Conversational Capacity in Triadic Telemedicine: A Conceptual Framework and Evidence Synthesis
Alexander Gamus, Gabriel Chodick, Yoram Blachar, Tamar ShalomBackground:
Telemedicine is conventionally modeled as a dyadic clinician–patient encounter, yet a third party—caregiver, community health worker, nurse, or increasingly an artificial intelligence (AI) conversational agent—frequently participates. No principled basis exists for determining when such a third party is a genuine facilitator versus an instrument of one party, rendering cross-study comparison incommensurable and deployment decisions poorly grounded.
Methods:
We conducted a narrative synthesis of literature spanning triadic clinical communication, shared decision-making, and AI-mediated interaction to derive a technology-neutral conceptual framework and classification model.
Results:
We propose conversational capacity, operationalized through four functions (interpret, translate, advocate, adapt), as the minimum criterion for triadic facilitation. The framework defines a structural boundary separating genuine triadic architectures (Modes A and B) from augmented dyadic models (Mode C); a five-level Conversational Capacity Spectrum classifying human and AI facilitators; and a Clinical Situation Matrix mapping architecture to morbidity complexity, patient vulnerability, and decision complexity. Advocacy emerges as the discriminating function AI is least able to perform, making it the current limiting dimension of AI facilitation. The framework generates three testable hypotheses and identifies an equity paradox whereby high-vulnerability populations most in need of human facilitation are those most likely to be assigned AI on resource grounds.
Conclusions:
Triadic telemedicine should be defined by conversational capacity rather than mere third-party presence. Replacing the binary triadic-versus-dyadic distinction with two gradable, measurable constructs is a prerequisite for cumulative research and responsible AI deployment in clinical consultations.