title: Artificial intelligence and health equity in primary care: A sociotechnical analysis and stakeholder exploration on how to make artificial intelligence a force for health equity in English primary care.
Creators: name: d'Elia, Alexander
Publisher: name: University of Liverpool Repository
Year: 2023

description: Background
Artificial Intelligence (AI)-augmented interventions are currently being rolled out across primary care, but the sociotechnical theory for deploying AI is in its infancy. The current literature focuses predominantly on reducing health inequity by minimising algorithmic bias. Applying AI in healthcare will affect however health inequity beyond algorithmic bias, through interactions with existing societal health inequities. There is a need to understand how the ecosystem in which AI is being implemented can be made to benefit HE through AI.
Aims
To map the ecosystem involved in the implementation of AI in English primary care, and from a sociotechnical perspective assess how this network of actors can be conducive to improving health equity.

Methods
A systematic scoping review was conducted followed by an ethnographically anchored inquiry based on 32 interviews with stakeholders including commissioners, decision-makers, AI developers, researchers, GPs and patient groups. This was complemented by an analysis of UK primary care data to assess the risk of algorithmic bias in big-data applications such as AI systems.

Results
1. AI is likely to impact health inequity in primary care through a multitude of mechanisms, including both those intrinsic to the AI systems (e.g. algorithmic bias) and wider system- and societal impact (e.g. digital exclusion and enabling privatisation and commercialisation of care provision).
2. Regulation and policy cannot guarantee equitable implementation of AI but need to provide a baseline framework to enable other stakeholders to promote equity in the implementation process: a shared understanding of the causal mechanisms of AI and health inequity, how to measure HE, and how to share necessary data.
3. All stakeholders need to be on board for implementation success concerning the above. Currently, innovation typically leaves clinicians and patients behind.
4. Capacity building is needed to enable the addressing of the above, in particular on the commissioning and clinician level.
5. Whilst true for most innovations, the difference with AI is the pace of innovation. Previous waves of innovations have happened at a more gradual pace, allowing for a more controlled implementation.

Conclusions
AI in primary care holds great potential, however, if the current implementation is to benefit the health of everyone, careful consideration is needed on the sociotechnical context in which the process is taking place.
This project is not the first to cover the effects of AI on health inequity, and the rapid development of AI and related research meant that considerable scholarship has been produced during this project. However, this thesis carves out a niche against the preceding research. Namely, in contrast to preceding works, this thesis takes a systematic, empirical approach specifically focused on the implementation setting that is English NHS primary care, and as such produces an empirically grounded set of recommendations for how AI can be implemented equitably. It also sets a methodological precedent on how complex interventions can be assessed “prospectively” from a sociotechnical perspective.