  Increasing automation in the healthcare sector calls for a Hybrid
Intelligence (HI) approach to closely study and design the collaboration of
humans and autonomous machines. Ensuring that medical HI systems'
decision-making is ethical is key. The use of Team Design Patterns (TDPs) can
advance this goal by describing successful and reusable configurations of
design problems in which decisions have a moral component, as well as through
facilitating communication in multidisciplinary teams designing HI systems. For
this research, TDPs were developed to describe a set of solutions for two
design problems in a medical HI system: (1) mitigating harmful biases in
machine learning algorithms and (2) sharing health and behavioral patient data
with healthcare professionals and system developers. The Socio-Cognitive
Engineering methodology was employed, integrating operational demands, human
factors knowledge, and a technological analysis into a set of TDPs. A survey
was created to assess the usability of the patterns on their understandability,
effectiveness, and generalizability. The results showed that TDPs are a useful
method to unambiguously describe solutions for diverse HI design problems with
a moral component on varying abstraction levels, that are usable by a
heterogeneous group of multidisciplinary researchers. Additionally, results
indicated that the SCE approach and the developed questionnaire are suitable
methods for creating and assessing TDPs. The study concludes with a set of
proposed improvements to TDPs, including their integration with Interaction
Design Patterns, the inclusion of several additional concepts, and a number of
methodological improvements. Finally, the thesis recommends directions for
future research.
