ANCHOR Validation Trial in High-Risk Multidisciplinary Care

This pre-registered, pragmatic, three-arm (1:1:1) patient-level randomized controlled trial with mixed-effects analysis at the encounter level tests two questions in real high-risk multidisciplinary clinical encounters at the Waymark clinically integrated network across three U.S. states (Ohio, Washington, Virginia): (1) does adding ANCHOR - a clinical AI structural verification layer - to a Gemini 3.1 Pro-assisted supervising-physician workflow reduce the rate of clinically meaningful safety failures, compared with the same Gemini 3.1 Pro-assisted workflow without ANCHOR? (2) does the Gemini 3.1 Pro-assisted workflow itself reduce the same safety endpoint compared with unassisted standard care in which the supervising physician writes their own SOAP assessment/plan from a blank template? ANCHOR is a single-call structural verification layer combining a Logical Neural Network (Riegel et al. 2020) certificate, six specialist agents, and concept-decomposed output with PMID citation provenance. ANCHOR is physician-facing only and is used by supervising physicians, not by the multidisciplinary clinical team they oversee. The trial randomizes 240 patients 1:1:1 across the Waymark clinically integrated network over a 12-week active-enrolment window (80 per arm). Eligible patients are adults (age 18+) identified as high-risk by combined claims-based and clinical criteria. Eligible encounters span three integrated Waymark service modalities: high-risk primary care, specialty care coordination, and real-time telemedicine urgent care. The primary endpoint is a per-encounter binary composite: any of (a) failure to mention a do-not-miss diagnosis, (b) under-triage, (c) contraindicated medication recommendation, (d) failure to recommend escalation when clinically warranted; adjudicated by a blinded panel of 3 board-certified physicians with majority-of-three scoring. The primary contrast is Arm 3 (LLM+ANCHOR) versus Arm 2 (LLM with safety prompt), isolating ANCHOR's marginal c