329
Views
0
CrossRef citations to date
0
Altmetric
Articles

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

&
Pages 735-757 | Received 09 Aug 2023, Accepted 02 Feb 2024, Published online: 07 Mar 2024

References

  • Ammitzbøll Flügge, A., Hildebrandt, T., & Møller, N. H. (2021). Street-level algorithms and AI in bureaucratic decision-making: A caseworker perspective. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–23. https://doi.org/10.1145/3449114
  • Baker, K. S., & Karasti, H. (2018). Data care and its politics: Designing for local collective data management as a neglected thing. In Proceedings of PDC (pp. 1–12).
  • Balkin, J. M. (2015). Information fiduciaries and the first amendment. UCDL Review, 49, 1183.
  • Balkin, J. M., & Zittrain, J. (2016). A grand bargain to make tech companies trustworthy. The Atlantic, 3.
  • Beaton, B., Acker, A., Di Monte, L., Setlur, S., Sutherland, T., & Tracy, S. E. (2017). Debating data science: A roundtable. Radical History Review, 2017(127), 133–148. https://doi.org/10.1215/01636545-3690918
  • Bossen, C. (2020). Data work and digitization: The impact of computerized systems and automation on healthcare professionals. XRDS: Crossroads, The ACM Magazine for Students, 26(3), 22–25. https://doi.org/10.1145/3383370
  • Bossen, C., & Bertelsen, P. S. (2023). Digital health care and data work: Who are the data professionals? Health Information Management Journal, 18333583231183083.
  • Bossen, C., Pine, K. H., Cabitza, F., Ellingsen, G., & Piras, E. M. (2019). Data work in healthcare: An introduction. Health Informatics Journal, 25(3), 465–474. https://doi.org/10.1177/1460458219864730
  • Bowker, G. C., & Star, S. L. (2000). Sorting things out: Classification and its consequences. MIT Press.
  • Chaari, T., Chaabane, S., Aissani, N., & Trentesaux, D. (2014, May). Scheduling under uncertainty: Survey and research directions. 2014 International Conference on Advanced Logistics and Transport (ICALT) (pp. 267–272). IEEE.
  • Clarke, A. (2005). Situational analysis: Grounded theory after the postmodern turn. Sage.
  • Cook, J., & Trundle, C. (2020). Unsettled care: Temporality, subjectivity, and the uneasy ethics of care. Anthropology and Humanism, 45(2), 178–183. https://doi.org/10.1111/anhu.12308
  • Crawford, K. (2021). The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
  • Dalla Costa, M., & James, J. (1973). The power of women and the subversion of the community. Falling Wall Press.
  • Daston, L., & Galison, P. L. (1992). The image of objectivity. Representations, 40, 81–128. https://doi.org/10.2307/2928741
  • Davies, C. W., & Kameron, S. (2023, May 22). Achieving quality data for AI in healthcare and why it is important. Out-Law. Pinsent Masons. Retrieved August 31, 2023, from https://www.pinsentmasons.com/out-law/analysis/achieving-quality-data-ai-healthcare.
  • Delkeskamp-Hayes, C. (2022). From physicians’ professional ethos towards medical ethics and bioethics. Springer International Publishing.
  • Federici, S. (1975). Wages against housework. Falling Wall Press.
  • Feinberg, M. (2017, May). A design perspective on data. In Proceedings of the 2017 CHI (pp. 2952–2963).
  • Felzmann, H. (2012). Adherence, compliance, and concordance: An ethical perspective. Nurse Prescribing, 10(8), 406–411. https://doi.org/10.12968/npre.2012.10.8.406
  • Fernández-Gutiérrez, M., Bas-Sarmiento, P., Albar-Marín, M. J., Paloma-Castro, O., & Romero-Sánchez, J. M. (2018). Health literacy interventions for immigrant populations: A systematic review. International Nursing Review, 65(1), 54–64. https://doi.org/10.1111/inr.12373
  • Fisher, J. A. (2008). Medical research for hire: The political economy of pharmaceutical clinical trials. Rutgers University Press.
  • Fotopoulou, A. (2019). Understanding citizen data practices from a feminist perspective: Embodiment and the ethics of care. In H. Stephansen & E. Treré (Eds.), Citizen media and practice (pp. 227–242). Routledge.
  • Fox, C., Levitin, A., & Redman, T. (1994). The notion of data and its quality dimensions. Information Processing & Management, 30(1), 9–19.
  • Gitelman, L. (ed.). (2013). Raw data is an oxymoron. MIT Press.
  • Kallinikos, J., & Tempini, N. (2014). Patient data as medical facts: Social media practices as a foundation for medical knowledge creation. Information Systems Research, 25(4), 817–833. https://doi.org/10.1287/isre.2014.0544
  • Kaziunas, E., Ackerman, M. S., Lindtner, S., & Lee, J. M. (2017, February). Caring through data: Attending to the social and emotional experiences of health datafication. In Proceedings of the 2017 ACM CSCW (pp. 2260–2272).
  • Keyes, O. (2019, December 7). The Body Instrumental. Logic(s) Magazine. Issue 9. Nature. Retrieved July 28, 2023, from https://logicmag.io/nature/the-body-instrumental/.
  • Khang, A., Rana, G., Tailor, R. K., & Hajimahmud, V. A. (2023). Data-centric AI solutions and emerging technologies in the healthcare ecosystem. CRC Press, 10(978100335618), 9.
  • Lampland, M., & Star, S. L. (Eds.). (2009). Standards and their stories: How quantifying, classifying, and formalizing practices shape everyday life. Cornell University Press.
