4.1402
Subject(s) - context (archaeology) , government (linguistics) , covid-19 , computer science , resource (disambiguation) , population , risk analysis (engineering) , health care , general data protection regulation , service (business) , business , data science , operations research , knowledge management , computer security , data protection act 1998 , medicine , marketing , engineering , economics , paleontology , computer network , philosophy , linguistics , disease , environmental health , pathology , infectious disease (medical specialty) , biology , economic growth
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.