Health and medicine sociology

Social in/justice through data-driven healthcare technologies: empirical findings of early career researchers (session 1 of 2)

June 28, 2021 10:45
June 28, 2021 12:15

Laetitia Della Bianca, University of Geneva; Mélody Pralong, University of Geneva; Martina von Arx; University of Geneva


Karine Wendrich, Institute for Science in Society, Radboud University, Nijmegen

Ursula Meidert & Mandy Scheermesser, Zurich University of Applied Sciences, School of Health Professions

Shaul Duke – Ben-Gurion University

Solène Gouilhers, School of Health Sciences (HESAV), HES-SO, University of Applied Sciences and Arts Western Switzerland and The Institute of Gender Studies, University of Geneva

Mandy Scheermesser - Zurich University of Applied Sciences, School of Health Professions

Importance and have been pushed further by the SARS-CoV-2 pandemic in 2020. These digital tools are often promoted as inclusive and empowering for their users. Yet, while creating more social justice for some, they might produce other blind spots of social injustice for others. The present session invites early career scholars to present their empirical findings regarding the development and/or use of healthcare technologies (such as mobile health apps, telemedical tools, monitoring technologies, algorithmic treatments, etc.) within different matrices of power. Our aim is to gather understandings of the roles played by various actors within different networks of specific data-driven healthcare technologies. 

This paper session especially seeks to address the inclusionary and exclusionary effects of data-driven healthcare technologies. It questions to what extent such technologies blur traditional boundaries of healthcare and uncertainty in medical decision-making. Moreover, this session aims at disentangling whether related activities remain geographically and temporally bound. We, therefore, invite papers that address one or several of the following questions: 

  • How are algorithms interfering with the expertise of healthcare professionals and patients? Where and when does the so promoted anytime and anywhere healthcare take place? 
  • How do data-driven healthcare technologies reconfigure health inequalities? 
  • Are privacy concerns a privilege of the healthy? 
  • To which extent do algorithmic technologies allow for more precise and fair medical decisions? 
  • How do people adapt to or resist healthcare technologies? 
  • Which health-related issues do these technologies address and which are ignored?
  • Who has access to these technologies and the data they produce, and to whom is such access denied? 
  • Which places are configured as ‘well-suited’ for the use of such technologies, and which ones are excluded? 
  • How do these technologies reconfigure and redistribute the medical work within traditional and less traditional healthcare settings? 
  • What role do concepts such as justice and rights play in the design of data-driven healthcare technologies? 

We especially welcome contributions that draw on Science and Technology Studies, Feminist Technoscience Studies, Critical Data Studies, Sociology of Health and Medicine, and use an intersectional lens to question the role that gender, race, disability, migration, and class may play in such practices.

Tinkering with digital self-monitoring technologies: inquiry into Multiple Sclerosis patients’ experiences with and use of a smartphone application and activity tracker in their daily lives

Karine Wendrich, Institute for Science in Society, Radboud University, Nijmegen

Digital self-monitoring refers to the collection of personal health data through digital devices such as smartphones and wearable technologies. While there is critical discussion on how digital self-monitoring technologies shape, and at the same time are shaped by, existing norms, values and responsibilities in healthcare, thus far limited empirical research has been performed on how these technologies are actually enacted in patients’ daily lives. I address this knowledge gap in the context of the chronic neurological disease Multiple Sclerosis (MS), in order to gain a better understanding of of the different ways in which patients use digital self-monitoring in their daily lives.

My research departs from the notion that it is difficult to predict how a new technology will be used in actual practices. As previous research already showed, in the daily lives of patients technologies are often not being used or used differently than promised or envisioned by technology developers. In my PhD project I study the complex process of building a relationship between user and technology. For this I use Jeannette Pols’ concepts of “taming and unleashing of technologies” and “fitting of technologies in user practices”. The assumption behind these concepts is that users tinker with new technologies in order to embed technologies into their daily practices in a way that is useful and meaningful to them.

My empirical data are based on 26 interviews with MS patients who engaged in digital self-monitoring through a smartphone app and Fitbit activity tracker for a period of one year as part of a validation study by a Dutch start-up company and an academic hospital. Patients varied substantially in their enactment of the digital self-monitoring technologies in their daily lives. This was dependent on factors such as personality and coping with the MS. Whereas some patients actively engaged with the self-monitoring data and used these data to adapt their lifestyle or gain a better understanding of their disease, other patients only used the technologies because they were participating in a scientific study. Moreover, some patients described the self-monitoring as boring or annoying, whereas other patients did not experience any burden. Most patients are willing to continue with digital self-monitoring, but only when there is a clear clinical value and when the use of the technology can be adapted to personal needs and wishes, such as having the flexibility to change the date or frequency of self-monitoring assessments. I will also discuss to what extent actual use practices changed through time.

