Torquing patients into data: enactments of care about, for and through medical data in algorithmic systems

ABSTRACT

The increasing digitisation of healthcare services has transformed healthcare provision into a data-centric enterprise. Thinking with Joan Tronto and her notion of care, we study medical data practices in the context of a health-tech company developing an algorithmically driven platform to match patients and their physicians with clinical trials. What does it mean to pose the patient in the centre in such a context? In this paper, we show how the enactments of patient-centrism translate to multidimensional enactments of data care for a diversity of domain experts handling medical data, informed by the values and backgrounds of each ‘data handler’ situated within the concerns of their domain expertise. Where data experts engage solely with the patients’ data to facilitate data creation for the platform’s algorithmic system, the quest for data quality depends on the preceding practices of care and affective labour about and for the patients. We show how patients get help to torque their medical records and histories into data to fit the demands of the system to ensure access to experimental treatments and clinical trials. We demonstrate how patient-centrism manifests as care for data quality, shaped throughout by differentiated concerns for regulatory compliance. Finally, we argue that regulatory compliance constitutes a care practice across data work that is diversified in its enactments by the experts’ domain concerns and backgrounds.

KEYWORDS:

• Data care
• medical data
• domain expertise
• care about compliance

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.biopharmadive.com/spons/revolutionizing-clinical-trials-unlocking-peak-efficiency-with-total-labor/701091/

2 Achieving quality data for AI in healthcare and why it is important (Davies & Kameron, 2023). Pinsent Masons. Retrieved August 31, 2023, from https://www.pinsentmasons.com/out-law/analysis/achieving-quality-data-ai-healthcare.

Additional information

Funding

This work was supported by HORIZON EUROPE Marie Sklodowska-Curie Actions [Grant Number: 955990].

Notes on contributors

Natalia-Rozalia Avlona

Natalia-Rozalia Avlona is a lawyer (LLM), and Marie Curie Ph.D. Fellow (DCODE) at the Computer Science Department of the University of Copenhagen. Her research focuses on the creation and implementation of medical datasets in the AI-driven Health Care Sector. She is employing ethnographic methods to investigate these processes as socio-technical assemblages of human expertise and infrastructural capacities conditioned by the obligation for regulatory compliance. Ηer aim is to translate the experts’ nitty-gritty practices of data creation and implementation in the health-tech and healthcare sector, to the ways policymakers perceive, and hence regulate these systems. Natalia specialises in the intersection of emerging technologies, law, and society. She has worked for over a decade at the forefront of open and emerging technologies, focusing on their legal and ethical implications, with a particular commitment to intersectional feminist agenda [email: [email protected]].

Irina Shklovski

Irina Shklovski is a Professor of Communication and Computing in the Department of Computer Science and the Department of Communication at the University of Copenhagen. She also holds a WASP-HS visiting professorship at Linköping University. Her main research areas include speculative AI futures, responsible and ethical technology design, online data leakage, information privacy, creepy technologies and the sense of powerlessness people experience in the face of massive personal data collection.