Abstract
Population health management is an increasingly popular concept in health systems. Driven by growth in ‘big data’, the approach uses aggregated data to identify and manage the health of those deemed ‘at risk’. We use the UK’s response to the COVID-19 pandemic as a lens to critically examine this approach to improving population health, using the policy of ‘shielding’ those at high risk of harm as an exemplar. Firstly, we explore the policy as an example of categorisation, showing that criteria used to identify high-risk population ‘segments’ are never wholly objective, arising out of negotiations taking account of economic, political, or social issues, with the categorization of individuals as in/out of the high-risk group always approximate. Secondly, we consider the construction of risk and demonstrate that focusing on biomedical data brackets out societal factors driving individual risks including deprivation, racial discrimination, and employment status. Finally, we highlight how this framing of COVID-19-associated risk as a biomedical issue leads to a neoliberal focus upon individual rather than societal risk-mitigation approaches. We argue that population health management should be married with traditional public health advocacy and campaigning, digging beneath identified disparities to expose and interrogate the structural factors at work.
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Checkland, K., Hammond, J., Spooner, S. (2021). Population Health Management in the NHS: What Can We Learn from COVID-19?. In: Waring, J., Denis, JL., Reff Pedersen, A., Tenbensel, T. (eds) Organising Care in a Time of Covid-19. Organizational Behaviour in Healthcare. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-82696-3_11
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