From the interviews it can be concluded that most patients found a way to integrate the digital self-monitoring into their daily life routines, but that patients differ in their experiences and use practices. Engagement in digital self-monitoring seems to be facilitated when patients perceive a clear added value of these technologies and when the technologies  are in line with how patients are coping with their MS. However, when digital self-monitoring is experienced as burdensome, this poses a barrier to patients’ willingness to use such technologies. Technology developers should not assume that patients have unlimited enthusiasm to engage in digital self-monitoring. Rather, they should be sensitive to patients’ needs and wishes, in order to reduce the burden of use and facilitate the useful and meaningful integration of digital self-monitoring technologies in patients’ daily lives.

Quantified Self-Technologies for better a disease management?

Ursula Meidert & Mandy Scheermesser, Zurich University of Applied Sciences, School of Health Professions

Background: Apps, fitness-trackers and other wearables have found broad acceptance in the general population. The interest in self-quantification and -optimization has strongly increased during the past few years and got an additional boost during the Corona Crisis. This trend is called «The Quantified-Self» and it is practiced not only for fun and out of curiosity, but for many it is out of an urge to improve one’s health. However, little is known about what the peoples’ experience is like, what implications it has for them (Belliger & Krieger, 2015; Lupton, 2013), how they deal with the measures taken and the threat of data leakage. This study explores these questions and adds also the perspective of health professionals and how they deal with the self-obtained data from their patients in their practice. 

Method: An extensive literature review was conducted to outline the current state and future developments of data-driven self-optimization. Furthermore, three moderated focus groups of about 90 minutes each with 6-8 persons were conducted: one group with healthy individuals, one with chronically ill individuals and one with health professionals. In addition, a meet-up of the Quantified Self group in Zurich, was attended. The interviews were recorded, transcribed, and content-analyzed. 

Results: The analysis shows that healthy individuals measure their body states and behavior mainly out of fun, interest of the body, curiosity and for documentation purposes. Improving their health is often only a secondary goal. People with a chronic illness in contrast measure mainly for disease management purposes. They strive to maintain their health and rebuild normality and independence alongside their health issue. In using electronic devices and self-obtained data, they are trying to maintain everyday life (Haslbeck et al., 2012; Mahrer Imhof et al., 2007). Corbin and Strauss (1985) refer to these disease management activities as "illness work". Quantified Self-technologies contribute to illness work, e.g. taking medication, measuring glucose or sleep. Individuals with chronic diseases attach great importance to data security and therefore tend to use conventional measuring devices or obtain all together from any device that has capacity to go online. For both groups, the measurement results have an impact on everyday life. Health professionals are currently very reluctant to make use of self-measured data from their patients and prefer to measure themselves. Furthermore, they are reluctant in recommending such devices to their patients. The analysis showed a gap between the users need for guidance in choosing a good product and make use of the data and the health professionals willingness to provide guidance and coaching.

Risk Unloading: A Look Into How Developers Address Risk in Healthcare-AI, Through the Case Study of Israeli Startups in the Field of Radiology

Shaul Duke – Ben-Gurion University

The development and implementation of data-driven healthcare technologies, such as the current implementation of AI tools in the medical field, always incurs the creation of risk. Two of the most affected stakeholders are patients, whose health may be at risk from these new tools (especially in the initial implementation process), and medical personnel, whose job and profession may be significantly affected by these tools’ deployment. It is thus of great interest to understand how the driving force behind the creation and implementation of these data-driven tools – i.e. developers – refer to this risk, frame their role in it, and generally communicate with the two most affected groups about it. 

While contemporary scholarship in science technology and society does deal with risk negotiation, it is usually focused on how governments – which are considered the most dominant player in these texts’ case studies – negotiate risk with different stakeholders. Yet there are many cases in which private entities, such as developers, are in fact the dominant player, and on this category of asymmetrical relations there is little existing writing. This is especially true of big data projects, which almost always involve strong private enterprises and venture capital interests. 

The proposed paper is based on a time-lapse content analysis of the online material of six Israeli AI startups in the field of radiology. This field, which is already very technology-oriented, is currently undergoing a formidable artificial intelligence transformation which is affecting its core operations, and indeed creating risk in the process. The use of machine learning in order to analyze masses of data for the purpose of medical diagnosis is riddled with errors and difficulties. The engine behind this AI transformation is neither patients nor healthcare professionals, but rather developers, who are almost always organized under startup settings, and who are the ones that stand to benefit the most financially from this transformation. The goal of this research is to examine these developers' stances towards the potential threat their automated tools pose to patient safety and to the work standing of healthcare professionals. These startups use the internet as one of their main ways to interface with their surroundings, which makes analyzing their online output fruitful. 

