Speaker: Kevin
Wiggert, Department of Sociology of Technology and Innovation, Technical
University of Berlin.
Abstract:
Newly developed Clinical Decision Support
Systems (CDSSs), which are supposed to provide information or support
decisions, are increasingly systemically opaque and thus less comprehensible
for the physician. It becomes more and more unclear to the clinical user and to
the developers themselves what data sources and what information are the
fundaments of these technologies and how the technology is computing its
reasoning on this data. To better comprehend the reasoning of these
technologies for the actors engaged it is not only necessary to better
understand the inner processes of the technology but also to get more insights
into the assumptions the technology is built on. This, again, requires a better
understanding of the imagination and ideas about the ways of use which are
inscribed into the technology during its development. To exemplify this, I used
a case study of the development of a CDSS meant to support treatment decisions
in cardiology, a CDSS that is at least partly not based on expert knowledge, that
means knowledge from medical experts and/or clinical guidelines but uses
techniques of machine learning or simulation. It receives its way of reasoning
from recognizing patterns within the data this engine is operating with. This
can possibly lead to new findings and discoveries that are not based on
pre-existing knowledge and not represented by clinical guidelines but on
(potential) correlations between data points in a data corpus. In combining and
extending the theoretical concepts of situational scenarios in technology
development as well as the notion of scripts written into technology I am going
to show that the building of the CDSS-prototype and the negotiation about the
components of future situations of use co-evolved and led to specific scripts influencing
the envisioned user to use the technology in particular ways and receive
recommendations based on a so-called virtual patient, a data corpus
“representing” the real patient under treatment. In the case study it was
particularly the engineers’ perspective and less the clinicians’ one about the
application context that got implemented, which ended in less recognition of
the patients’ role in the consultation as well as an assumed passivity on the
part of the clinician as recipient of information delivered by the technology.
Speaker's Bio
I am a Doctoral Researcher at the Department of Sociology of Technology and
Innovation at the Technical University of Berlin. I have a special interest in
the development and application processes of artificially intelligent machines,
in particular within medical and health contexts. At present, I am part of a
research project that focusses on the social construction of human-robot
co-work by means of prototype work settings in the care and healthcare sector.
My doctor’s thesis is about trust in artificially intelligent machines in
health work contexts. Here, I am using the examples of assisting medical robots
and systems of decision support.