  Developing artificial intelligence (AI) tools for healthcare is a
collaborative effort, bringing data scientists, clinicians, patients and other
disciplines together. In this paper, we explore the collaborative data
practices of research consortia tasked with applying AI tools to understand and
manage multiple long-term conditions in the UK. Through an inductive thematic
analysis of 13 semi-structured interviews with participants of these consortia,
we aimed to understand how collaboration happens based on the tools used,
communication processes and settings, as well as the conditions and obstacles
for collaborative work. Our findings reveal the adaptation of tools that are
used for sharing knowledge and the tailoring of information based on the
audience, particularly those from a clinical or patient perspective.
Limitations on the ability to do this were also found to be imposed by the use
of electronic healthcare records and access to datasets. We identified meetings
as the key setting for facilitating exchanges between disciplines and allowing
for the blending and creation of knowledge. Finally, we bring to light the
conditions needed to facilitate collaboration and discuss how some of the
challenges may be navigated in future work.
