Implementing AI in Healthcare: Bridging the Gap Between Technical, Cognitive, and Sociotechnical Perspectives

There's a notable gap in sociotechnical research focusing on the clinician's experience with AI systems, particularly regarding the integration of data visualizations to support their decision-making. Many sociotechnical studies of AI-based systems have focused on patient end users, but there is a need for additional clinician studies for CDS users that evaluate information synthesis needs, effective incorporating data summarization and visualizations, and assessments of the changes in the data and clinical environment that the CDS itself produces beyond the targeted action. Effective interventions that support better decision-making can be bolstered with design and evaluation that incorporates cognitive theories.as well as social theories that recognize healthcare data as one part of a complex organizational system. A commonly used One example from cognitive psychology is dual-process theory which categorizes thinking processes – that which are intuitive and fast (system 1) or deliberate and slow (system 2). Dual-process theory does not map well to neurological processes, for that, approaches related to cognitive control may be better. Social science theories may also have an important role to play in helping us to conceptualize modern healthcare environments as complex dynamic systems that are actively shaped by tool users, tool designers, and tools themselves. In addition to these complexities, it is critical that AI-assisted CDS also be considered, assessed, and continuously evaluated for fairness and equity within clinically and culturally relevant sub-groups that may have differential care goals and needs.