Course leader: Claus Bossen
Graduate school: Faculty of Arts
Course fee: 0.00 DKK
Status: Course is open for application
Semester: Spring 2023
Application deadline: 01/04/2023
Start date: 30/05/2023
Administrator: Henriette Jaquet
All students are placed on a waiting list until we reach application deadline.
Please note that the registration is binding unless you are prevented by illness.
This course investigates the role of data and in particular data work in healthcare
The last decades of digitization have led to the availability of a large amount of data that is easy to accumulate, process, and distribute. Data-driven healthcare has become a major topic with aims of delivering better services to patients and citizens, optimize use of resources including staff time, improve research and spur innovation. However, data is not something that lies out there waiting to be applied, but must be gathered, processed, and put into use, which requires a wide variety of tasks, skills and competences. As Bowker aptly phrased it: “Raw data is both an oxymoron and a bad idea” (Bowker 2005, p184). The questions that this course will investigate are: Who is doing data work in healthcare? What kinds of tasks and skills does this require? What are the gains of datafication in healthcare?
Already, a small number of studies focusing of data work in healthcare have emerged. They focus, for example, on the data work of patients, nurses, coders, medical scribes, and clinical staff in general and on the technology in use. These studies testify to the increasing efforts healthcare staff and patients must invest to deal with the deluge of data that is produced in the wake of electronic health records, healthcare apps and other IT systems (Ruckerstein & Schüll 2017). They also detail the variety of tasks and processes that are involved in data work in addition to and beyond the usual pipeline data process models and their steps such as, for example, the following: Define the problem, generate data, clean data, analyse data, interpret results and apply them. However, data work also implies other processes: Re-contextualizing, categorizing, standardizing, legitimizing, sense-making, etc. Thus, these emergent studies on data work show that a broad variety of tasks and skills that go beyond mathematics and statistical skills are involved when working with data. Yet, overall, these aspects of data work are relatively invisible within research as well as within management and policymaking.
This course will foreground the importance of data work within a profoundly digitized healthcare domain; who conducts this work; and how to study it.
The course will foreground data work as a way to make visible the efforts that are required to accomplish the goal of making healthcare data-driven.
It will introduce participants to a number of studies of data work and the conceptual and analytical approaches involved.
Tentative list of Literature:
Cerna, K., Grisot, M., Islind, A. S., Lindroth, T., Lundin, J., & Steineck, G. (2020). Changing Categorical Work in Healthcare: the Use of Patient-Generated Health Data in Cancer Rehabilitation. Computer Supported Cooperative Work (CSCW), 29(5), 563-586.
Fiske, A., Prainsack, B., & Buyx, A. (2019). Data work: meaning-making in the era of data-rich medicine. Journal of medical Internet research, 21(7), e11672.
Foster, J., McLeod, J., Nolin, J., & Greifeneder, E. (2018). Data work in context: Value, risks, and governance. Journal of the Association for Information Science and Technology, 69(12), 1414-1427.
Langstrup, H. (2019). Patient-reported data and the politics of meaningful data work. Health informatics journal, 25(3), 567-576.
McVey, L., Alvarado, N., Greenhalgh, J., Elshehaly, M., Gale, C. P., Lake, J., ... & Randell, R. (2021). Hidden labour: the skilful work of clinical audit data collection and its implications for secondary use of data via integrated health IT. BMC Health Services Research, 21(1), 1-11.
Pine, K. H., & Bossen, C. (2020). Good organizational reasons for better medical records: The data work of clinical documentation integrity specialists. Big Data & Society, 7(2), 2053951720965616.
Pine, K. H. (2019). The qualculative dimension of healthcare data interoperability. Health informatics journal, 25(3), 536-548.
Torenholt, R., Saltbæk, L., & Langstrup, H. (2020). Patient data work: filtering and sensing patient‐reported outcomes. Sociology of Health & Illness, 42(6), 1379-1393.
Wallenburg, I., Essén, A., & Bal, R. (2021). Caring for numbers: Performing healthcare practices through performance metrics in Sweden and the Netherlands. In Worlds of Rankings. Emerald Publishing Limited.
The course is relevant for PhD's whether early or late in their projects, ase well as for postdocs.
The course will make use of a combination of pre-course assignments and readings, and onsite lectures and group work.
Pernille Bertelsen, Department of Planning, Aalborg University
Claus Bossen, Digital Design and Information Studies, Aarhus University
Amelia Fiske, Institute for History and Ethics of Medicine, Technical University of Munich
Klaus Høyer, Department of Public Health, Copenhagen University
Campus Aarhus, room TBA
- 30 May 2023 11:00 - 17:00
- 31 May 2023 09:00 - 17:00
- 01 June 2023 09:00 - 17:00
- 02 June 2023 09:00 - 12:00