Rethinking Health Agents: From Siloed AI to Collaborative Decision Mediators
This addresses the challenge of fragmented care and misalignment among healthcare stakeholders, offering a conceptual shift from siloed AI to collaborative mediators, though it is incremental as it builds on existing AI and healthcare collaboration concepts.
The paper tackles the problem of AI systems in healthcare operating in silos, which fragments understanding and misaligns stakeholders like patients and clinicians, by reframing AI as a collaborator embedded in multi-party interactions; through a pediatric chronic kidney disease case study, it shows that siloed AI fails to address collaboration gaps and proposes a framework for AI to surface context and scaffold shared understanding.
Large language model based health agents are increasingly used by health consumers and clinicians to interpret health information and guide health decisions. However, most AI systems in healthcare operate in siloed configurations, supporting individual users rather than the multi-stakeholder relationships central to healthcare. Such use can fragment understanding and exacerbate misalignment among patients, caregivers, and clinicians. We reframe AI not as a standalone assistant, but as a collaborator embedded within multi-party care interactions. Through a clinically validated fictional pediatric chronic kidney disease case study, we show that breakdowns in adherence stem from fragmented situational awareness and misaligned goals, and that siloed use of general-purpose AI tools does little to address these collaboration gaps. We propose a conceptual framework for designing AI collaborators that surface contextual information, reconcile mental models, and scaffold shared understanding while preserving human decision authority.