  • Lee, C. P., Dourish, P., & Mark, G. (2006, November). The human infrastructure of cyberinfrastructure. In Proceedings of the 2006 20th anniversary conference on computer supported cooperative work (pp. 483–492). https://doi.org/10.1145/1180875.1180950
  • Light, A., & Seravalli, A. (2019). The breakdown of the municipality as caring platform: Lessons for co-design and co-learning in the age of platform capitalism. CoDesign, 15(3), 192–211. https://doi.org/10.1080/15710882.2019.1631354
  • Martin, A., Myers, N., & Viseu, A. (2015). The politics of care in technoscience. Social Studies of Science, 45(5), 625–641. https://doi.org/10.1177/0306312715602073
  • Mol, A. (2008). The logic of care: Health and the problem of patient choice. Routledge.
  • Mol, A., Moser, I., & Pols, J. (2010). Care in practice: On tinkering in clinics, homes and farms. transcript Verlag.
  • Møller, N. H., Bossen, C., Pine, K. H., Nielsen, T. R., & Neff, G. (2020). Who does the work of data? Interactions, 27(3), 52–55. https://doi.org/10.1145/3386389
  • Muller, M., Lange, I., Wang, D., Piorkowski, D., Tsay, J., Liao, Q. V., Dugan, C., & Erickson, T. (2019, May). How data science workers work with data: Discovery, capture, curation, design, creation. In Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1–15).
  • Murphy, M. (2015). Unsettling care: Troubling transnational itineraries of care in feminist health practices. Social Studies of Science, 45(5), 717–737. https://doi.org/10.1177/0306312715589136
  • Nielsen, T. R., Menendez-Blanco, M., & Møller, N. H. (2023). Who cares about data? Ambivalence, translation, and attentiveness in asylum casework. Computer Supported Cooperative Work, https://doi.org/10.1007/s10606-023-09474-7
  • Pine, K. H., Bossen, C., Chen, Y., Ellingsen, G., Grisot, M., Mazmanian, M., & Møller, N. H. (2018, October). Data work in healthcare: Challenges for patients, clinicians and administrators. In Companion of the 2018 ACM CSCW (pp. 433–439). https://doi.org/10.1145/3272973.3273017
  • Pinel, C., Prainsack, B., & McKevitt, C. (2020). Caring for data: Value creation in a data- intensive research laboratory. Social Studies of Science, 50(2), 175–197. https://doi.org/10.1177/0306312720906567
  • Pipino, L. L., Lee, Y. W., & Wang, R. Y. (2002). Data quality assessment. Communications of the ACM, 45(4), 211–218. https://doi.org/10.1145/505248.506010
  • Prainsack, B. (2017). Personalized medicine. In Personalized medicine. New York University Press.
  • Purtova, N. (2018). The law of everything. Broad concept of personal data and future of EU data protection law. Law, Innovation and Technology, 10(1), 40–81. https://doi.org/10.1080/17579961.2018.1452176
  • Ribes, D. (2019). How I learned what a domain was. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–12. https://doi.org/10.1145/3359140
  • Ribes, D., Hoffman, A. S., Slota, S. C., & Bowker, G. C. (2019). The logic of domains. Social Studies of Science, 49(3), 281–309. https://doi.org/10.1177/0306312719849709
  • Sambasivan, N., & Veeraraghavan, R. (2022). The deskilling of domain expertise in AI development. In Proceedings of the 2022 CHI (pp. 1–14).
  • Schank, R. C., & Jona, M. Y. (1994). Issues for psychology, AI, and education: A review of Newell’s unified theories of cognition. In D. G. Bobrow (Ed.), Artificial intelligence in perspective (pp. 375–388). The MIT Press.
  • Schermer, M. (2002). The different faces of autonomy: Patient autonomy in ethical theory and hospital practice (Vol. 13). Springer Science & Business Media.
  • Shklovski, I., & Némethy, C. (2023). Nodes of certainty and spaces for doubt in AI ethics for engineers. Information, Communication & Society, 26(1), 37–53. https://doi.org/10.1080/1369118X.2021.2014547
  • Sirur, S., Nurse, J. R., & Webb, H. (2018, January). Are we there yet? Understanding the challenges faced in complying with the General Data Protection Regulation (GDPR). In Proceedings of the 2nd International Workshop on Multimedia Privacy and Security (pp. 88–95).
  • Star, S. L. (1989). Regions of the mind: Brain research and the quest for scientific certainty. Stanford University Press.
  • Stark, L., & Hoffmann, A. L. (2019). Data is the new what? Popular metaphors & professional ethics in emerging data culture.
  • Subramonyam, H., Im, J., Seifert, C., & Adar, E. (2022). Solving separation-of-concerns problems in collaborative design of human-AI systems through leaky abstractions. In Proceedings of the 2022 CHI (pp. 1–21).
  • Sunarti, S., Rahman, F. F., Naufal, M., Risky, M., Febriyanto, K., & Masnina, R. (2021). Artificial intelligence in healthcare: Opportunities and risk for future. Gaceta Sanitaria, 35, S67–S70. https://doi.org/10.1016/j.gaceta.2020.12.019
  • Tronto, J. (1993). Moral boundaries: A political argument for an ethic of care. Routledge.
  • Zając, H. D., Avlona, N. R., Andersen, T. O., Kensing, F., & Shklovski, I. (2023). Ground truth or dare: Factors affecting the creation of medical datasets for training AI. In AAAI/ACM Conference on AI, Ethics, and Society (AIES ‘23), Augus t 08–10, 2023, Montréal, QC, Canada. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3600211.3604766