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Paper short abstract

Scaling healthcare data for AI raises issues of quality, bias, and privacy. Wearables add skewed or missing data. Synthetic data offers solutions but raises issues around trust, ownership, and regulation. The paper explores the political-economy of using synthetic datasets for AI in healthcare.

Paper long abstract

Scaling healthcare data for AI opens several problems. Varying quality of data inputs and abstraction from contextual variables – primarily, the social determinants of health – make it harder to ensure data quality. It is more likely that datasets contain bias and exclusions, and that it is harder to ameliorate their effects. Health apps and wearables provide ever more healthcare data points on which models can be trained but also introduce the likelihood of missed data points due to faulty signals as well as skewed data. In addition, with increasingly detailed intersecting data points it becomes harder to ensure the privacy of individuals.

Synthetic data has been presented as a solution to these concerns. Firms specialised in synthetic data build on long-standing data hygiene practices (e.g. filling in missing data points, bias control, etc) but also offer fully simulated data sets that seek to emulate real data sets but with sufficient alteration to be distinct. Currently there is global disagreement over whether to trust synthetic data in medical certification, with the EU opposed but US regulators much more supportive even prior to the current administration. Whilst in many jurisdictions a patient’s health data is their property, meaning access and usage requires their consent, modifying that data to be ‘synthetic’ complicates who owns it. The paper maps these political-economic aspects, outlining how synthetic data impacts how we think of healthcare as well as its broader political consequences for regulation, states, as well as individuals.

Keywords: Synthetic data, Healthcare, AI, Political-Economy, Regulation

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Paper short abstract

AI and other digital technologies have entered the healthcare field as a promise of efficiency. This study examines the gap between that narrative and its lived reality: the glitches, repairs, and improvisations through which healthcare workers keep the script intact (or not).

Paper long abstract

Digital technologies are increasingly introduced in the healthcare field through promissory discourses that present efficiency as an unambiguous good. However, efficiency is not just a technical outcome: it is a narrative that needs to be continuously maintained and defended. This study examines the gap between efficiency as it is sold and efficiency as it is lived: the frictions, glitches, and improvisations that characterise its implementation in everyday (health)care settings, and the labour both workers and institutions invest in trying to uphold this narrative.

Building on scholarship on sphere transgressions (Sharon, 2021; Sharon & Gellert, 2024; Walzer, 1983) and historical analyses of efficiency and automation (Alexander, 2008, 2009), this research argues that corporate-driven efficiency enters healthcare not as a stable value, but as a script that is constantly under negotiation. When digital systems produce unexpected outputs, break down, or fail to fit their own aims, workers are left to absorb that gap: repairing, reframing, and improvising in ways that sustain the appearance of smooth, efficient operations. These everyday practices of maintenance are not incidental to the implementation of digital technologies, but a constitutive part of it.

Engaging with empirical cases from the literature on digitally supported homecare, medical chatbots, and digital mental health technologies, this study aims at showing how efficiency-as-narrative and efficiency-as-practice diverge, and how that divergence is managed. This research investigates how a value originating in industrial and market spheres crosses into healthcare, reshaping what counts as legitimate care practice, while bypassing the critical assessment that sphere transgressions demand.

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Paper short abstract

Drawing on the studies of promissory futures around digitalization and my ongoing ethnography at the Dutch outpatient hospital, I explore how healthcare professionals and hospital decision-makers navigate the tensions between promised datafied futures and the under-repair present.

Paper long abstract

STS research on promissory futures around digitalization and AI have demonstrated their performative power in structuring action and setting agendas in the present (Oomen, 2022). While these techno-optimistic visions of desirable futures often rely on narratives of technological breakthroughs and immediacy, research shows that the transition to data-driven healthcare is often accompanied by delays and postponement (Choroszewicz, 2024; Hoeyer, 2023), and invisible work (Green et al., 2023). Drawing on this literature and my ongoing ethnographic study at the Dutch outpatient hospital, I explore how healthcare professionals and hospital decision-makers navigate the tensions between promised futures and the under-repair present.

This paradox manifested in the futuristic narrative around AI technologies to improve healthcare efficiency. The newly introduced “chat with your doctor” hospital app feature caused a lot of concerns among professionals, who felt overwhelmed by patient messages. Despite difficulties in the present, some physicians hoped that more advanced tools, like personalized AI-powered assistants, would resolve the problem in the future. Similar hopes were expressed by the chief pharmacist, who was involved in developing an LLM for medicine verification to address the shortages of pharmacists at the hospital. Despite acknowledging the hurdles of model development and usability concerns, she still believed that technological advances in the near future will solve the current issues. In my presentation, I will draw on these and related examples to shed light on how hopes for datafied healthcare futures co-exist with emerging tensions in the present.