1. Quantified data in healthcare: transformation of nursing work practice Katerina Cerna, Department of Education, University of Gothenburg, Sweden
Project EfterCancern (AfterCancer)
Quantified data are collected through self-tracking applications or smart devices. For example, a self-tracking application that a user can use to measure how many times they urinated, produces quantified data. When designing tools collecting quantified data, it is important to consider the trade-offs that the design participants might face. More specifically, this study presents what consequences nurses had to face when they became co-designers of a self-tracking app. Learning and making data meaningful are highlighted as issues that need to be considered when designing self-tracking tools.
Relationship of my research to the symposium
The starting point of my research is a simple assumption and its more than complicated implications: that the more data we will collect about ourselves, the “better, stronger, faster” we can be. In the context of healthcare, the increased amount of quantified data about patients is often expected to result into more effective treatment and improved quality of life. However, my preliminary results show that collecting large amounts of data as well as the changes it brings to the professionals working with the data do not necessarily mean a better life (for the patients) or higher efficiency (for the professionals).
Case
This study focuses on nurses who became meta-designers in a development of a self-tracking tool and what implications their participation have for their work. This study draws on a design ethnographic engagement conducted for more than two years in a small cancer rehabilitation center. Here, the nurses provide support and advice to patients who survived cancer but are now coping with consequences of the cancer treatment. The center is part of an interdisciplinary project called AfterCancer (EfterCancern) that focuses on improvement of quality of life of cancer surviving patients. The self-tracking app was being developed to help document patients’ symptoms, such as frequency of urination or defecation, pain experience, or medication intake. Nurses, developers, researchers, and patients participated in designing of the tool.
Contribution of my study
Empirical case, specifically in relation to consequences of participating in design process and usage of quantified data in new areas
Starting points for discussion about strategies how to manage the trade-offs in design:
What is it actually that the participants learn in the design process?
How can we support this learning emerging from the participations so the professionals can make data meaningful?
How to support professionals so they can interpret quantified data?
And what about those who did not have a chance to become part of a design process - can we teach them the same competence? And in that case how?
And what about those who do not want to participate and constantly learn more - are they doomed?
Results: Learning in the design process
Positive consequences
Negative consequences
Learned about the designed application and how data will be collected
The design sessions went outside of their own expertise
Formalization of tacit routines
Temporary changes in work-tasks priorities
 Opportunity to reflect about own and their colleagues’ working strategies and routines and
Various stressful situations based on stretching of already limited resources
Future work and challenges
How can we create tools that would support the learning also at work and not only during design?
How to create systems that support collaborative making sense and not only collaborative designing?
How much of the designing knowledge will the other design participants need to gain?
Session 8: Grand challenges