Investigating Collaborative Data Practices: a Case Study on Artificial Intelligence for Healthcare Research
This addresses practical collaboration challenges for AI healthcare researchers, but is incremental as it documents existing practices rather than proposing new solutions.
The study investigated collaborative data practices in UK research consortia applying AI tools to manage multiple long-term conditions, finding that meetings facilitate interdisciplinary exchanges and tools are adapted for knowledge sharing, though electronic health records and dataset access impose limitations.
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.