An LLM chatbot to facilitate primary-to-specialist care transitions: a randomized controlled trial
Summary
Patient-facing large language models (LLMs) hold potential to streamline inefficient transitions from primary to specialist care. We developed the preassessment (PreA), an LLM chatbot co-designed with local stakeholders, to perform the general medical consultations for history-taking, preliminary diagnoses, and test ordering that would normally be performed by primary care providers and to generate referral reports for specialists. PreA was tested in a randomized controlled trial involving
Content
# An LLM chatbot to facilitate primary-to-specialist care transitions: a randomized controlled trial
*Published: 2026 Mar*
Patient-facing large language models (LLMs) hold potential to streamline
inefficient transitions from primary to specialist care. We developed the
preassessment (PreA), an LLM chatbot co-designed with local stakeholders, to
perform the general medical consultations for history-taking, preliminary
diagnoses, and test ordering that would normally be performed by primary care
providers and to generate referral reports for specialists. PreA was tested in a
randomized controlled trial involving 111 specialists from 24 medical
disciplines across two health centers, where 2,069 patients (1,141 women; 928
men) were randomly assigned to use PreA independently (PreA-only), use it with
staff support (PreA-human), or not use it (No-PreA) before specialist
consultation. The trial met its primary end points with the PreA-only group
showing significantly reduced physician consultation duration (28.7% reduction;
3.14 ± 2.25 min) compared to the No-PreA group (4.41 ± 2.77 min; P < 0.001),
alongside significant improvements in physician-perceived care coordination
(mean scores 113.1% increase; 3.69 ± 0.90 versus 1.73 ± 0.95; P < 0.001) and
patient-reported communication ease (mean scores 16.0% increase; 3.99 ± 0.62
versus 3.44 ± 0.97; P < 0.001). Equivalent outcomes between the PreA-only and
PreA-human groups confirmed the autonomous operation capability. Co-designed
PreA outperformed the same model with additional fine-tuning on local dialogues
across clinical decision-making domains. Co-design with local stakeholders,
compared to passive local data collecting, represents a more effective strategy
for deploying LLMs to strengthen health systems and enhance patient-centered
care in resource-limited settings. Chinese Clinical Trial Registry identifier:
ChiCTR2400094159 .
DOI: 10.1038/s41591-025-04176-7