Results show that these developers do engage via their online content in debates about many of the risks that are created by this AI transformation, but tend to deny their own role in it, and dismiss or downplay the risks that their data-driven products may pose to both patients and healthcare workers. The end result, I observe, is that instead of risk negotiation which could lead to risk reduction, there is pseudo-negotiation which leads to ‘risk unloading’, the latter being a process in which risk – both in terms of responsibility and cost-bearing – is shifted from the primary player to secondary ones by manipulative means.

"A monitoring obstetrics ?": what a healthcare technology for distance surveillance of the fetus does to birth, bodies and knowledge.

Solène Gouilhers, School of Health Sciences (HESAV), HES-SO, University of Applied Sciences and Arts Western Switzerland and The Institute of Gender Studies, University of Geneva

My paper focuses on data collection during childbirth. The cardiotocograph (CTG) is the main device used by midwives and obstetricians to monitor the health of the fetus during childbirth. Using captors held on the parturient's belly, the CTG collects data on fetal heart rate and uterine contractions in the form of curves on both screen and paper. Health professionals are trained to interpret these curves to evaluate the degree of risk during birth, and to decide whether to intervene medically and how. In the maternity ward where I conducted my fieldwork, a remote-control system was introduced to transmit these curves on screens located outside the delivery room. Midwives and obstetricians could now interpret the data directly from their offices, at a distance from the fetal and maternal bodies. Drawing on an approach at the crossroads of Feminist Technoscience Studies and the Sociology of health and medicine, my paper proposes to examine the consequences of the introduction of this technology on birth, bodies and knowledge. 

In order to address these issues, I will draw on the ethnography that I conducted in the delivery room of a university hospital in the French-speaking part of Switzerland, as well as the 46 interviews carried out with midwives, obstetricians and mothers. The fieldwork that I conducted in birthing centers and birth at home, in which this technology is not present, will be used as a mirror case.  

I first show how the CTG's teletransmission reworks the expertise of the caregivers and the holds they rely on to make their decision. I examine controversies surrounding this device as well as the knowledge and the type of care it produces. Then, I describe how, after encountering resistance from midwives, this technology was quickly "naturalized" by contributing to a new "geography of responsibilities" (Akrich 2012) supported by all professionals. Finally, I explore how this device is both a hold and a let go for women who give birth. Having become standard practice in the delivery room, these monitoring devices contribute to reworking what is made visible and invisible. By producing new forms of knowledge and ignorance, the devices reconfigure the experience and bodies of caregivers, parturients and fetuses.

Acceptance work by non-humans at the development of new technologies: A reconstruction from the actor-network-theory

Mandy Scheermesser - Zurich University of Applied Sciences, School of Health Professions

The acceptability of technologies is one of the biggest challenges in the development of new technologies. Research in the field of social sciences offers various theoretical and methodological approaches to explain acceptance, acceptability and technology adoption. Different criteria of acceptance are used, e.g. utility, ease of use, aesthetics, contextual, individual and social differences. From the perspective of the actor network theory (ANT) (Callon 1984; Latour 2005) new technologies are the result of many interconnected and heterogeneous actors. They cannot be fully understood if they are considered as isolated technical artifacts (Callon 2006). 

This work will examine acceptance and acceptability of technologies as network formation and not, as in conventional technology acceptance models, as adoption by individual human actors. Using the concept of translation sociology (Callon 1984, 2006), the acceptance work necessary for network formation was examined. 

For this purpose, a case study on the actibelt® technology ( was conducted. The actibelt® is a body tracking technology that measures physical activity of patients over a period of several days using a belt with an integrated activity sensor. The aim of this work is to reconstruct the actibelt from the perspective of ANT, with a focus on the non-humans of the actibelt-actor-network. 

Drawing on qualitative interviews with users (patients, health professionals) and technology developers, and ethnographic observations, this study explored the question of how non-human actors contribute to the acceptability of technologies. 

As a result, the (technical) actibelt®-Actor-Network and five modes of acceptance work by non-human actors and their effects on patients were identified. The different modes of acceptance work show that non-human actors, such as events, meetings, graphs and socio-technical discourses, participating in the actibelt-actor-network. Non-humans are not passive actors in the development of technology, but can enable, hinder or condition acceptability. Therefore, non-human actors play a central and constitutive role in the translation process by performing acceptance work and contributing to the stabilisation and acceptability of the actibelt®-Actor-Network.